Reviewerly/exHarmony huggingface dataset:
LINet: A Location and Intention-Aware Neural Network for Hotel Group Recommendation

Motivated by the collaboration with Fliggy1, a leading Online Travel Platform (OTP), we investigate an important but less explored research topic about optimizing the quality of hotel supply, namely selecting potential profitable hotels in advance to build up adequate room inventory. We formulate a WWW problem, i.e., within a specific time period (When) and potential travel area (Where), which hotels should be recommended to a certain group of users with similar travel intentions (Why). We identify three critical challenges in solving the WWW problem: user groups generation, travel data sparsity and utilization of hotel recommendation information (e.g., period, location and intention). To this end, we propose LINet, a Location and Intention-aware neural Network for hotel group recommendation. Specifically, LINet first identifies user travel intentions for user groups generalization, and then characterizes the group preferences by jointly considering historical user-hotel interaction and spatio-temporal features of hotels. For data sparsity, we develop a graph neural network, which employs long-term data, and further design an auxiliary loss function of location that efficiently exploits data within the same and across different locations. Both offline and online experiments demonstrate the effectiveness of LINet when compared with state-of-the-art methods. LINet has been successfully deployed on Fliggy to retrieve high quality hotels for business development, serving hundreds of hotel operation scenarios and thousands of hotel operators.
PipeEdge: A Trusted Pipelining Collaborative Edge Training based on Blockchain

Powered by the massive data generated by the blossom of mobile and Web-of-Things (WoT) devices, Deep Neural Networks (DNNs) have developed both in accuracy and size in recent years. Conventional cloud-based DNN training incurs rapidly-increasing data and model transmission overheads as well as privacy issues. Mobile edge computing (MEC) provides a promising solution by facilitating DNN model training on edge servers at the network edge. However, edge servers often suffer from constrained resources and need to collaborate on DNN training. Unfortunately, managed by different telecoms, edge servers cannot properly collaborate with each other without incentives and trust. In this paper, we introduce PipeEdge, a scheme that promotes collaborative edge training between edge servers by introducing incentives and trust based on blockchain. Under the PipeEdge scheme, edge servers can hire trustworthy workers for pipelined DNN training tasks based on model parallelism. We implement PipeEdge and evaluate it comprehensively with four different DNN models. The results show that it outperforms state-of-the-art schemes by up to 173.98% with negligible overheads.
Graph Traversal (GT) on Linear and Quadratic Graphic Processing Units with CPU

Nowadays, Graph Traversal (GT) is a fundamental procedure for developing a measure in computing systems and algorithms. Graph traversal algorithms usually iterate input datasets of a graph to provide a logical convergence in every iteration. Graphic Processing Units (GPUs) have been utilized broadly as accelerators of GT due to their massive parallelism and accessibility of high-transfer speed memory. Furthermore, graph-based computation is pervasive and challenging for memory systems implementation in a specific time period. This paper provides quadratic and single GPUs, allowing developers to extend graph-based GPU computation effortlessly. The paper concentrates on the creditability and validation of sparse graphs in GPUs. Since the benchmark characteristic of Breadth-First Search (BFS) plays a critical role in graphs, this paper considers a primary characteristic benchmark for BFS computation usage. This paper explores the effectiveness and applicability of BFS for analyzing graph systems by utilizing the mentioned characteristics in BFS computation. This paper also utilizes the penalization of GPU BFS during the analysis and simulation results. In addition, using a comparative simulation result, this paper concludes that improved traversal rates speed up the computation and reduce memory usage for the single and quadratic GPU structures.
Interest-aware Collaborative filtering Recommendation Model based on Graph Neural Networks

Abstract Recommendation systems have become an integral part of various online platforms, providing personalized recommendations to users while reducing information overload. Graph Convolution Neural Network (GCN) based models have become the most popular research method in recommendation systems. These models have the ability to learn embedding representations on graph structures, which makes them effective in modeling recommendation systems. However, the GCN model suffers from the oversmoothing problem, which makes node representations indistinguishable after multiple layers of graph convolution. Researchers have proposed models such as LR-GCN, Light-GCN, DropEdge, and IMP-GCN to mitigate this problem. Despite the significant advancements in GCN-based models, they still do not take full advantage of higher-order neighbors' covariance signals. Additionally, they do not consider the influence of users' different opinions on the recommendation results when propagating and aggregating synergistic signals. This paper proposes a graph convolutional neural network recommendation model with interest-aware message delivery that utilizes 2nd-order neighbors' cooperative signals and users' views to improve accuracy in learning node embeddings. Our model separates subgraphs for users and products, allowing for intergroup crossover in the classification. The proposed model addresses the shortcomings of current GCN-based models, and it is evaluated on several datasets. The results show that our model outperforms state-of-the-art models in terms of prediction accuracy, making it a promising recommendation model for various applications.
Implementation and Maintenance of FAST as Linked Data in a Digital Collections Platform at University of Victoria Libraries

