Reviewerly/exHarmony huggingface dataset:
Recommendation Systems on Twitter Data for Marketing Purposes using Content-Based Filtering

With artificial intelligence and machine learning enhancement over time, many concepts in commerce and marketing have changed. Today, many people are using online shopping options since online platforms provide a wide variety of products. Vendors also try to attract customers and offer them user-specific products on online shopping platforms using many technologies. One of these technologies is Recommendation Systems (RS) which makes information filtering on batch data. RS makes shopping very easy for both customers and vendors. While it suggests products they may like or prefer for customers, it also provides a target customer list for vendors. RS can also be applied to many social media platforms for marketing purposes. In this paper, we propose an RS analyzing Twitter data for vendors. In our RS, vendors search some keywords to extract target Twitter users as customers, and then it lists the corresponding Twitter users using the Content-Based Filtering technique. Our systems succeeded to recommend the target customer list with 86. 24 % accuracy.
Investigation of Local and Global Methods of Explainable Artificial Intelligence. Metrics for Evaluating Explanation Methods

Artificial intelligence systems in meaning of machine learning face not only technical (collecting enough data, efficient hardware implementation), but also social difficulties in implementation. The reason for this is distrust due to a lack of understanding by end users how such systems work and why they make certain decisions. Therefore, this paper discusses the methods of explainable artificial intelligence aimed at solving the problem of misunderstanding, also considers existing approaches to the evaluation of these methods and determines the requirements for the evaluation of AI system and the metrics that can be used in it. It will also answer the question that runs through this work – what is the role of hybrid intelligence in the tasks of explaining models?
Multi-scale depth classification network for monocular depth estimation

In addition to the RGB information of an image, depth information is the most critical. Monocular depth estimation is an effective method for predicting depth from RGB images. First, we propose a multiscale classification network that transforms the predicted depth values into a nonlinear combination problem between multiple depth interval values. Based on the correlation of the depth information, a depth classification network was used to determine the results of the weight values of each interval. Second, the depth maps predicted by feature maps with different resolutions contain different critical information. The lower-resolution depth maps play an overall role in predicting the overall contour; the higher-resolution depth map is more accurate in predicting object edge details. Finally, the desired depth map can be obtained by a 3 × 3 convolution of the predicted depth maps at multiple scales. We tested our proposed method on the NYU Depth V2 and KITTI datasets and achieved effective performance.
P‐72: <i>Student Poster:</i> Highly Uniform Speckle Pattern Created via an Elastomeric Stencil Mask for High‐Precision Digital‐Image‐Correlation Analysis of Substrate‐Stretching Deformation

Digital image correlation(DIC) analysis is rooted in a well‐formed speckle pattern, and the spray technique is widely utilized due to its decent unpredictability. However, there are clear limitations in using the spray technique for high‐precision DIC analysis. Herein we provide a novel approach for creating a speckle pattern using an elastomeric stencil mask with uniformly sized perforations. As a result, not only can the ideal speckle pattern be obtained, but also individual speckle sizes can also be easily changed. Most importantly, the mask can be conformably mounted to a variety of 3‐D curved surfaces, allowing for precise DIC analysis based on the appropriate pattern.
Imbalanced regression using regressor-classifier ensembles

Abstract We present an extension to the federated ensemble regression using classification algorithm, an ensemble learning algorithm for regression problems which leverages the distribution of the samples in a learning set to achieve improved performance. We evaluated the extension using four classifiers and four regressors, two discretizers, and 119 responses from a wide variety of datasets in different domains. Additionally, we compared our algorithm to two resampling methods aimed at addressing imbalanced datasets. Our results show that the proposed extension is highly unlikely to perform worse than the base case, and on average outperforms the two resampling methods with significant differences in performance.
SVM texture classification and R-CNN approach on medical image

This article deals with the image segmentation method based on texture description and subsequent classification into regions. The second researched method was a Region-Based convolutional neural network as a detector. This paper provides a comparison of both methods and an overview of measured criteria. An important criterium in respiratory cilia segmentation is proper identification of the region of occurrence in the image.
Components and model of implementation of a hand gesture recognition system

The paper describes the components of a hand gesture recognition system. The model of program implementation is described. An overview of the most commonly used SDK“s - Intel RealSense and OpenCV. The aim is to assist the beginner programmer in design and implementation a program using this new type of user interaction.
An approach towards Model-To-Model Mapping between AUTOSAR and Android systems

Every new generation of modern vehicles aims to provide the customers with a higher level of comfort and entertainment services. Consequently, the burden is placed upon the manufacturers to incorporate ever more complex electronic systems in their products and introduce service-based technologies. To meet these demands, service-oriented architecture (SOA) has become an integral part of the Adaptive AUTOSAR as the industry standard. Also, mobile services and applications are natively supported in the new in-vehicle infotainment systems (IVI) which usually run on the Android operating system. As these fundamentally different technologies are incorporated in the vehicles, the developers are facing the challenge of connecting them and exchanging information between the services running in different domains. Service-oriented architectures usually rely on a model-driven engineering approach to facilitate the development of the applications. However, as the complexity of the system grows, the mapping of various metamodels becomes a challenge, as no single standard is used across all domains in the vehicle. This paper provides a possible solution for mapping the already existing AUTOSAR metamodel to an Android Interface Definition Language (AIDL) metamodel. By applying the proposed mapping, it is possible to build applications that require the cooperation of various domains within the vehicle.
A Federated Approach for Learning from Electronic Health Records

