Reviewerly/exHarmony huggingface dataset:
A Recommendation System Based on Big Data: Separation of Preference and Similarity

With the development of the web, smart devices, and social networks, the utilization of tags has increased. This study proposes a recommendation system that can be used in tag-based web services. This study measures the user’s preference and similarity by reflecting the frequency and timing of the use of tags. In particular, this study reflected the tagging time on a cardinal scale for more precise information. To evenly consider the size of the neighbors, the size is systematically surveyed from 3 to 30. In the process of determining the recommendation, a methodological combination is constructed by reflecting preferences and similarities without mutual dependence. Actual big data of social bookmark users were collected and applied to verify performance. Precision, Recall, and F-ratio were measured as verification indicators. The analysis results found that techniques with the best performance among the proposed algorithms were superior to benchmarks in all indicators. The methodology and results of this study may be applicable to various tagging systems.
A Question-Answering System on COVID-19 Scientific Literature

The vast amount of COVID-19 research literature has made it difficult for medical experts, clinical scientists, and researchers to keep up with the latest research findings. We present two datasets for COVID-19 in this work: (1) first, we create a dataset from the up-to-date scientific publications on COVID-19, and (2) second, we build a gold-standard dataset of question-answering pairs annotated by volunteer biomedical experts on COVID-19 related scientific articles. We develop a question-answering (QA) pipeline that uses the first dataset to provide answers related to COVID-19 questions; we fine-tune MPNet (a Transformer model) on our gold-standard dataset and use it in the QA pipeline to enhance its reading capability. We also use this gold-standard dataset to evaluate the QA pipeline. The proposed MPNet version on the gold-standard dataset outperformed previous datasets and models, achieving an Exact Match/Fl score of 69.72/78.50 %, respectively
Design of medical robot control system based on single-chip microcomputer

In order to save manpower, improve the management of COVID-19 prevention and prevent the spread of the epidemic, this paper proposes and designs a medical robot based on a one-chip computer. The single-chip STC89C52 is used as the main control core. Obstacles are detected by infrared sensors. And the robot uses the tracking module to determine the path. The working states of the two DC motors are then changed by the IO-port control L298N drive template, thereby changing the motion state of the robot through the speed difference between the motors on both sides. In the intelligent tracking module, the robot first uses a genetic algorithm to find the best path forward inspection and then enters the ward. After disinfection, the robot uses STM32F4 to drive the OV2640 camera to collect data and detect the mask using the yolov5s algorithm. Finally, it sends the collected information to the computer to realize the real-time monitoring of the patient's condition. The simulation results show that the medical robot can effectively and accurately realize the requirements of path planning, facial mask recognition, and wireless communication. This will significantly improve the efficiency and safety of medical staff.
Fitting Bell Curves to Data Distributions Using Visualization

Idealized probability distributions, such as normal or other curves, lie at the root of confirmatory statistical tests. But how well do people understand these idealized curves? In practical terms, does the human visual system allow us to match sample data distributions with hypothesized population distributions from which those samples might have been drawn? And how do different visualization techniques impact this capability? This article shares the results of a crowdsourced experiment that tested the ability of respondents to fit normal curves to four different data distribution visualizations: bar histograms, dotplot histograms, strip plots, and boxplots. We find that the crowd can estimate the center (mean) of a distribution with some success and little bias. We also find that people generally overestimate the standard deviation-which we dub the "umbrella effect" because people tend to want to cover the whole distribution using the curve, as if sheltering it from the heavens above-and that strip plots yield the best accuracy.
Self-Learning Chatbots using Reinforcement Learning

There has been a surge in the development of dialogue generation systems also called chatbots in Recent years. Research on sequence-to-sequence architectural chatbots has led to a number of efficient and adaptive chatbots. Several of them worked on goal-oriented chatbots, some worked on open domain chatbots and others developed emotional chatbots called CheerBots that respond to people&#x2019;s emotions. Be it in the IT field or in Education, these Dialogue systems have proven their worth in every industry. This paper discusses, the proposal of a SEQ2SEQ architecture-based chatbot that uses a Reinforcement Learning Algorithm to respond to user queries. This allows the model to explore the domain of all possible responses that can be generated by the SEQ2SEQ model by learning novel utterances/queries. The model answered all the queries, making it a suitable chitchat system for open-domain applications like SIRI and ALEXA. It responded in an engaging and interactive manner which led to an interesting conversation. The model, despite being complex, showed remarkable accuracy.
WikiDes: A Wikipedia-based dataset for generating short descriptions from paragraphs