University of Victoria Libraries has implemented faceted vocabularies, particularly FAST, in its digital collections platform (Vault). The process involved migrating a variety of standardized (pre-coordinated Library of Congress subject headings) and non-standardized metadata to conform to a URI-centric metadata application profile. The authors argue that faceted vocabularies and FAST have helped to create a robust and intuitive user navigation in the platform and allowed for an efficient and straightforward metadata creation process. Maintaining FAST as linked data within Vault has required putting in place some technical processes to keep URIs and textual labels up to date and solutions (FAST Updater) have been locally developed.
Perancangan Mesin Pencetak Paving Blok Hexagonal Diagonal 20 CM

Paving blocks are one of the most needed materials in rural and urban areas. In the East Luwu region, most of the paving blocks are produced by manual manufacture which takes a long time. Making paving blocks manually usually only compacts the sand grains that are collected or pounded on the mold. This method allows for air voids between the grains of sand which causes a decrease in the quality of the paving blocks. The purpose of this research or final project is to design a paving block making machine in the shape of a hexagonal with a diagonal of 20 cm that uses a hydraulic power press. The design consists of designing the form of the arrangement of the drawings and parts, the design of the manufacture, maintenance and work safety. The design result is a Hexagonal Block Paving Machine with a diagonal of 20 centimeters consisting of a hydraulic system, mold components, and a vibrating machine supported by a C profile steel frame (chanel steel) with a compression force of 3.2 tons. The hexagonal block paving machine with a diagonal of 20 centimeters is designed to produce eight paving blocks at one time.
Analisis Instrumen Tes Hasil Belajar Matematika Kelas V Sekolah Dasar

This study aims to determine the quality of the Instrument of Mathematics Learning Outcomes Class V Primary Schools used by educators seen from the level of validity, level of reliability, level of truth, and the level of power distinguishing questions. This research was conducted at SDN Moro in the even semester of the academic year 2021/2022. The population of this study was all 96 students. The sampling technique in this study is to use purposive sampling technique. The sample in this study was 21 SDN Moro SDN students. This study uses quantitative methods. Data collection techniques in this study are tests. Data Analysis Researchers use quantitative descriptive analysis with a percentage. Based on the results of the analysis of the level of validity, reliability, difficulty level, and distinguishing power it can be seen that the quality of the test instrument as a whole is good questions and stored in question banks, namely 5 questions or 17%, revised and used 11 questions or 37% and discarded 14 questions or 46%.
Image Caption Generator by using CNN and LSTM

In this article, we systematically analyze a deep neural networks-based image caption generation method. Image Captioning aims to automatically generate a sentence description for an image. Our article model will take an image as input and generate on English sentence as output, describing the contents of the image. It has attracted much research attention in cognitive computing in the recent years. The task is rather complex, as the concepts of both computer vision and natural language processing domains are combined together. We have developed a model using the concepts of a Convolutional Neural Network (CNN) and long Short-Term Memory (LSTM) model and build a working model of Image caption generator by implementing CNN and LSTM. After the caption generation phase, we use BLEU Scores to evaluate the efficiency of our model. Thus, our system helps the user to get descriptive caption for the given input image.
Padlet as a Tool in Learning L2 Grammar

Padlet can be used as a platform to collaborate in collecting ideas, brainstorming, and more. Students will be able to access it for free, no matter what device they have. Padlet is used in this research as a tool in learning L2 Grammar as it is in line with the Malaysian education blueprint whereby there is the need of leveraging ICT to scale up quality learning across Malaysia in shift 7. This study explores the impact of Padlet in learning L2 grammar among primary ESL students and it aims to study the students’ perceptions of using Padlet in learning L2 grammar and it consists of observation, document analysis, pre-post tests and semi structured interviews collected from 24 respondents and 2 teachers. The data were analysed using SPSS and thematic analysis. The findings suggest that when students use learning software such as Padlet, they achieve better understanding of L2 Grammar because Padlet highly motivates them to connect the use of technology with the contents of the lesson. Moreover, they are able to trace their grammar acquisition by doing their self-assessment from the activities and material provided by the teacher during every lesson.
Efficient Human Face Recognition in Real-Life Applications using the Discrete Wavelet Transformation (HFRDWT)

