Reviewerly/exHarmony huggingface dataset:
AI/ML Based new smart customer feedback system for hospitality Industry

Since the last many years customer satisfaction play a significant role to improve the hospitality industry. In this article we introduce a proposed technology based practical and theoretical framework for customer feedback management system to improve the hospitality services and to enhance the customer business era. Technology such as Artificial intelligence and machine learning has important role to develop scalable smart feedback system. AI solution support the responding process for online customer reviews and assist the using automatically collected data. Sometime this is difficult to interpret the collected data form the customer, so we apply machine learning to fetch the corresponding data. Our solution contributes the existing customer feedback application to provide the smart feedback system to improve smart and open platform the customer and hospitality industry.
ME-BERT: Multi-exit BERT by use of Adapter

Multi-exit BERT is the backbone architecture for many inference speedup methods. However, its training procedure is not well studied. In this work, We propose a novel framework, Multi-exit BERT (ME-BERT), for improving the training procedure of multi exit BERT. First, through analysis of the two-stage training (2ST) procedure [1], we propose the Enhanced-2ST, which consists of two generic yet effective modifications to 2ST. Second, to further uncover the representation capabilities of the shallow layers in the second stage of Enhanced-2ST, we apply the multi-exit adapter to finetune the backbone layer-by-layer and provide more suitable representations to the intermediate exits. Extensive experiments are conducted on the GLUE benchmark, which shows that our ME-BERT can significantly outperform the state-of-the-art (SOTA) mssulti-exit BERT training methods.
DYNAMICAL DESIGN OF PRINTED PRODUCTS: THE METHODS OF ARTISTIC IMAGE TRANSFORMATION

In the study we presented the complex analysis of theoretical and practical aspects of the print design as the innovative phenomenon in the design culture. We proved that the form and image transformation in the printed industry contributes to the activation of consumers’ attention and the increase of the informational message perception quality. We analyzed the pop-up phenomenon in the historical context and described the features of different paperfolding techniques that provide visual dynamics. On the base of the printing samples analysis we formed the classification of dynamical visual systems and considered the main methods of the image transformation in the terms of printing production. The results of the theoretical study were implemented in the series of the dynamical leaflets on social issues.
Blockchain-based vaccination certificates management

With the explosion of COVID-19 cases and the government’s needs to control virus spreading, the development of effective and robust systems for managing vaccination certificates to restrict citizens’ activities has been in the centre of many governments. This paper proposes a system that allows for the update of the status of certificates and bases its function on a specific form of logs stored on Blockchains and a set of rules for the interpretation of these logs. Also an outline of a proof of concept implementation of the system in Ethereum together with a cost and security analysis are provided in the paper. The proposed architecture provides several benefits with the most prominent one being the suspension of certificates in case an already vaccinated individual is found positive. In existing certificate management systems a vaccinated individual that is tested positive still holds a valid vaccination certificate during the self-isolation period. This vulnerability allows infected individuals to commute freely and thus facilitates the spread of the pandemic. The proposed solution is not limited to COVID-19 related certificates, but rather it could be deployed in any kind of digital certificate.
Research of Implementation Methods for Quantum Machine Learning and Their Development on QPU

Over the past few decades, there has been a significant breakthrough in the field of quantum computing. Research is of growing interest, which has recently led to the development of quantum information systems prototypes and methods for their development. The paper describes the possibilities, advantages and disadvantages of quantum neural networks. The analysis of throughput, learning complexity and comparison of a classical feed-forward neural network and a quantum neural network with two different feature maps is carried out.
Developing a Domain-Specific Lexicon for the Greek Language

We live in a society where a massive quantity of data is generated daily on online social network platforms. This enormous data contains vital opinion-related information that many companies and other scientific and commercial industries are trying to exploit for their benefits. For that purpose, sentiment analysis is required. Sentiment analysis or opinion mining is the branch of data analytics for extracting sentiments from messages expressed by users on a particular subject. Although, in the past years a considerable research has been made for the English language, the works of Sentiment Analysis in Greek language is not so popular, due to smaller user base. In this work, we provide a method to create domain-specific dictionaries given a corpus of tweets in the Greek language. In those Lexicons, we take into consideration the significance of each word for the specific domain, by introducing a new attribute Weightw. Also, we deploy a hybrid framework which utilizes the newly created domain-specific Lexicon with the Naïve Bayes classifier to analyze and predict the sentiment of each tweet. Our framework has the ability to merge the better of the two basic concepts, the Lexicon and Machine Learning method, and demonstrates the significance of the words for domain-specific Lexicon, for achieving optimal results when performing Sentiment Analysis.
Self-Adaptive Privacy in Cloud Computing: An overview under an interdisciplinary spectrum