As free online encyclopedias with massive volumes of content, Wikipedia and Wikidata are key to many Natural Language Processing (NLP) tasks, such as information retrieval, knowledge base building, machine translation, text classification, and text summarization. In this paper, we introduce WikiDes, a novel dataset to generate short descriptions of Wikipedia articles for the problem of text summarization. The dataset consists of over 80k English samples on 6987 topics. We set up a two-phase summarization method — description generation (Phase I) and candidate ranking (Phase II) — as a strong approach that relies on transfer and contrastive learning. For description generation, T5 and BART show their superiority compared to other small-scale pre-trained models. By applying contrastive learning with the diverse input from beam search, the metric fusion-based ranking models outperform the direct description generation models significantly up to ≈ 22 ROUGE in topic-exclusive split and topic-independent split. Furthermore, the outcome descriptions in Phase II are supported by human evaluation in over 45.33% chosen compared to 23.66% in Phase I against the gold descriptions. In the aspect of sentiment analysis, the generated descriptions cannot effectively capture all sentiment polarities from paragraphs while doing this task better from the gold descriptions. The automatic generation of new descriptions reduces the human efforts in creating them and enriches Wikidata-based knowledge graphs. Our paper shows a practical impact on Wikipedia and Wikidata since there are thousands of missing descriptions. Finally, we expect WikiDes to be a useful dataset for related works in capturing salient information from short paragraphs. The curated dataset is publicly available at: https://github.com/declare-lab/WikiDes . • Wikipedia- and Wikidata-based dataset to generate short descriptions from paragraphs. • Two-phase summarization – candidate description generation and ranking. • Sentiment consistency analysis between paragraph and gold description.
An Adversarial Attack Method against Specified Objects Based on Instance Segmentation

The deep model is widely used and has been demonstrated to have more hidden security risks. An adversarial attack can bypass the traditional means of defense. By modifying the input data, the attack on the deep model is realized, and it is imperceptible to humans. The existing adversarial example generation methods mainly attack the whole image. The optimization iterative direction is easy to predict, and the attack flexibility is low. For more complex scenarios, this paper proposes an edge-restricted adversarial example generation algorithm (Re-AEG) based on semantic segmentation. The algorithm can attack one or more specific objects in the image so that the detector cannot detect the objects. First, the algorithm automatically locates the attack objects according to the application requirements. Through the semantic segmentation algorithm, the attacked object is separated and the mask matrix for the object is generated. The algorithm proposed in this paper can attack the object in the region, converge quickly and successfully deceive the deep detection model. The algorithm only hides some sensitive objects in the image, rather than completely invalidating the detection model and causing reported errors, so it has higher concealment than the previous adversarial example generation algorithms. In this paper, a comparative experiment is carried out on ImageNet and coco2017 datasets, and the attack success rate is higher than 92%.
Seismic Performance and Vibration Control of Rapid Construction Environmental Protection Wall Based on Artificial Intelligence

With the continuous increase in building types, people's demand for buildings is increasing. In addition to focusing on the work of the building itself, high requirements are also placed on the safety, stability, and durability of the building. Especially in other earthquake-prone areas, it is even more necessary to strengthen seismic isolation research to reduce vibration. Rapid construction of environmental protection walls pays attention to environmental protection and efficiency during construction, which can effectively solve problems in the construction process, but the seismic resistance of rapid construction environmental protection walls is unknown. Therefore, this paper used artificial intelligence technology to study the seismic performance of rapid construction environmental protection walls and analyzed its vibration control. This paper firstly established the seismic performance analysis model of the environmental protection wall, used the finite element method to construct the environmental protection wall model, and then used the artificial intelligence algorithm to analyze the seismic performance of the rapid construction environmental protection wall. The experimental results showed that the peak load of the rapid construction environmental protection wall was about 250 KN, and the seismic performance was good.
Personalized Recommendation of Online Shopping Products Based on Online Fast Learning through Latent Factor Model

