Reviewerly/exHarmony huggingface dataset:
Graph-Based Recommendation for Sparse and Heterogeneous User Interactions

Recommender system research has oftentimes focused on approaches that operate on large-scale datasets containing millions of user interactions. However, many small businesses struggle to apply state-of-the-art models due to their very limited availability of data. We propose a graph-based recommender model which utilizes heterogeneous interactions between users and content of different types and is able to operate well on small-scale datasets. A genetic algorithm is used to find optimal weights that represent the strength of the relationship between users and content. Experiments on two real-world datasets (which we make available to the research community) show promising results (up to $$7\%$$ improvement), in comparison with other state-of-the-art methods for low-data environments. These improvements are statistically significant and consistent across different data samples.
TourismNLG: A Multi-lingual Generative Benchmark for the Tourism Domain

The tourism industry is important for the benefits it brings and due to its role as a commercial activity that creates demand and growth for many more industries. Yet there is not much work on data science problems in tourism. Unfortunately, there is not even a standard benchmark for evaluation of tourism-specific data science tasks and models. In this paper, we propose a benchmark, TourismNLG, of five natural language generation (NLG) tasks for the tourism domain and release corresponding datasets with standard train, validation and test splits. Further, previously proposed data science solutions for tourism problems do not leverage the recent benefits of transfer learning. Hence, we also contribute the first rigorously pretrained mT5 and mBART model checkpoints for the tourism domain. The models have been pretrained on four tourism-specific datasets covering different aspects of tourism. Using these models, we present initial baseline results on the benchmark tasks. We hope that the dataset will promote active research for natural language generation for travel and tourism. ( https://drive.google.com/file/d/1tux19cLoXc1gz9Jwj9VebXmoRvF9MF6B/ .)
INTEGRASI LAYANAN INFORMASI DALAM MENINGKATKAN KAPASITAS PEMAHAMAN MAHASISWA MENGHADAPI BENCANA ALAM

The situation and condition of the universe has a great influence on individual attitudes and environmental changes. Failure to create environmental friendliness can be seen from unstable natural conditions such as floods, earthquakes, landslides. This change is caused by a lack of awareness, low environmental care from individuals, and the surrounding community. The purpose of this study is to provide students with an understanding of the causes of natural disasters, how to respond appropriately so as not to harm the community when natural disasters occur as part of preventive measures through the provision of information services. This research uses qualitative-descriptive method. Interview data collection techniques and documentation. the main informants consisted of 4 5th semester students and 2 BKPI lecturers supporting informants. The research results obtained from interviews with BKPI students in semester 5 include their self-understanding of natural disaster situations that have not been able to be handled properly. This cause becomes a joint evaluation of the community, students, and the local government to prioritize the provision of disaster preparedness information services. Information services have an important role in increasing knowledge and self-understanding for each individual to respond to disaster situations with stable emotional conditions, minimizing anxiety levels and being able to adapt well to adaptation in the post-disaster environment.
Satisfaction with Online and Face-to-Face Learning: HRD Practices in Times of Crisis and Opportunity

This study investigated the determinants of public officials' satisfaction with learning effectiveness in Korea during the COVID-19 pandemic, when an unprecedented amount of learning began to take place online due to the closure of traditional educational settings. Thus, this study aims to understand employees' satisfaction with online learning compared to face-to-face learning. A survey was conducted about perceptions of differences in goal management, learning needs, and satisfaction with learning in different settings in the public sector. The findings show that employees' satisfaction with face-to-face learning is impacted by goal management and learning needs while program length is associated with employees' satisfaction with online learning. The current study has found that online learning has a limited impact on satisfaction and some types of face-to-face learning programs cannot be replaced by online learning methods. Based on the result, this study provides several implications for HRD professional to develop online and face-to-face learning curriculum.
Design and Analysis of Convolutional Neural Layers: A Signal Processing Perspective