The rapid development of cloud computing environments has resulted in various advances and personalized services for users, raising thus several privacy issues. Towards this, research focused on privacy safeguard in the cloud, indicating solutions on the area of self-adaptive privacy. A detailed review is produced to bring forward the carried out work and to analyze it in terms of privacy interdisciplinary standards. In this regard, our work presents the existing self-adaptive privacy approaches and identifies the context for which they have been developed. Moreover, a corresponding classification scheme is provided. The findings give also insights on the proposed tools, which were critically analyzed. This review aims at indicating the developments and limitations of the area, providing potentials of future work in less discussed aspects of the self-adaptive privacy in cloud under an interdisciplinary point of view.
Towards an Explainable AI-based Tool to Predict the Presence of Obstructive Coronary Artery Disease

Obstructive coronary artery disease (CAD) is characterized as significant upon detection of stenosis of coronary artery diameter. In this paper, we adapt Artificial Intelligence (AI)-based predictive models to accurately estimate the pretest likelihood of obstructive CAD on coronary computed tomography angiography (CCTA) in patients with suspected CAD. In doing so, we use patients’ objective results and variables extracted from the screening procedure in combination with demographics, medical history, social history, and other medical data. We use a dataset consisting of 77 patients and we apply a number of alternative Machine Learning (ML) algorithms to predict coronary artery stenosis severity . The ensemble voting model showed the best results across all performance metrics with an area under curve (AUC) of approximately 0.88. We also attempt to provide the clinicians with an explanation of the prediction as to make it more trustworthy.
Confronting and intrusion detection techniques of cyber-attacks in wired and wireless communication networks

This publication aims to study the attacks that telecommunications networks face every day. Thus, the tools that have been developed through new technologies are presented in detail in order to successfully deal with these attacks. Some of these tools are more effective and others less effective depending on the form of attack. As a conclusion is that very significant steps have been taken in confronting with attacks by intruders by introducing a new and wide field of study in the networks.
A Face Recognition System for Bears

Face recognition, in the sense of identifying people, is controversial from a legal, social, and ethical perspective. In particular, opposition has been expressed to its use in public spaces for mass surveillance purposes. Face recognition in animals, by contrast, seems to be uncontroversial from a social and ethical point of view and could even have potential for animal welfare and protection. This paper explores how face recognition for bears (understood here as brown bears) in the Alps could be implemented within a system that would help animals as well as humans. It sets out the advantages and disadvantages of wildlife cameras, ground robots, and camera drones that would be linked to artificial intelligence. Based on this, the authors make a proposal for deployment. They favour a three-stage plan that first deploys fixed cameras and then incorporates camera drones and ground robots. These are all connected to a control centre that assesses images and developments and intervenes as needed. The paper then discusses social and ethical, technical and scientific, and economic and structural perspectives. In conclusion, it considers what could happen in the future in this context.
Using TOSCA language to model personalized educational content: Introducing eduTOSCA

Students attending Higher Education Institutions (HEIs) of Vocational Educational and Training (VET) are faced with a variety of complex decisions and procedures. To provide students with more sustained and personalized advising, many HEIs/VETs use academic advising systems and tools as a way to minimize costs and streamline their advising services. Furthermore, it is quite common for educational programs to include and combine educational content from different educational providers, while they are managed and executed on different platforms. Therefore, the ability to develop conceptual models for personalized learning based on educational content produced by heterogeneous educational service providers is a pressing need to address. A similar issue is confronted when deploying applications across diverse cloud computing platforms. A solution that is provided in these situations is the development of specialized languages for defining the topology and the orchestration of applications such as TOSCA, CAMP, Open-CSA, etc. In this paper, we propose to use similar conceptual models for modelling heterogeneous educational offerings toward personalized learning, which are presented along with the overall architecture of a system, named cc-coach, able to support these concepts. Further, this paper is a proposal for the standardization efforts needed for creating a multi-vendor educational ecosystem with diverse stakeholders, able to support personalized learning at various levels.
SparkReact: A Novel and User-friendly Graphical Interface for the Apache Spark MLlib Library

