Reviewerly/exHarmony huggingface dataset:
Development of a Virtual Cloud-Based Traffic Rules Learning Simulator Using Spherical Video Streams

The article examines the efficiency of spherical video usage for road traffic rule learning and driving skill acquisition in comparison to conventional driving instruction methods. Additionally, it outlines the creation of a virtual educational simulator designed for road traffic rule comprehension and enhancement of driving abilities. The outcomes of this investigation hold significance for road safety and the design of driver education programs. A novel teaching model employing spherical video streaming for road traffic rule understanding has been devised. The research was provided in the scope of Erasmus+ Jean Monnet «Augmented Reality for Education: implementation of European experience» project.
Approaching Quantum Utility by Leveraging Quantum Software Stack

Improvement in Quantum Computing (QC) performance will allow us to solve a wide range of complex problems that classical computers of today can't handle. Nowadays we feel closer to achieving a state of Quantum Utility (QU) than ever before. Importance for both academia and business to understand the current state of technology and tooling, their extensibility points along with perspective optimization algorithms are on the verge. The purpose of the article is to provide a structured analysis of existing progress in QC. It serves a purpose of a guide on where significant progress is expected in the upcoming years, outlines details about tooling to start doing experiments, and introduces Quantum Computing Optimization Middleware (QCOM) reference architecture as a backbone around which new building blocks are expected to be added in the upcoming years, e.g., industry-specific enterprise connectors. The article reviews only the software stack, not considering opportunities for hardware as they seem to be much further away. This should help scientists and engineers to define a mental model of how to move forward to reach both mid and long-term goals toward QU.
Cloud-based Architecture for Deploying a Distributed Ambient Assisted Living Environment

Ambient Assisted Living (AAL) is starting to become the norm as more and more smart devices and sensors are installed in people’s homes. This is an important aspect in improving the quality of life, especially for the elderly and for people with disabilities.The solution presented in this paper approaches this paradigm in a large scale deployment context, as it proposes an AAL architecture on three layers (Edge, Dew, and Cloud), which is capable of handling high amount of sensor data in near realtime. Using a distributed system of rules engines, the system is able to take actions based on the acquired data. An important novel concept is the “new home deployment”, which facilitates the integration of the devices from a new installation site - the software setup of the new edge infrastructure is done by using a dedicated Cloud service. In order to showcase the practicality and efficiency of the architecture, this paper presents the intricacies of the employed services and service-interaction when deploying the solution on the OpenStack Cloud platform.
Performance Analysis of Database Access: Comparison of Direct Connection, ORM, REST API and GraphQL Approaches

This study aims to estimate the performance of fetching data by different approaches. Effective access to data is critical for high-performance operations, and understanding the advantages and disadvantages of each system is pivotal for software engineers. The experiments conducted in this study indicated that the REST(REpresentational State Transfer) API(Application Programming Interface)[1] and ORM(Object-Relational Mapping) offered excellent performance, whereas GraphQL[2] required additional resources to achieve comparable efficiency. These results can help businesses choose the applicable system for their specific requirements. The study's findings contribute to the growing knowledge of database access optimization, enabling IT professionals to make informed opinions when designing and enforcing effective database access strategies in their systems. The study also sheds light on the implicit trade-offs between the different database access styles and emphasizes the significance of careful consideration of operating conditions when opting for the most applicable system. All approaches were implemented using the Golang programming language[3] and MongoDB database[4].
Automating Web Scraping of User Comments for Sentiment Analysis in Social Networks

Social networks can show the general mood of the population or groups interested in a specific topic. Machine learning will recognize people's moods in posts and automatically classify them according to preferences. Often, the problem is a lack of training data, which can now be solved using ChatGPT. Before use, the training data is preprocessed and verticalized. Web scraping from real social network data can be used as test data to evaluate neural network performance. A decision tree machine learning algorithm is used for data classification. The study's results may be of interest to analysts and page managers on social networks Facebook and Instagram.
Trigger Systems: Reusability and applicability in Serious Games

The integration of technology in education has provided a more interactive and engaging experience through the creation of Serious Games. By using 3D virtual environments and Virtual Reality, interactive experiences which simulate real-life situations and environments can be created. The development process of such applications often proves to be slow and cumbersome. However, the addition of a trigger system or scenario editor could enable educators to easily create scenarios for students without requiring much programming knowledge. Designing the trigger system should be based on the principles of reusability, applicability and extensibility. This paper focuses on the creation of such a trigger system and its uses throughout the development of a 3D VR Serious game called Visit UPT. This game enables students to explore the university campus and simulate small scientific experiments.
Unsupervised Detection of Anomalous Running Patterns Using Cluster Analysis

