Reviewerly/exHarmony huggingface dataset:
Effects of Chronic Heart Failure on Longitudinal Changes of Cognitive Function in Elderly Patients

Elderly patients with cognitive impairment present problems such as memory loss, impaired judgment, and loss of language function. Severe cases affect daily life and social functions. For the past few years, the possible disease and possible illness of chronic heart failure (CHF) in elderly patients have continued to increase. However, there is not enough research on the effect of CHF on the longitudinal change speed of cognitive function in elderly patients. Most studies focus on the effects of diseases like hypertension, coronary heart disease, and diabetes on cognitive function in elderly patients, which results in incomplete research. In this context, this article used a Bayesian network to build a cognitive function classification model for the elderly. The effects of CHF on the rate of longitudinal changes in cognitive function in elderly patients were studied by mental state examination and statistical methods. The experiment finally concluded by evaluating the attention, language ability, and memory ability of elderly patients. In the elderly with CHF, the incidence of cognitive impairment was about 71.66%. The experimental results further indicated that the higher the degree of CHF in elderly patients, the lower the level of cognitive function. This article could help advance research on preventing or delaying cognitive decline in patients with CHF.
Comparing fuzzy cognitive maps: Methods and their applications in team communication

As fuzzy cognitive mapping becomes more ubiquitous and transferable between fields, there is a growing necessity to be able to compare its results. However, despite the abundance of measures for this purpose, the act of comparing these maps is not widespread. This paper gathers different analysis measures and demonstrates how they can be used for fuzzy cognitive map (FCM) comparison. For this, various measures are described and applied to communication FCMs, specifically when differentiating team performance levels. This paper shows how existing measures can be applied and interpreted, and identifies gaps in the current methods of FCM comparison. Relevance to Industry: This research can aid the communication, human systems engineering, and computer science communities in identifying appropriate measures for analysis and comparison of their FCMs. Our results indicate the need for transdisciplinary work on the application and comparison of FCMs.
Computational Complexity and Analysis of Supervised Machine Learning Algorithms

Data is generated at a much faster pace, and it is increasing exponentially day by day. Machine learning methods are being used to extract patterns and trends from data to streamline different business activities for more profit with fewer resources. Machine learning models need to be trained with lots of data before being deployed for predictive analysis (Lecture notes in Computer Science, 2012 [1]). Training time depends upon the complexity of an algorithm. We are analyzing the space and time complexity of various machine learning algorithms so that it becomes easier to select and deploy the most efficient and appropriate model for a particular dataset. This research work primarily focuses on data analytics for supervised machine learning algorithms in industrial research domains.
A Widespread Assessment and Open Issues on Image Captioning Models

Automated generation of image captions is a demanding AI crisis as it necessitates the exploitation of numerous methods from diverse computer science fields. Deep learning (DL) approaches have revealed marvelous results in a lot of diverse appliances. On the other hand, data augmentation in DL that imitates the quantity and the variety of training data without the need of gathering additional data is a hopeful area in machine learning (ML). Producing textual descriptions for a specified image is a demanding task using the computer. This survey makes a critical analysis of about 65 papers regarding image captioning. More particularly, varied performance measures that are contributed in diverse articles are analyzed. In addition, a comprehensive study is made regarding the maximal performances and varied features deployed in each work. Moreover, chronological analysis and dataset analysis are done and finally, the survey extends with the determination of varied research challenges, which might be productive for the analysts to endorse enhanced upcoming works on image captioning.
Code samples summarization for knowledge exchange in developer community

Abstract A question title's function is to generate readable titles and describe a problem encountered by the code. Previous studies often used an end‐to‐end sequence‐to‐sequence system to generate question title's from source code. However, long‐term dependencies are often difficult to capture, and this may result in an incomplete source code representation. To address this issue, we propose a Transformer for Generating Code Title (hereinafter referred to as TGCT) model. Specifically, the TGCT model uses the position coding mechanism to model paired relationships between source terms by applying relative position representations. Multiple self‐attention mechanism components are also used to capture long‐term dependencies of the code. Comprehensive experiments on datasets from five coding languages, namely Python, Java, JavaScript, C#, and SQL, are conducted, and the results show that TGCT outperforms state‐of‐the‐art models based on the measurements of BLEU and ROUGE in general. In addition, a cross‐sectional comparison experiment was conducted to verify the effects of different model parameters, different data set sizes, position coding mechanism, and self‐attention mechanism on model results.
Electricity Usage Settlement System Based on a Cryptocurrency Instrument