Visualization is a critical component across every software as it enables users to familiarize themselves with the environment and perform certain tasks with ease. Therefore, straightforward yet interactive and easy-to-understand tools let users’ complex demands be satisfied within minutes. The objective of this work is to give an optimized graphical user interface for the Apache Spark MLlib library to apply machine learning algorithms quickly, conveniently, and effectively. We introduce SparkReact, a responsive graphical user interface that allows users to apply clustering, classification, and regression techniques within just a few mouse clicks by implementing and evaluating a certain algorithm and pre-building the code ready for import to Spark. To evaluate the usefulness of our tool we performed crowdsourcing to two categories, computer experts and ordinary users. The results indicate that both populations were satisfied with the tool at a surprising 98 percent. As per the time required to construct and evaluate a machine learning model, it took approximately 4 minutes using SparkReact while with ordinary methods it took almost 4 times longer. Ultimately, future extensions will seek to provide more algorithmic choices.
Flat vs Skeuomorphic Design for Smart Home Devices: An Exploratory Eye-Tracking Study

Creating a well-integrated IoT management system requires a usable user interface. Usability, which is the outcome of interface design and is affected by the experience provided when using the system, is a critical component that influences how successful an interface is and how well users accept a system. With IoT devices spreading around us at an enormous pace in the last years, a growing research interest concerns how to design efficient interaction between users and smart devices. The experimental process in this study includes the pilot design of two interface variations of a smart home dashboard, a flat and a skeuomorphic design, with the purpose of examining which is better in terms of performance and aesthetics. The results indicate that participants performed better in the flat design environment as they managed to execute the assigned tasks easier and faster based on a set of metrics that comprised time to complete the task, as well as eye-tracking metrics (Time to First Fixation, Total Fixation Duration, Total Visit Duration, Visit Count, and Time to First Click). Moreover, users claimed that icons and controls in the skeuomorphic design took more time to recognize and use, an observation confirmed by recorded eye-tracking data. Overall, flat design is preferable in terms of user performance while skeuomorphism is preferable in terms of aesthetics as users consider it more visually appealing.
Innovative Cultural Experience (ICE), an Augmented Reality system for promoting cultural heritage

Innovative Cultural Experience (ICE) is an Augmented Reality (AR) system for promoting cultural heritage. ICE combines cutting-edge technologies such as an interactive transparent screen, AR, motion sensors and multimedia material in order to provide a unique personal or mass-touring experience, utilizing information based on material and intangible cultural heritage, through narrative scenarios. Part of the ICE system is an interactive transparent box in which an exhibit can be placed. When a user/visitor approaches the exhibit, multimedia information is displayed on the transparent screen of the box, creating an interactive AR experience for the user. Users can interact with the content which can be text, images, videos, 360 images, 360 videos, 3D models or even play games based on the exhibit that is in front of them. By combining the real exhibit with digital information displayed on top, an interactive AR experience is created. Additionally, users can provide feedback by recording and uploading text, images, and videos to the ICE system. ICE is cognitively neutral (domain independent) technology, which makes it useful for a variety of thematic items (from museum exhibits to folk customs, local recipes, etc.) and it can be used also in education, commercial and in the tourist sectors. This paper presents the architecture of the ICE system, and the technologies used for building it. Initial internal evaluation results show that the system is easy to use, and users tend to stay longer in front of the exhibit, interacting with it, thus collecting more information about it.
OPTORER PPE

OPTORER is a newly funded project targeting to enhance and support cultural activities within the region of Attica. OPTORER introduces a novel routing and exploration service in outdoor and indoor areas of touristic and cultural interest in the broad area of Attica. It offers a series of innovative routings across and towards areas of interest, targeting users experience optimization and promotion of underlying purposes. Safety and social wellbeing are prioritized for every tour. Making use of the smartphone device most end users possess, as well as wearable devices such as activity trackers in the form of bracelets/smartwatches that lately have gained broad adoption for self-monitoring activity and wellbeing, the project offers a complete solution developing the algorithms and the end user applications along with an orchestration platform responsible for the administration, operation and execution of the service producing and presenting to the end user results coming from the solution of complex optimization problems.
Incorporating Belief Merging into Relevance-Sensitive Belief Structures

Belief structures have been introduced by Chopra and Parikh as a well-behaved and relevance-sensitive model that is perfectly suitable for representing the beliefs of resource-bounded agents. In this paper, we point out that belief structures are compatible with all the rationality-postulates for belief revision, proposed by Alchourrón, Gärdenfors and Makinson, combined with a popular relevance-sensitive principle, proposed independently by Parikh. This conclusion was unknown, since Chopra and Parikh considered in their original work only a portion of the rationality-postulates for belief revision. Furthermore, we incorporate the well-studied process of belief merging into the belief-structures model, and show how this approach rationally eliminates undesired inconsistencies arising when reasoning with or revising belief structures, and leads to an increased coherence of such structures.
The Integration of Artificial Intelligence Technology and Digital Media Art