Anomaly detection is an important problem in various domains, such as user analysis, network intrusion detection, fraud detection and system monitoring. This paper provides a comprehensive review of anomaly detection algorithms for clustering applications. Hence, the paper provides a detailed analysis of various clustering techniques, including distance based, hierarchical, and density based. They also discussed using ensemble techniques and outlier detection methods to improve anomaly detection accuracy on this data set. The authors have applied them to a customer data set to detect anomalies. The authors discussed different cluster-based algorithms and compared their performance based on scalability, precision, recall, and f1 score metrics. Moreover, this work highlights the challenges of cluster-based anomaly detection, such as selecting the appropriate number of clusters, dealing with high-dimensional data, and handling imbalanced datasets. Finally, the authors provided insights into addressing these challenges and discussed future research directions.
Augmentation in a Binary Text Classification Task

- The purpose of this paper is to investigate the effect of different kinds of augmentation on the binary text classification performed by different transformer models. Augmentations were performed in three ways: synonym augmentation, contextual word embeddings, and combined. For classification, BERT, ALBERT, DistilBERT, and RoBERTa transformer models were used. It has been found that when using context word embeddings augmentation every model started to overfit during the second training epoch. In the case of combined contextual word embeddings and synonym augmentations utilization, the overfitting issue was overcome, and models exhibited overall good performance. The best performance, however, was obtained when synonym augmentation was used: the overfitting issue was also avoided, and the models' effectiveness was the highest among all experiments.
Exploring the Potential of OCR Integration for Object Detection in Invoices

This paper investigates the impact of incorporating Optical Character Recognition (OCR) information into object detection models for extracting key information fields from invoices. We propose a method that adds a fourth channel to the input images, representing text presence, derived from two OCR models. Our experiments show that while larger models do not benefit from the additional text localization information, smaller models exhibit significant accuracy improvements and accelerated learning. In particular, we observe a substantially higher mean average precision (mAP) by epoch 20 out of 100 when including the fourth OCR information channel. This research demonstrates the potential benefits of incorporating OCR information into object detection models, particularly for smaller models with limited resources, by enhancing not only accuracy but also the speed of convergence during training.
A Brief Overview on Deep Learning-Based Person Re-Identification

Person re-identification (Re-ID) has attracted considerable attention within the field of biometrics in recent times, driven by an escalating demand in public communities, marketing and banking endeavors, and social media networks. Moreover, the rapid advancement of deep learning has introduced a wide range of effective tools for addressing person re-identification tasks. The result is more precise and faster processing compared to the video surveillance performed manually by human operators. This article provides an overview of recent Re-ID studies by highlighting the challenges and issues, proposing a taxonomy of Re-ID methods, and comparing the performances of the most representative deep learning-based approaches.
Enhancing the Teaching of Natural Sciences in Rural Environments through Educational Videos

Digital tools are revolutionizing primary education, although their implementation in rural settings is still in its infancy. Educational videos are presented as innovative and efficient pedagogical tools that improve teaching-learning, especially to consolidate understanding in still-incipient or underdeveloped areas. Despite teachers’ familiarity with these videos, a gap is perceived in their effective use. In this study, training for teachers of a single-teacher school was conducted and the implementation and impact of digital tools was evaluated, with a particular emphasis on educational videos, in the teaching of natural sciences. An initial survey was conducted to collect data on the use of technology in the classroom and teachers’ perceptions of the usefulness and effectiveness of educational videos. The results showed a performance of 90% in the effectiveness of teaching natural sciences thanks to the use of educational videos and recognition by the authorities. However, the need to improve training in the integration of these tools into pedagogical strategies was identified. This finding underlines the importance of training programs that reinforce the efficient use of digital tools in teaching, which will allow teachers to become more effective facilitators of learning.
A Framework for Anything-as-a-Service on a Cloud platform

There is an increasing need for computational and storage capabilities for complex distributed applications. Existing solutions need to be deployed in an environment that allows for an increase in performance, scalability, and availability. This paper takes looks at the state-of-the-art regarding methods that take existing applications and make them more efficient by using Cloud services. The novelty of the paper consists of a proposed framework for deploying applications on three major Cloud providers (i.e., Amazon’s AWS, Google Cloud and Microsoft Azure) and on the OpenStack open-source Cloud. After the main services from the four Cloud providers are identified, different deployment methods are described depending on the Cloud services and on the requirements of the application. Also, some examples of migrations are discussed with reference to specific Cloud provider services. The proposed solution for Anythingas-a-Service (YaaS) is a straightforward framework for taking different types of applications and migrating them to the Cloud. Therefore, the deployed applications benefit from Cloud features such as resource pooling, availability or scalability, while also being wary of the incurring costs.
Fall Detection with Artificial Intelligence and IoT Health Monitoring System