&lt;p&gt;Human Face receives major attention and acquires most of the efforts of the research and studies of Machine Learning in detection and recognition. In real-life applications, the problem of quick and rapid recognition of the Human Face is always challenging researchers to come out with powerful and reliable techniques. In this paper, we proposed a new human face recognition system using the Discrete Wavelet Transformation named HFRDWT. The proposed system showed that the use of Wavelet Transformation along with the Convolutional Neural Network to represent the features of an image had significantly reduced the face recognition time, which makes it useful in real-life areas, especially in public and crowded places. The Approximation coefficient of the Discrete Wavelet Transformation played the dominant role in our system by reducing the raw image resolution to a quarter while maintaining the high level of accuracy rate that the raw image had. Results on ORL, Japanese Female Facial Expression, extended Cohn-Kanade, Labeled Faces in the Wild datasets, and our new Sudanese Labeled Faces in the Wild dataset showed that our system obtained the least recognition timing (average of 24 milliseconds for training and 8 milliseconds for testing) and acceptable high recognition rate (average of 98%) compared to the other systems.&lt;/p&gt;
Qsign Comparator Ex Tool for Handwriting Characteristics Analysis

The activation of electronic signature commences a new interest in handwriting examination in the field of forensic discipline and the criminal justice community. The study was designed to develop a tool that is more available and usable to the students or trainees in Forensic Questioned Document Examination. Waterfall software development methodology was used in this study which consists of several phases such as requirement, design, implementation, verification and maintenance. This Handwriting Analysis Tool enables the users to experience its features through web browser. Several significant features of this tool are the following; actual signature comparison, navigation, characteristics labelling, snapshot and the over-all conclusion of the examination. This effort may increase the technical competency of the students in a specific handwriting examination.
Artificial Intelligence

Artificial Intelligence (A.I.) is a multidisciplinary field focused on automating tasks that currently require human intelligence. Despite its lack of general awareness, artificial intelligence (AI) is a technology that is revolutionizing all aspects of life. This article aims to educate lay people about AI and encourage them to use it as a tool in many fields to rethink how we combine data, analyze it and make decisions. In this article, we quickly discussed what artificial intelligence (AI) is, how it works, and how it can be used in our daily lives
Cloud Cryptography

Cloud computing is a membership - a contract dependent on which you can get network disk space and device services. The paper also include some algorithms used in our future. This helps us to save our data from breaches and ensure security
‘Verbs’ in Thesis and Journal-Article Introductions: A Comparative Functional Approach

This transitivity study aims to delineate the ‘verbs’ employed in the introduction sections of students’ English-major theses (IST) in reference to those of the introduction sections of research articles (ISRA) published by TESOL journal. To obtain this purpose, ten introduction sections from ISRA were analysed as the parameter, and 10 from its IST counterparts were evaluated. Both data sets were initially scrutinized for their categorization of process types. Then the analysis was followed by the categorization of specific processes in each process type. All data analysis was executed by employing the tables of analysis adapted from the transitivity framework of Halliday and Matthiessen. The transitivity analysis reveals that compared with the data of TESOL journal, the meaning making in the IST is inclined to be represented by less investigative processes of material, verbal, and mental clauses. Some less investigative processes are reflected from the verbs ‘use’, ’want’, ‘say’, etc., while the knowledge building processes in the ISRA are shown by more investigative processes such as ‘investigate’, ‘find’, ‘question’, etc. Overall, in reference to the clauses in the research articles (RA) of TESOL journal, the ones in the introduction sections of students’ undergraduate theses still lack processes for supporting the basis of knowledge building. It is expected that the findings of this study contribute to the field of academic writing, that is, to give insight to student thesis writers into how to make meaning of their introduction functionally.
Kernel semi-parametric model improvement based on quasi-oppositional learning pelican optimization algorithm

Statistical modeling is essential in many scientific research areas because it explains the relationship between the response variable of interest and a number of explanatory variables. However, it is not easy to determine the optimal model beforehand. Therefore, in this paper, we look at how to choose a hyper-parameter in a kernel semi-parametric regression model. A quasi-oppositional learning pelican optimization algorithm strategy is used to select the smoothness parameter. In comparison to other competitor approaches, simulation results revealed that the suggested method, the quasi-oppositional learning pelican optimization algorithm, is superior in terms of MSE. The experimental findings and statistical analysis show that when compared to the CV and GCV, our proposed quasi-oppositional learning pelican optimization algorithm provides greater performance in terms of computational time.
ORGANIZATIONAL CULTURE AND ITS IMPORTANCE FOR THE EFFECTIVENESS OF EDUCATIONAL ORGANIZATION AND THE PSYCHOLOGICAL WELL-BEING OF STUDENTS

The article is devoted to the influence of the organizational culture of an educational organization on the objective indicators of its effectiveness and the psychological well-being of students in the example of schools in the city of Yaroslavl. A comparative analysis of the effectiveness of educational organizations with a predominance of types of personality and power cultures is presented. The conclusion is made about the existing potential for the formation of the psychological well-being of students in all the presented types of organizational cultures.&#x0D; In the context of these cultures, the authors consider such components of the psychological well-being of students as self-esteem, anxiety, motivation, success and academic success. Differences in the level of satisfaction with the education received among students were revealed depending on the objective indicators of the effectiveness of schools and the prevailing type of organizational culture. The possibilities of positive psychology and resource approach for the formation of psychological well-being at school are indicated.
Implementasi metode Ward clustering untuk klasterisasi penduduk yang memiliki jaminan kesehatan pada kabupaten dan kota di Provinsi Maluku