This article reviews the issue of the use of cryptocurrencies (crypto-assets, in general) for an electricity settlement system. The development of digital techniques, including blockchain-based mechanisms, has meant that an increased interest in blockchain-based solutions is to be expected. Blockchain and similar approaches are characterised by decentralisation, so they are concurrent with the trends of the transforming power sector. Decentralised energy generation based on a high proportion of prosumer installations requires the implementation of a new settlement system for grid activities related to electricity use. The first projects of such systems based on a dedicated cryptocurrency have emerged. Based on these, the general concept of such a system with its own cryptocurrency called CCE is presented, including variants implementing net-metering and net-billing. Furthermore, issues requiring interdisciplinary research work and discussion before implementing such systems were identified. A settlement system in which a cryptocurrency is linked to a unit of energy used could be a first step towards introducing a new universal means of value exchange, linked to energy as the primary measure of the value of goods.
A Point-Cloud Solar Radiation Tool

Current software solutions for solar-radiation modeling in 3D focus on the urban environment. Most of the published tools do not implement methods to consider complex objects, such as urban greenery in their models or they expect a rather complex 3D mesh to represent such objects. Their use in an environment that is difficult to represent geometrically, such as vegetation-covered areas, is rather limited. In this paper, we present a newly developed solar-radiation tool focused on solar-radiation modeling in areas with complex objects, such as vegetation. The tool uses voxel representations of space based on point-cloud data to calculate the illumination and ESRA solar-radiation model to estimate the direct, diffuse, and global irradiation in a specified time range. We demonstrate the capabilities of this tool on a forested mountain area of Suchá valley in the Hight Tatra mountains (Slovakia) and also in the urban environment of Castle Hill in Bratislava (Slovakia) with urban greenery. We compare the tool with the r.sun module of GRASS GIS and the Area Solar Radiation tool of ArcGIS using point-cloud data generated from the digital-terrain model of Kamenistá valley in High Tatra mountains in Slovakia. The results suggest a higher detail of the model in rugged terrain and comparable results on smooth surfaces when considering its purpose as a 3D modeling tool. The performance is tested using different hardware and input data. The processing times are less than 8 min, and 8 GB of memory is used with 4 to 16 core processors and point clouds larger than 100,000 points. The tool is, therefore, easily usable on common computers.
Sarcasm Detection in Tunisian Social Media Comments: Case of COVID-19

The goal of this study is to learn more about how the public sees COVID-19 pandemic behaviors and to identify important themes of concern expressed by Tunisian social media users during the epidemic. Around 23K comments were collected, written in both Arabic and Latin characters in the Tunisian dialect. Native language experts manually tagged these comments for sarcasm identification (sarcastic and non-sarcastic). In addition to health, our dataset contains comments on entertainment, social, sports, religion, and politics, all of which are impacted by COVID-19. This research examines the sarcasm expressed in Tunisian social media comments regarding the novel COVID-19 from its appearance in the first half of 2020. We also provide benchmarking findings applying machine learning and deep learning algorithms for sarcasm detection. We obtained an accuracy of above 80%.
Travelling Route Recommendation Method Based on Graph Neural Network for Improving Travel Experience

With the rapid development of Internet technology, people can learn all kinds of travel information anytime and anywhere. However, the serious information overload causes travelers to be unable to make accurate and reasonable travel routes that meet tourists’ tastes for a while, thus reducing the quality of travel. The recommendation system as the mainstream solution to the information explosion of two means has received the attention of the majority of scholars and industry. Based on the research theory of tourist route recommendation, this paper analyzes the characteristics of attractions, factors affecting travelers’ travel experience when touring attractions and factors affecting travelers’ travel experience along tourist routes. Furthermore, we propose a tourist route recommendation model that meets tourists’ preferences. Then, this paper uses the graph neural network (GNN) algorithm to build a framework for tourist route recommendations based on the GNN using the relationship of preference and commonality existing among groups, tourists and attractions. The GNN algorithm is optimized and improved using multiple graphs and an attention mechanism. Finally, the effectiveness of this paper’s algorithm is verified by conducting experiments on different data sets.
Automated detection and classification of counterfeit banknotes using quantitative features captured by spectral-domain optical coherence tomography