The elderly population is constantly increasing, facing physical and cognitive limitations that require attention and care. Consequently, caregivers of older adults must maintain constant attention to their physical well-being. Falls are common and can produce serious consequences. The constant monitoring of vital signs is crucial to monitor health status and respond quickly to emergencies. Therefore, the design of a device that accurately detects risk situations in an older adult is a challenge. This paper presents the development of a bracelet-type device with Internet of Things (IoT) architecture for automatic fall detection and vital signs monitoring. Fall detection is performed by a machine learning algorithm trained on data obtained from an accelerometer. The Edge Impulse platform was used where the data processing and the learning block are configured. The trained model was implemented in an ESP32 microcontroller that detects falls with an accuracy of 91%. The device incorporates sensors for monitoring temperature, heart rate, and oxygen saturation that are measured on the wrist of the elderly. A mobile application for the caregiver of the elderly was implemented that issues alerts if the system detects falls, notifies vital sign values if these are outside the normal range, and presents the results in real time.
Semi-disentangled non-negative matrix factorization for rating prediction

Rating predictions have been extensively used in evaluation websites for recommendation tasks in recent past. Due to the single data category and the simple way of feature interaction, traditional rating prediction methods do not portray the actual scoring properly, and lack of interpretation at the same time, which problem is particularly acute in latent factor model. Thanks to the excellent interpretability and generalizability of disentangled representations, in this paper, we propose a Semi-Disentangled Non-negative Matrix Factorization (SDNMF) by splitting user- and item-latent embedding into two blocks with an improved regularizer: the whiten block and the blacked block. Specifically, the whiten block uses external prior knowledge instead of latent factors to improve the internal interpretability of SDNMF, whereas the blacked block consists of those latent factors different from the prior knowledge to promote more feature interaction and preserve algorithm performance, and the regularizer is defined by Kullback–Leibler divergence to characterize the relationship between whiten and black blocks. By disentangling user–item relations to obtain disentangled representation, we want to be able to on this basis, the non-negative matrix factorization for a certain degree of interpretation. Extensive experiments conducted on four real-world data sets (Amazon, Yelp, Yelp10, Dianping) indicate that the proposed SDNMF not only is the state-of-the-art rating prediction method compared with other existing methods, but also preserves the ability to be interpreted.
Exploring how politeness impacts the user experience of chatbots for mental health support

Politeness is important in human-human interaction when asking people to engage in sensitive conversations. If politeness manifests similarly in human-chatbot interaction, it may play an important role in the design of sensitive chatbot interactions such as those for providing mental health support. Our mixed methods study (N = 39) contributes findings on how the use of politeness by chatbots, for the mental healthcare activity of mood logging, is perceived by users. Our study combined a within-participants controlled experiment, whereby participants interacted with three prototype chatbots differing in their use of politeness, with semi-structured interviews. Our analysis demonstrates that a chatbot’s use of politeness can impact how a participant experiences interacting with it, both positively and negatively. While politeness can be experienced as caring, supportive, and encouraging, it can also be experienced as overly apologetic, condescending, and untrustworthy. We discuss the nuances of using politeness in conversational interaction design, setting out a research agenda for polite conversational interaction.
Sentiment analysis of restaurant customer satisfaction during COVID-19 pandemic in Pattaya, Thailand

The tourism and hospitality industry, particularly the restaurant business, has been greatly affected by the COVID-19 pandemic. To comprehend customer behavior and preferences during this unprecedented time, it is crucial to analyze online restaurant customer reviews. Thus, this study utilized the valence aware dictionary for sentiment reasoning (VADER) model to examine TripAdvisor reviews of restaurants in Pattaya City, Chon Buri, Thailand, covering the period 2017-2022, which encompasses both pre-pandemic and pandemic years. The findings reveal a significant decrease in the number of reviews and a notable increase in negative sentiments during the COVID-19 pandemic compared to normal circumstances. We noticed two concern areas, i.e., service and staff, and food and taste, that should be addressed urgently. The findings of this study offer valuable insights into customer behavior and requirements, thereby empowering restaurant businesses to enhance service quality, satisfy customer requirements, and strategically plan for a post-COVID-19 future.
Holistic Implicit Factor Evaluation of Model Extraction Attacks