Convolutional layers (CLs) are ubiquitous in contemporary deep neural network (DNN) models, commonly used for automatic feature extraction. A CL performs cross-correlation between the input to the layer and a set of learnable kernels to produce the layer output. Typically, kernel weights are randomly initialized and automatically learned during model training using the backpropagation and gradient descent algorithms to minimize a specific loss function. Modern DNN models comprise deep hierarchical stacks of CLs and pooling layers. Despite their prevalence, CLs are perceived as a magical tool for feature extraction without solid interpretations of their underlying working principles. In this work, we advance a method for designing and analyzing CLs by providing novel signal processing interpretations of the CL by exploiting the correlation and equivalent convolution functions of the layer. The proposed interpretations enable the employment of CLs to develop finite impulse response (FIR) filters, matched filters (MFs), short-time Fourier transform (STFT), discrete-time Fourier transform (DTFT), and continuous wavelet transform (CWT) algorithms. The main idea is to pre-assign the CL kernel weights to implement a specific convolution- or correlation-based DSP algorithm. Such an approach enables building self-contained DNN models in which CLs are utilized for various preprocessing and feature extractions tasks, enhancing the model portability, and cutting down the preprocessing computational cost. The proposed DSP interpretations provide an effective means to analyze and explain the operation of automatically trained CLs in the time and frequency domains by reversing the design procedures. The presented interpretations are mathematically established and experimentally validated with a comprehensive machinery fault diagnosis application example illustrating the potential of the proposed methodology.
Quantum Advantage of Binary Discrete Modulations for Space Channels

We assume information is discrete-modulated over coherent states of the single-mode electromagnetic field and transmitted over space quantum channels. In this letter, we provide a comparison of quantum detection with classical detection for communication using on-off keying (OOK) and binary phase shift keying (BPSK) for illustrative system parameters for LEO, MEO and GEO satellites. To do so, we assume an attenuation-only (pure-loss) channel and compute the Shannon capacities for the following detection methods: classical (shot-noise limited) single-photon and coherent detectors as well as optimal quantum hypothesis-testing (Helstrom) detection. As in classical system engineering design, we also obtain theoretical references for comparison, namely, Shannon capacities and Holevo bounds for the ideal Gaussian optical channel. Our results show the ranges of transmitted power and orbital heights for which either classical or quantum detection is optimal, thus providing useful insights into design guidelines and operations of future space communication systems. We also obtain the remarkable result that the BPSK Holevo bound achieves the ultimate quantum capacity of the considered channel.
Monocular Road Scene Bird’s Eye View Prediction via Big Kernel-Size Encoder and Spatial-Channel Transform Module

A detailed representation of the surrounding road scene is crucial for an autonomous driving system. yellow The camera-based Bird’s Eye View map has been a popular solution to present the surrounding information, due to its low cost and rich spatial context information. Most of the existing methods predict the BEV map based on the depth-estimation or the trivial homography method, which may cause the error propagation and the absence of content. To overcome these drawbacks, we propose a novel end-to-end framework that employs the front monocular image to predict the road layout and vehicle occupancy. In particular, to capture the long-range feature, we redesign a CNN encoder with a large kernel size to extract the image features. For reducing the big difference between the front image features and the top-down features, we propose a novel Spatial-Channel projection module to convert the front map into the top-down space. Additionally, concerning the correlation between front view and top-down view, we propose the Dual Cross-view Transformer module to refine the top-down view feature maps and strengthen the transformation. Extensive evaluations on the KITTI and Argoverse datasets present that the proposed model achieves the state-of-the-art results for both datasets. Furthermore, the proposed model runs in 37 FPS on a single GPU, demonstrating the generation of a real-time BEV map. The code will be published at https://github.com/raozhongyu/BEV_LKA.
Dynamic Three-Dimensional Lift Planning for Intelligent Boom Cranes

Manually planning a crane's lifting path is complicated and laborious since it typically depends on the planner's experience. We examined dynamic three-dimensional lift planning, including problem formulation for multiobjective planning and problem-solving with an improved A* algorithm. First, we quantified various feasible movements by introducing energy and time consumption models to determine the most efficient and quickest path. Second, we examined a new method for determining the visible lifting path in Configuration space (C-space) and derived an obstacle enveloping method for collision detection in C-space. Our method is both safe and feasible. Finally, we proposed an improved A* algorithm for lift planning considering multiple objectives and featuring better computational efficiency. The experimental and simulation results demonstrate that the proposed lift planning method can comprehensively consider multiple objectives, thus providing a high-quality path for actual lifting. Furthermore, compared with the ordinary A* algorithm, the improved A* algorithm had better computational efficiency and consumed less energy and time. To our best knowledge, this is the first novel algorithm for planning crane lifts considering energy consumption and visibility in C-space.
An enterprise adaptive tag extraction method based on multi-feature dynamic portrait