Counterfeiting of banknotes is still a severe crime problem in many countries. One of the most significant issue for solving the crime is to classify the counterfeit types and identify the sources. Most of the current methods to classify counterfeit banknotes rely on manual examination that is time-consuming and labor-intensive. Moreover, these methods only detect surface features which can be easily imitated through advanced printing technology. In this study, an automated method based on optical coherence tomography (OCT) and machine-learning algorithms was proposed to classify different types of banknotes based on the internal features. A spectral-domain OCT (SD-OCT) system was employed for sub-surface imaging and quantitative assessment of banknotes. A total of 29 Chinese 100-Yuan banknotes were collected, in which 4 of them were real and 25 of them were counterfeiting by three different printing processes. Each banknote was imaged 10 times in 3 distinct regions, which resulted in a dataset of 290 samples. Each sample was characterized by extracting 2 A-scan (OCT signal intensity along depth) based features and 14B-scan (cross-sectional OCT images) based features. Several machine-learning models, including logistic regression (LR), support vector machines (SVM), K-nearest neighbor (KNN) and random forest (RF), were built and optimized as the classifiers that were trained using 203 samples and applied to predict 87 testing samples. The best performance was achieved by SVM classifier in which the sensitivity of 96.55% and specificity of 98.85% were obtained in discriminating between authentic and counterfeit banknotes, and the sensitivity of 94.67% and specificity of 98.22% were obtained in predicting the types of counterfeit banknotes. These classifiers were also evaluated using the receiver operating characteristic (ROC) curves. To the best of our knowledge, this is the first study where A-scan and B-scan derived features from OCT images have been used for the detection and classification of different types of counterfeit banknotes.
Introducing Causal Inference Using Bayesian Networks and <i>do-</i>Calculus

We present an instructional approach to teaching causal inference using Bayesian networks and do-Calculus, which requires less prerequisite knowledge of statistics than existing approaches and can be consistently implemented in beginner to advanced levels courses. Moreover, this approach aims to address the central question in causal inference with an emphasis on probabilistic reasoning and causal assumption. It also reveals the relevance and distinction between causal and statistical inference. Using a freeware tool, we demonstrate our approach with five examples that instructors can use to introduce students at different levels to the conception of causality, motivate them to learn more concepts for causal inference, and demonstrate practical applications of causal inference. We also provide detailed suggestions on using the five examples in the classroom.
Summarization of Videos from Online Events Based on Multimodal Emotion Recognition

In this paper, we propose a novel video summarization technique for automatic affect analysis of participants of an online event. At first, face verification neural network is used to cluster facial regions that correspond to each participant. Next, emotional features are extracted from each face by using EfficientNet model obtained in the previous paper of the author. The features of several consecutive frames are combined into a single descriptor that is used to classify emotions. In addition, audio features are extracted using wav2vec, and an ensemble of audio and video classifiers predicts emotions for each face. Finally, dependence of these emotions on time is visualized in special color charts. In the experimental study with the AFEW dataset it was demonstrated that the proposed approach makes it possible to obtain the best-known validation accuracy 67.88%. The models were optimized using OpenVINO and gained reasonable performance even if Nvidia GPUs are unavailable. The models and source code are publicly available at https://github.com/amirabdrahimov/multimodal-emotion-recognition.
JMNET: Arbitrary-shaped scene text detection using multi-space perception

The arbitrary-shape scene text detection faces enormous challenges in accuracy and speed with increasing demand in the industrial sector. Although most studies have introduced segmentation and achieved remarkable performance in text detection tasks, researchers ignored the importance of detection efficiency. This paper proposes a new Joint Multi-Space Perception Network (JMNET) for efficient scene text detection to address this issue. Based on a lightweight feature extraction backbone, we put forward two novel modules, i.e., Scale Spatial Perception Module (SSPM) and Attention Spatial Perception Module (ASPM) to enhance the expression ability of text features with low computational complexity. Moreover, we propose an Unsupervised Embedding Spatial Perception Loss function (UnESP Loss) by introducing the Euclidean distance measurement between the embeddings to overcome the ambiguity of text instance boundary, such as a small line spacing. In this way, text embedding learning is not restricted by specific shapes, and detection robustness can be improved. Extensive experiments on four benchmarks, including the ICDAR2015, MSRA-TD500, CTW1500 and Totaltext, show that the proposed JMNET achieves competitive performance in terms of both accuracy and speed over the state-of-the-art methods. Particularly, our method can reach a remarkable F-measure of 85.5% at 52 FPS on Totaltext. Code is available at:https://github.com/sakura-910/JMNET.
Floating photovoltaic technology definition aided with multi-criteria decision analysis: a case study

The demand for electrical energy is constantly growing. Power generation from renewable sources will play a role in meeting the electricity demand. Floating photovoltaics is a solution that still needs to be explored in the design and construction phases. The floating system has important characteristics: it reduces evaporation of water and algae growth in the basins, it does not occupy land and has a high efficiency due to the evaporative cooling effect. Multi-criteria decision analysis is used to determine the best technology, between fixed or tracked structure, on a system in Sicily. Analysis provided results regarding the superiority of horizontal axis tracking systems; the Levelized Cost Of Electricity is 64.37 USD/MWh and the avoided CO2 emissions are 23,491.60 tonnes.
Creating Futuristic Heritage Experiences: An Exploratory Co-Design Study through Design Fiction