Model extraction attacks (MEAs) allow adversaries to replicate a surrogate model analogous to the target model's decision pattern. While several attacks and defenses have been studied in-depth, the underlying reasons behind our susceptibility to them often remain unclear. Analyzing these implication influence factors helps to promote secure deep learning (DL) systems, it requires studying extraction attacks in various scenarios to determine the success of different attacks and the hallmarks of DLs. However, understanding, implementing, and evaluating even a single attack requires extremely high technical effort, making it impractical to study the vast number of unique extraction attack scenarios. To this end, we present a first-of-its-kind holistic evaluation of implication factors for MEAs which relies on the attack process abstracted from state-of-the-art MEAs. Specifically, we concentrate on four perspectives. we consider the impact of the task accuracy, model architecture, and robustness of the target model on MEAs, as well as the impact of the model architecture of the surrogate model on MEAs. Our empirical evaluation includes an ablation study over sixteen model architectures and four image datasets. Surprisingly, our study shows that improving the robustness of the target model via adversarial training is more vulnerable to model extraction attacks.
ARODNet: adaptive rain image enhancement object detection network for autonomous driving in adverse weather conditions

The current field of autonomous driving has achieved superior object detection performance in good weather conditions. However, the environment sensing capability of autonomous vehicles is severely affected in rainfall traffic environments. Although deep-learning-based image derain algorithms have made significant progress, integrating them with high-level vision tasks, such as object detection, remains challenging due to the significant differences between the derain and object detection algorithms. Additionally, the accuracy of object detection in real rain traffic environments is significantly reduced due to the domain transfer problem between the training dataset and the actual rain environment. To address this domain-shifting problem, we propose an adaptive rain image enhancement object detection network for autonomous driving in adverse weather conditions (ARODNet). This network architecture consists of an image adaptive enhancement module, an image derain module, and an object detection module. The baseline detection module (CBAM-YOLOv7) is built by incorporating the YOLOv7 object detection network into a feed-forward convolutional neural network, and it includes an attention module (CBAM). We propose a domain adaptive rain image enhancement module, DRIP, for low-quality images acquired under heavy rainfall conditions. DRIP enhances low-quality images on rainy days by adaptively learning multiple preprocessing weights. To remove the effects of rain patterns and fog clouds on image detection, we introduce DRIP-enhanced images into the depth estimation derain module (DeRain) to prevent rain and fog from obscuring the objects to be detected. Finally, the multistage joint training strategy is adopted to improve the training efficiency, and the object detection is performed while the image is derained. The efficacy of the ARODNet network for object detection in rainy weather traffic environments has been demonstrated through numerous quantitative and qualitative studies.
An OpenStack Cloud Solution for a Community Database with Handwritten Characters Used in Developing OCR Algorithms

Most research addressing OCR through machine learning techniques is focused on the actual algorithms and on using the MNIST data set as the de facto benchmark. Little effort was made to extend the data set or to build an entirely new one. Furthermore, support for characters other than English ones is mostly limited. This paper presents an OpenStack based approach that aims to overcome this last limitation by providing a community-oriented solution for developing and maintaining richer, language agnostic, community-shared data sets for OCR based applications. The proposed architecture is integrated with OpenStack services and relies on new Cloud perspectives, such as Function-as-a-Service (FaaS), to achieve a greater degree of flexibility. The included modules allow users to upload their own data sets, select or fine-tune their desired pre-processing methods, and derive the required features for their target character set. Both the input and the output data are stored using OpenStack specific data services and are shared for all the users of the Cloud deployment. An interesting feature is that the underlying FaaS functionality would also allow interested parties to upload their own pre-processing and feature extraction stages.
Reproducibility in Deep Reinforcement Learning with Maximum Entropy

The latest work in the field of deep reinforcement learning speaks highly about the advanced exploration techniques which avoid the greedy decisions of agents. Usually, reinforcement learning works by finding the optimal policy for a Markov Decision Process. In off-policy algorithms the agent learns a value function for this optimal policy, separate of the action choice, an example being the deep Q-learning algorithm. Algorithms based on a maximum entropy framework, like soft Q-learning, overcome the greedy behavior of the agent, effectively combining exploration and exploitation by adding an entropy term to the Bellman equation. This method, applied to the Lunar Lander environment, was compared to the classic deep Q-learning, using the same set of different random seeds and averaging multiple runs. An implicit exploration strategy proves to compensate for disturbances caused by intrinsic sources of non-determinism, such as random seeds. This paper highlights the sensitivity to intrinsic and extrinsic influences for deep reinforcement learning, with respect to exploration and repeatability.