Abstract User portrait has become a research hot spot in the field of knowledge graph in recent years and the rationality of tag extraction directly affects the quality of user portrait. However, most of the current tag extraction methods for portraits only consider the methods based on word frequency statistics and semantic clustering. These methods have some drawbacks: they cannot effectively discover the preferred themes of the enterprise, dynamically update the portrait tags, and adapt to the needs of the enterprise. In this paper, we propose an enterprise adaptive tag extraction method based on multi-feature dynamic portrait (ATEMDP). ATEMDP first uses K -means to measure the similarity between enterprise texts in preference division, and converts similar enterprise text clustering problems into tag feature clusters to obtain the point cluster structure containing the distribution of tag preference topics. In addition, in the multi-feature selection, the professional domain thesaurus is introduced for feature expansion, and the topic text is introduced into the Bert model as a sample set to discover the potential features of the enterprise text. In the end, in dynamic tag extraction, BiLSTM and CNN are used to extract features, and dynamic preference tags are obtained by updating enterprise text. THUCNews data set and Ente-pku data set are used for simulation, and seven other methods are considered in comparison. The experimental results indicate that ATEMDP is not only superior to other conventional methods in accuracy and F1-score, but also effectively solves the dynamic tagging problem of enterprise portrait.
Mask‐guided image person removal with data synthesis

Abstract As a special case of common object removal, image person removal is playing an increasingly important role in social media and criminal investigation domains. Due to the integrity of person area and the complexity of human posture, person removal has its own dilemmas. In this paper, a novel idea is proposed to tackle these problems from the perspective of data synthesis. Concerning the lack of a dedicated dataset for image person removal, two dataset production methods are proposed to automatically generate images, masks and ground truths, respectively. Then, a learning framework similar to local image degradation is proposed so that the masks can be used to guide the feature extraction process and more texture information can be gathered for final prediction. A coarse‐to‐fine training strategy is further applied to refine the details. The data synthesis and learning framework combine well with each other. Experimental results verify the effectiveness of the method quantitatively and qualitatively, and the trained network proves to have good generalization ability either on real or synthetic images.
Review of User Interface-Facilitated Serendipity in Recommender Systems

Serendipity has become a desirable quality in designing recommender systems and user interfaces, hence offering a new measurement for system quality. At the same time, recognizing serendipitous experiences and determining their value is difficult due to their subjective nature. This review builds on 10 studies of user interfaces facilitating serendipity studies and attempts to understand the patterns that guide relevant user interface designs in recent recommender systems. This study furthers our understanding of turning the elusive experience of serendipity into more actionable user interface designs and patterns. The key findings are as follows. First, user controls and visualizations have facilitated serendipity, but studies of recommender systems have not gained considerable attention. Second, frameworks instrumental for user-interface-facilitated serendipity have not gained the researcher's worthy attention. Third, developing countries need to explore serendipity-facilitating recommender systems with more diverse users and more prominent test cases.
Evaluation of the Construction of a Data Center-Driven Financial Shared Service Platform From the Remote Multimedia Network Perspective

The financial shared service (FSS) platform under the remote multimedia network perspective is a newly proposed accounting and reporting business management mode in recent years that plays a good role in the operation of enterprises. The multimedia technology in the health cloud biological authentication and data management system can be applied to the construction of the data center-driven FSS platform. This study used multimedia in the data management system to build a data center-driven FSS platform to maintain the normal operation of the enterprise. Moreover, the study analyzed from the perspective of employee satisfaction, work efficiency, enterprise cost, and work continuity and drew a conclusion. The survey of employee satisfaction showed that the satisfaction of employees after modeling had been greatly improved compared with that before modeling. Moreover, according to the survey of work efficiency, the work efficiency of employees in the five enterprises after modeling has been greatly improved compared with that before modeling. Furthermore, in the survey of enterprise cost, the cost reduction effect of postal enterprises is best after using multimedia technology to model the data center-driven FSS platform. Then, according to the survey on work continuity, the work continuity of the five enterprises has been improved to varying degrees after the establishment of the FSS platform. Among them, the work continuity of Enterprise 2 has been improved by 11%, with the best effect. The multimedia modeling in the health cloud biological authentication and data management system plays a role in promoting the development of enterprises.
Digitization of cultural heritage and copyright