In order to improve the personalized recommendation effect of online shopping products, this article combines online fast learning through latent factor model to construct a personalized virtual planning recommendation system for online shopping products. Moreover, this article improves on the ONMTF model. In the problem of cross-domain recommendation, this article clusters users and items in each data domain with hidden scoring patterns and learns common scoring patterns that can be shared between different data domains to deal with the data sparse problem that often occurs in recommender systems. The experimental research results show that cross-domain recommendation can indeed use the implicit semantics or topics between domains to share information and knowledge, thereby improving the accuracy of recommendation.
Trust Evaluation of Blockchain-Based Cryptocurrencies: The Cases of Bitcoin and Diem

Despite a recent increase in popularity, most individuals remain hesitant to use cryptocurrencies due to concerns about their volatility, general lack of understanding of blockchain technology, and various trust issues. Therefore, it is of high priority for the community to better understand such systems, and what increases or decreases trust in them. In this paper, we explore the current trust-related requirements on blockchain systems, focusing on the use of the dedicated requirements elicitation method TrUStAPIS. We evaluate Bitcoin and Diem trust issues and requirements. Based on the evaluation, we present a cryptocurrency trust model.
Machine Learning Methods for Device Identification Using Wireless Fingerprinting

Industrial Internet of Things (IoT) systems increasingly rely on wireless communication standards. In a common industrial scenario, indoor wireless IoT devices communicate with access points to deliver data collected from industrial sensors, robots and factory machines. Due to static or quasi-static locations of IoT devices and access points, historical observations of IoT device channel conditions provide a possibility to precisely identify the device without observing its traditional identifiers (e.g., MAC or IP address). Such device identification methods based on wireless fingerprinting gained increased attention lately as an additional cyber-security mechanism for critical IoT infrastructures. In this paper, we perform a systematic study of a large class of machine learning algorithms for device identification using wireless fingerprints for the most popular cellular and Wi-Fi IoT technologies. We design, implement, deploy, collect relevant data sets, train and test a multitude of machine learning algorithms, as a part of the complete end-to-end solution design for device identification via wireless fingerprinting. The proposed solution is currently being deployed in a real-world industrial IoT environment as part of H2020 project COLLABS.
AI-based telepresence for broadcast applications

In this paper, we introduce a new solution and underlying architecture that allows remote participants to interact with hosts in a broadcast scenario. To achieve this, background extraction is first applied to video received from remote participants to extract their faces and bodies. Considering that the video from remote participants are usually of lower resolutions when compared to content produced by professional cameras in production, we propose to scale the extracted video with a super-resolution module. Finally, the processed video from remote participants are merged with studio video and streamed to audiences. Given the real-time and high-quality requirements, both background extraction and super-resolution modules are learning-based solutions and run on GPUs. The proposed solution has been deployed in the Advance Mixed Reality (AdMiRe) project. The objective and subjective assessment results show that the proposed solution works well in real world applications.
Improve Detection and Tracking of Pedestrian Subclasses by Pre-Trained Models

There are sub-classes of pedestrians that can be defined and it is important to distinguish between them for the detection in autonomous vehicle applications, such as elderly, and children, to reduce the risk of collision. It is necessary to talk about effective pedestrian tracking besides detection so that object remains accurately monitored, here the effective pre-trained algorithms come to achieve this goal in real-time. In this paper, we make a comparison between the detection and tracking algorithms, we applied the transfer learning technique to train the detection model on new sub-classes, after making Images augmentation in previous work, we got better results in detection, reached 0.81 mAP in real-time by using Yolov5 model, with a good tracking performance by the tracking algorithm dependent on detection Deep-SORT.This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium provided the original work is properly cited.
Determination of Evidence Weights Based on Convolutional Neural Network for Classification Problem

The weighted average is an efficient way to address conflicting evidence combination in the Dempster-Shafer evidence theory. However, it is an open issue how to determine the evidence weights reasonably. Although many traditional conflicting evidence combination solutions based on evidence distance or entropy have been put forward, the evidence weights are determined with a single aspect, and no comprehensive consideration of other useful information affects the weights. Thus, it does not ensure that determination of weights is the most reasonable. By introducing deep learning into conflicting evidence combination, this paper proposes a comprehensive method for determining the evidence weights based on a convolutional neural network. Taking the evidence as the network input and the corresponding weight as the output, it utilizes convolutional neural network to fully mine potentially useful information that affects the evidence weights, in order to determine the weights comprehensively. Additionally, we define a weight loss function. The weights are continuously optimized through back propagation and achieve the optimal when the weight loss function value is the minimum. Classification experimental results demonstrate that the proposed method outperforms traditional ones based on evidence distance or entropy and can be flexibly extended to other application fields as a decision-making fusion method.
Tourism Destination Recommendation and Marketing Model Analysis Based on Collaborative Filtering Algorithm