This study adopted design fiction and co-design as the two main methodological approaches for designers and artists to explore the complex relationships and intersections of futuristic heritage experience and engagement. A literature-based seminar and a co-design workshop were conducted with 26 participants from multidisciplinary backgrounds, via Miro Board. A series of collaborative design practices and science fiction films were utilised as key media for propelling thinking, raising awareness or questions, provoking actions, inaugurating discussions, and offering alternatives necessary for a heritage setting. Additionally, this study reflects upon the complexities of using design fiction activities to re-think the technological future of heritage engagement and experience.
HealMA: a model-driven framework for automatic generation of IoT-based Android health monitoring applications

The development of IoT-based Android health monitoring mobile applications (apps) using traditional software development methods is a challenging task. Developers need to be familiar with various programming languages to manage the heterogeneity of hardware and software systems and to support different communication technologies. To address these problems, in this paper, we first analyze the domain of health monitoring mobile applications and then propose a framework based on model-driven engineering that accelerates the development of such systems. The proposed framework, called HealMA, includes a domain-specific modeling language, a graphical modeling editor, several validation rules, and a set of model-to-code transformations, all packed as an Eclipse plugin. We evaluated the framework to assess its applicability in generating various mobile health applications, as well as its impact on software productivity. To this end, four different health monitoring applications have been automatically generated. Then, we evaluated the productivity of software developers by comparing the time and effort it takes to use HealMA compared to a code-centric process. As part of the evaluation, we also evaluated the usability of HealMA-generated apps by conducting a user study. The results show that HealMA is both applicable and beneficial for automatic generation of usable IoT-based Android health monitoring apps.
QSLT: A Quantum-Based Lightweight Transmission Mechanism against Eavesdropping for IoT Networks

Quantum Key Distribution (QKD) is a promising paradigm for Internet of Things (IoT) networks against eavesdropping attacks. However, classical quantum-based mechanisms are overweight and expensive for resource-constrained IoT devices. That is, the devices need to frequently exchange with the QKD controller via an out-band quantum channel. In this paper, we propose a novel Quantum-based Secure and Lightweight Transmission (QSLT) mechanism to ease the overweight pain for IoT devices against eavesdropping. Particularly, the mechanism predistributes quantum keys into IoT devices with SIM cards. Using one of the keys, QSLT encrypts or decrypts IoT sensitive data. It is noting that an in-band key-selection method is used to negotiate the session key between two different devices. For example, on one IoT device, the in-band method inserts a key-selection field at the end of the encrypted data to indicate the key’s sequence number. After another device receives the data, QSLT extracts the key-selection field and decrypts the data with the selected quantum key stored locally. We implement the proposed mechanism and evaluate its security and transmission performances. Experimental results show that QSLT can transmit IoT data with a lower delay while guaranteeing the security performance. Besides, QSLT also decreases power usage by approximately 58.77% compared with state of the art mechanisms.
The Perception Scale for the 7E Model-Based Augmented Reality Enriched Computer Course (7EMAGBAÖ): Validity and Reliability Study

Education, teaching, and learning topics, known to have gained an international dimension with technological developments, are still seen as the most discussed themes and subject to change. It is clear that in the 21st century, the increasing information density, the means of transfer, and the technological adaptation skills of the teacher and the learner are at the forefront, and more efforts are required to develop them. The integration of technologies in education and training is related to the necessity of preparing learners in the most suitable way for future work and lifelong learning within the information society. For this reason, for the COVID-19 pandemic process and thereafter, starting with known education models makes it necessary to enable the development of education, teaching, and learning under better conditions and situations by blending them with technological developments. Everyone has understood the ever-changing and developing universal digital world much better during this pandemic. The 7E model of the Constructivist Learning Theory (CLT), known as the student-centered model based on distance education, has been mandatory for the entire education community during the first global pandemic of the digital age. Augmented Reality (AR) is another web-based technological development that can work in harmony with the 7E model. In the 7E model, the teaching of the lessons was at the forefront since the learners learn by doing, experiencing, and applying, directly participating in the lesson, and sharing opinions. For the present study, a scale was developed to determine the perceptions of the learners about the 7E model-based AR-enriched computer lesson. Validity and reliability studies were also conducted on the data obtained from the developed scale. The scale, which was prepared using a five-point Likert scale, was applied to 400 students who fit the profile of the sample group. A statistical analysis of the results concluded that 26 low-factor loading items should be removed from the questionnaire, and the final version of the 28-item scale was a six-factor structure. The statistical analysis concluded that the scale was suitable for all criteria in terms of validity and reliability. Considering the values revealed in the study, it was concluded that the overall scale (α = 0.932) was highly reliable.