The Health Insurance Program is a protection guarantee for comprehensive health services which includes promotive, preventive and curative and rehabilitative services provided in stages for the community/participants whose contributions are paid by the government. The health insurance program in Maluku Province from year to year has been getting better. However, there are still disparities or gaps between regions in Maluku Province, so it is necessary to study the clusterization of the population who have health insurance in districts/cities in Maluku Province. Ward clustering method is a method that aims to get a cluster that has the smallest possible internal cluster variance. The purpose of this study is to obtain a cluster of residents who have health insurance in districts/cities in Maluku Province. The data used in this study were sourced from Maluku publications in figures at the Maluku Province BPS. This study obtained the results that there are 3 clusters in the cluster of residents who have health insurance in districts/cities in Maluku Province with details of cluster 1 consisting of Tanimbar Islands Regency, Central Maluku, Buru, West Seram, East Seram and South Buru District. Cluster 2 consists of Southeast Maluku Regency, Aru Islands, Southwest Maluku and Tual City. Furthermore, Cluster 3 consists of Ambon City.&#x0D; Keywords: Clustering, health insurance, wardMSC2020: 62H30
CRYPTOGRAPHIC METHOD TO ENHANCE DATA SECURITY USING RSA ALGORITHM AND MELLIN TRANSFORM

Cryptography is the technique of using mathematical algorithms to encrypt and decrypt the information. The process of converting plaintext to ciphertext is known as encryption, whereas the process of converting ciphertext to plaintext is known as decryption. Encryption and decryption methods based on Mellin Transform are unable to provide more security while transmitting the information. RSA algorithm is an Asymmetric key cryptography algorithm. The purpose of this study is to present a cryptographic method that uses the RSA algorithm and Mellin Transform to improve communication security.
Hybrid Quantum Artificial Intelligence Electromagnetic Spectrum Analysis Framework for Transportation System Security

Abstract The traffic signal control (TSC) system is faced with both opportunities and challenges as a consequence of connected vehicle (CV) technology. Although implementing CV technology might considerably enhance safety and mobility performance, the connection between vehicles and transportation infrastructure may raise the dangers of cyber-attacks. Studies on cybersecurity in TSC systems have been undertaken in recent years. However, a safe framework for air-gapped malware analysis is still lacking. Our study aims to close this research gap by presenting a thorough electromagnetic analysis approach to address the cybersecurity issue facing the TSC in the CV environment. In this technique, a hybrid deep-learning architecture based on classical-quantum transfer learning models is constructed to study electromagnetic (EM) spectra. We assess the effect of adversarial attacks on TSC systems using these hybrid models and distinguish attacks from normal operations of the controller. A neural network trained on a dataset to collect pertinent characteristics from a high-dimensional dataset of Electromagnetic (EM) trace-based TSC attack vectors form the basis of hybrid models. The outcome of the standard deep learning process is then examined by the quantum layer. A number of quantum gates make up the quantum layer, which can enable a number of quantum mechanical activities, including superposition and entanglement. The most conceivable and feasible attack approach in transport signal controllers has been found to be data spoofing, and the potential of the proposed air-gapped electromagnetic Hybrid classical quantum monitoring framework in sensor fusion and interrupts is explored in light of these findings.
Mahalanobis Distance of Reservoir States for Online Time-Series Anomaly Detection

&lt;p&gt;Automated anomaly detection in time-series data has important applications in modern society. In practical settings, sequence patterns must often be evaluated in real-time under limited computational resources and scarce training data, presenting significant challenges. Reservoir computing models can help reconcile the trade-off between computational expense and precision in such situations. We propose the Mahalanobis distance of reservoir states (MD-RS) method, as an alternative to standard reservoir-based methods that rely on prediction error. MD-RS can effectively highlight anomalies because it measures the deviation from the subspace that minimally encompasses reservoir responses to normal inputs. Our semi-supervised MD-RS method only requires training data labeled as normal, making it suitable for practical scenarios under limited training data. Both the training and inference can be performed online with reduced computational cost, enabling real-time anomaly detection. We apply the MD-RS method to a benchmark dataset with a short training phase from the UCR time series anomaly archive and demonstrate its superiority in terms of accuracy, reliability, and robustness over prediction error-based methods when employing the same reservoir. Additionally, we propose the response dimension increase (RDI) index for initial hyperparameter tuning, which measures the increase in the effective dimension of the reservoir trajectories. These results position our method as a leading choice for applications requiring rapid and accurate anomaly detection.&lt;/p&gt;