Given the progress of artificial intelligence technology, computer has been more popular. More, the digital media art and artificial intelligence technology have fused well to help modern cultures blossom with greater charm. So, the core of blending between AI techniques and digital media art will be discussed with the basis of the era of AI. More, current state of development of digital media art and technology will be analysed and finally innovative directions and future trends will be proposed.
Research on Computer Information Security Defense Methods Based on PCA and BP Neural Network Underneath the Backdrop of Large Set of Information

Based on the characteristics of high dimension and huge data of abnormal data in the background of big data, traditional detection methods aim at the phenomenon that the detection efficiency of abnormal data is not high and the identification progression of unknown atypical data is negligible. In this paper, data safety detection technique comprising of PCA and the proposal for the BP neural system is presented, which is suitable for big data environment. By training and learning the known abnormal data, this method can effectively identify the set abnormal data, and can also identify the abnormal data without training and learning. This PCA based BP neural network abnormal data recognition method is applied to information security defense in the backdrop of large set of information, which has the advantage of proactive defense.
Application of Data Mining in the Design of English TRAnslator's Speech Recognition System

With the further improvement of basic equipment, more and more data can be discovered and used. With the continuous improvement of computer computing power and the perfection of machine learning and other theories, speech recognition technology gradually tends to be practical. Research on the application of data mining in speech recognition and constructing an English translator speech recognition system has practical significance in our country. Therefore, this article mainly studies the application of data mining in the design of English translator speech recognition system. This paper analyzes the speech data of data mining technology, speech recognition technology and machine translation technology, and proposes a speech error correction algorithm based on the Jaro-winkler algorithm, which improves the accuracy of speech recognition. This paper designs a platform development plan for an English translator speech recognition system, and tests the translation performance of the system. The results show that the overall recognition rate of the system is 89.45%, and the translation accuracy rate is 91.64%. This shows that the accuracy of the system is high, and the input sentence can be translated more accurately, which meets the requirements of the English speech translation system in specific situations.
LDM: A Location Detection Method of Emotional Adversarial Samples Attack for Emotion-Cause Pair Extraction

ECPE as the task of extracting emotion-cause pairs provides technical support for some fields, such as public opinion analysis. The existing ECPE methods are attacked by emotional adversarial samples, the precision of extracted EC pairs will decrease. The current adversarial defense methods cannot accurately detect the attacked location that affects the extraction results. To address this problem, this paper proposes LDM (A Location Detection Method of Emotional Adversarial Samples Attack for Emotion-Cause Pair Extraction), to location attack. Firstly, according to the results of ECPE dataset extraction, the Emotion-Cause scores are calculated. The EC domain (Emotional Cause Domain) is constructed by the upper and lower limits of the Emotion-Cause scores. Then, EC pairs are extracted from ECPE data attacked by emotional adversarial samples, and their Emotion-Cause scores are calculated. The Emotion-Cause scores distribution in the EC domain is analyzed to obtain the outlier data information. According to the location of the outlier data, capture the attack location. Finally, remove outlier data, and the ECPE secondary extraction is carried out to obtain the final extraction results. The experimental results show that LDM can accurately capture the attack location and help the model prominent improve the extraction precision.
AS-GSI: Aspect-Level Sentiment Analysis Integrating Global Semantic Information

The purpose of aspect-level sentiment analysis is to predict the sentiment polarity of a given aspect in context. At present, most of the existing models use the attention mechanism to capture sentiment information with a given aspect. That did not fully consider the global semantic information, resulting in low accuracy of sentiment polarity recognition. To address this problem, this paper proposes an aspect-level sentiment analysis model that integrates global semantic information(AS-GSI). Firstly, AS-GSI captures the semantic information of context by using BiLSTM. The self-attention mechanism and aspect-based attention mechanism are introduced into AS-GSI, which to obtain global semantic information and sentiment information. Then, a simple and effective fusion mechanism is designed to fuse the two pieces of information fully. Finally, the fused information is input into the softmax-based aspect level sentiment analyzer to calculate the sentiment score. The validity of AS-GSI is verified on the Restaurant14, Restaurant15, and Restaurant16 of SemEval. Experimental results show that the accuracy of AS-GSI can reach 78.27%, 78.14%, and 80.33% on three data sets.