The rapid technical and technological progress in the second half of the twentieth century led to the increasing use of computers in the economy and society and to the dominance of digital technology over the use of mechanical and electronic analog devices. Therefore, copyright has faced the challenge of how to regulate the digitization of copyrighted works, which in today's conditions is necessary for the development of innovation, science and knowledge transfer. The problem is that the increased technological possibilities have facilitated the reproduction of copyrighted works and their availability to the public via the Internet, so that copyright infringements occur online on a daily basis, and the scale of such infringements is enormous. As copyright is the foundation of the creative industry, because it encourages innovation, creation, investment in the creative industry and production, it is necessary to change the existing normative framework and adapt to the changes brought by the rapid development of technology. Attempts to legally regulate digitalization within the European Union in cases where there is no consent of the copyright holder began at the beginning of this century and continue to this day, facing numerous challenges brought by the modern age of the Internet and globalization. The European Union has approached this process very committedly, passing a large number of acts, primarily directives, which seek to enable the harmonization of copyright and related rights in the information society. In order to facilitate the process of preservation and dissemination of cultural heritage, it is necessary to implement in our law on copyright and related rights the solutions from the European Directive 2012/28 and Directive 2019/790.
tegdet: An extensible Python library for anomaly detection using time evolving graphs

This paper presents tegdet, a new Python library for anomaly detection in unsupervised approaches.The input of the library is a univariate time series, representing observations of a given phenomenon.Then, tegdet identifies anomalous epochs, i.e., time intervals where the observations differ in a given percentile of a baseline distribution.Epochs are represented by time evolving graphs and the baseline distribution is given by the dissimilarities between a reference graph and the graphs of the epochs.Currently, the library implements 28 dissimilarity metrics, i.e., 28 different anomaly detection techniques, and its extensible design allows to easily introduce new ones.tegdet exposes a complete functionality to carry out the anomaly detection, through a straightforward designed API.Summarizing, to the best of our knowledge, tegdet is the first publicly available library, based on time evolving graphs, for anomaly detection in time series.Our experimentation shows promising results.For example, Clark and Divergence techniques can achieve an accuracy of 100%, while the time to build the model and predict lasts for few hundreds milliseconds.
Requests classification in the customer service area for software companies using machine learning and natural language processing

Artificial intelligence (AI) is one of the components recognized for its potential to transform the way we live today radically. It makes it possible for machines to learn from experience, adjust to new contributions and perform tasks like human beings. The business field is the focus of this research. This article proposes implementing an incident classification model using machine learning (ML) and natural language processing (NLP). The application is for the technical support area in a software development company that currently resolves customer requests manually. Through ML and NLP techniques applied to company data, it is possible to know the category of a request given by the client. It increases customer satisfaction by reviewing historical records to analyze their behavior and correctly provide the expected solution to the incidents presented. Also, this practice would reduce the cost and time spent on relationship management with the potential consumer. This work evaluates different Machine Learning models, such as support vector machine (SVM), Extra Trees, and Random Forest. The SVM algorithm demonstrates the highest accuracy of 98.97% with class balance, hyper-parameter optimization, and pre-processing techniques.
Remote Sensing Image Object Detection Based on Improved YOLOv3 in Deep Learning Environment

A deep learning-based method, improved YOLOv3 algorithm is proposed in the deep learning environment to tackle challenges such as big scale, uneven distribution, largescale variation, and complicated background of small- and medium-sized remote sensing photos. This manufacture uses Densenet as the backbone, replacing Darknet-53 to realize feature reuse and make the feature extraction more effective; introduces the spatial pyramid pooling module into the feature pyramid part for increasing the receptive field and isolating the most prominent contextual features; adds SE attention module in the process of feature extraction and obtains richer features by learning more location information and channel information from the images. Under DOTA dataset, the final results are that the mean Average Precision value is 86.78%, which is 4.16% higher than the baseline YOLOv3 network. The model put forward makes it easier to extract information from the feature map and achieve higher detection accuracy without influencing the real-time performance of detection.
A QoS-based technique for load balancing in green cloud computing using an artificial bee colony algorithm

Nowadays, high energy amount is being wasted by computing servers and personal electronic devices, which produce a high amount of carbon dioxide. Thus, it is required to decrease energy usage and pollution. Many applications are utilised by green computing to save energy. Scheduling of tasks acts as an important process to reach the mentioned goals. It is worth stating that the vital characteristic of task scheduling in green clouds is the load balancing of tasks on virtual machines. Efficient load balancing moves tasks from overloaded to underloaded virtual machines to maintain the Quality of Service (QoS). This issue is an NP-complete problem, so this research suggests a new technique based on the behavioural structure of artificial bee behaviour. This method aims to improve QoS while lowering energy usage in green computing. In addition, the honey bees are considered the removed tasks from overloaded virtual machines and a candidate for migrating selected tasks with the lowest priority. The CloudSim testing findings demonstrate that the technique is successful in QoS, makespan, and energy usage compared to other ways.