The Internet has penetrated into all fields. As the most dynamic “sunrise industry,” tourism has also been swept into such a wave of Internet. In such an era of “information overload,” how to find one’s favorite attractions among the massive tourist attractions has become a difficult problem. In order to solve this problem, personalized recommendation technology is applied, among which collaborative filtering recommendation technology is one of the core technologies while the collaborative filtering algorithm still has problems. The research and analysis of the algorithm, this paper improves the technology for the problems of low recommendation accuracy that considers user interest changes. It for attribute scoring. It uses the multiattribute score of the item to calculate the user’s overall evaluation score of each attribute of the item; for the change of user interest, a time function based on the Ebbinghaus forgetting law is introduced to calculate the user similarity. It is given a certain weight, that is, a time function, to ultimately ensure the accuracy of the recommendation. Exploring the tourism destination recommendation and marketing model based on the collaborative filtering algorithm can enrich the relevant theories of it on the one hand, and on the other hand, it can lay the foundation for building a real tourism recommendation website.
Developing a Facial Identification System Using Pre-Trained Model and Spoken Dialogue Agent

Our research group is currently studying and developing listening services using spoken dialogue agents and IoT technologies to assist the “mind” of the elderly at home. However, the user identification function, an essential part of the service, has not yet been realized. It is difficult to determine the identity of the person who interacts with the spoken dialogue agent. Although with the rapid development of the artificial intelligence field, various smart devices and services using deep learning have appeared in the face recognition technology, problems exist, including costs and computational resources to build and apply a recognition model. The purpose of this paper is to develop a facial identification system using the pre-trained model and spoken dialogue agent. Our key ideas include automatic training data generation by spoken dialogue between the user and the agent and the acquisition and comparison of facial features using a pre-trained model. In this way, our face identification system can be easier introduced and expected with only a general-purpose computer and a Web camera, without needing a conventional Internet connection and manual labeling of training data.
Workflow of Extended Reality Applications for Museum Exhibitions

A rapid increase in development and use of Extended Reality applications for all areas of life is expected in metaverse age. Many museums are in the last decade introducing digital content in their exhibitions. They hired digital curators to establish connection with developers and IT industry. Extended Reality technologies enable museum visitors to experience the topics of exhibitions, see original appearances of exhibits and travel to the past through Virtual Reality headsets or Augmented Reality applications on mobile devices. In this paper we show an example of good practice, describing the application Battle on Neretva VR installed in the Museum in Jablanica, Bosnia and Herzegovina. We present the application design and development workflow and show the impressions of museum management and visitors after introducing it in the exhibition. This workflow can be replicated to any museum topic or collection.
Syntactic and Semantic Similarities and Discrepancies between Terms of Glossaries for Software Testing

For software testing, this work performs a comparison and analysis of syntactic and semantic similarities and discrepancies between 3 glossaries. To conduct the study, 8 terminological categories were conceived, which were used to categorize each glossary term, considering the intended semantics. Also, to count the occurrence frequency of a term in the glossaries, a tool was built that also takes into account the matching of synonyms. Then, the analysis of similarities and discrepancies, as well as absent terms for a subset of them, is performed using metrics and expert interpretations. This study identifies several disagreements in standard terminologies that should merit further attention and efforts to promote harmonization amongst the authors/ publishers of these glossaries with the overarching end goal of assisting their readers in learning and understanding the domain of software testing.
A Novel UAV Path Planning Method Based on Layered PER-DDQN

Path planning is a key technology for Unmanned Aerial Vehicles (UAVs) to complete the operational mission in a complex battlefield environment. A step-by-step path planning method based on the Layered Double Deep Q-Network with Prioritized Experience Replay (Layered PER-DDQN) is proposed in this paper. The novel method is constructed by combining the threat avoidance network and collision-free network based on the PER-DDQN framework. By analyzing the current environment of the UAV, the networks output threat avoidance action vector and obstacle avoidance action vector, and the method does a weighted summation of the action vectors according to the weight of the subproblems to obtain the final action. The simulation experiment verifies that the Layered PER-DDQN path planning method has better convergence and practicability than the Deep Q-Network and A* algorithm.