The free sharing and exchange of medical information is difficult because medical data is by its nature sensitive. However, medical data is often found across various sources. Each of these sources contain very useful information, and we investigate the possibility of aggregating this information and utilizing this data without its direct transfer. Instead, we explore the use of Federated Learning to transfer the knowledge found in sensitive patient information through gradient updates and model parameters in order to better inform a variety of learning tasks. We also explore specific types of models such as graph networks to model the information in Electronic Health Records (EHRs) collected at hospitals, and we investigate how we might use federated learning to aggregate information such as this across hospitals. Lastly, we show how we can mitigate some of the security risks that are inherent in such federated systems.
Using Project-Based Learning in the Course "Databases" on the Topic "Relational Algebra"

The paper presents the results of the application of the project-based learning approach during the last academic year (2020-2021), in the conditions of the COVID-19 pandemic, in the course "Databases", when teaching and studying the topic "Relational Algebra". During the project-based learning, students-bachelors from the specialty "Internet and Mobile Communications" at the University of Ruse "Angel Kanchev" had to use various computer-based tools, such as MS Excel, MATLAB, LearningApps, etc., to present the basic operations in relational algebra. An analysis of students’ opinions about the approach used for their training in the course "Databases" is given in the paper.
Activity recognition within smart homes using logistic regression

This study takes a look and investigates the application of logistical regression to recognize multiple predefined human activities performed by a single test subject within smart home environment. Two Inertial Measurement Unit (IMU) devices were used as wearable gadgets to collect movement data from the test subjects. Test subject worn these devices on their right wrist and ankle. The obtained data are combined room air quality data (Humidity, CO <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</inf> , temperature) measured using KNX smart home technology. The research is beneficial to provide detailed report about activity levels and well-being of the smart home residence and create a smart home environment that adjust automatically to its residence requirements. The obtained results showed very high classification accuracy (97.8% in average).
A Deep Learning Based Object Detection System for User Interface Code Generation

The Graphical User Interfaces (GUIs) of web applications include visuals and designs that allow users to interact with machines. Once the GUI design is done, it is necessary to generate its GUI code. However, the GUI code generation process is highly time consuming as well as highly dependent on software developers. Therefore, the development of automatic GUI code generating systems is of great importance recently. In this study, a GUI code generating system for web sites is designed using the Deep Learning (DL) approach. The dataset including “coordinate, width, height and type” of GUI objects is created using 7500 webpages. The created dataset is applied to the proposed system in order to detect objects in the GUI image and generate DSL mark-up code. Experiments were carried out to analyze the effectiveness of the proposed system and the performance evaluations were made.
Benchmarking Performance of Ethereum Blockchain on Resource Constrained Devices

Blockchain has challenged many of the conventions around digital security. In essence, blockchain supports a decentralized platform maintained by peers instead of a single entity. Furthermore, the data in the blockchain is immutable and is being held in a secure and encrypted way. However, running the blockchain on resource-limited devices, such as a consumer PC or a low-power Raspberry Pi as opposed to dedicated servers, is demanding due to the resource limitation in energy, memory, and time taken to validate the transaction on the blockchain. This paper explores these limitations by evaluating and benchmarking the blockchain framework Geth: a terminal interface for the Ethereum blockchain which makes it possible to create a private blockchain in addition to joining the actual blockchain. This article employs a private blockchain within Geth and benchmarks the blockchain to highlight the differences (and limitations) between the devices. Specifically, we make the following observations: (i) the time it takes to validate and add the transaction to the blockchain on a Raspberry Pi 4 is markedly slower compared to an Intel i9-10885H CPU @ 2.40GHz, (ii) the transaction between PCs compared to transactions between Pis is around two orders of magnitude higher. These valuable insights can be used to help researchers in the design and implementation of blockchain-driven security architectures for distributed systems, such as industrial IoT deployments and UAV swarms.
The Development of Virtual Worlds as a Tool for Providing Virtual Grocery Store Tours

Poor dietary choices are a leading cause of preventable chronic disease. Consequently, improving dietary choices is a key public health goal. While several approaches are available to influence dietary choices, consumer education is likely a key to improving the diet. One approach to educating consumers is to provide grocery store tours where consumers are provided with nutrition education where purchasing decisions are often made. At this time, there has been limited research to show the effectiveness of this approach or to develop best practices. A possible barrier to innovation in this area concerns the difficulty to get access to grocery stores to conduct tours. Moreover, these barriers may also hamper research efforts to identify best practices. A possible alternative approach is to create virtual worlds where grocery store tours can be conducted remotely. This present manuscript describes an approach to developing virtual grocery stores as a method for providing nutrition education with a description of the design possibilities.
Safety Aspects of the Wheeled Mobile Robot

This paper discusses the safety of mobile robots and proposes improvements by specific methods of collision avoidance, and by changes of logical architecture or the communication protocol. The proposed architecture is semi-distributed, which allows optimalization of the load balance among components while supporting strict safety rules. Such approach allows development of small-size mobile intelligent robots using low-cost computational hardware.
The Value of Immersive Media in Expanding Chinese Public Cultural Participation and Its Realization Path From the Perspective of Cultural Education

This paper mainly introduces the application of immersive media in Chinese public cultural participation from the perspective of cultural education, as well as the important value of the application of immersive media in expanding the breadth, accuracy, and depth of education and thereby improving the quality of education. On this basis, the realistic path of the role of immersive media technology in further realizing public cultural participation is discussed. First of all, through a questionnaire survey, it analyzes the problems existing in Chinese public cultural participation from the perspective of cultural education and the application of immersive media in Chinese public cultural participation. Secondly, from the aspects that the application of immersive media can extend the space of cultural participation, enrich the content of cultural participation and strengthen the value recognition of cultural participation, it demonstrates that immersive media helps to solve the dilemma of Chinese public cultural participation as a whole and thus improve the quality of education. This will bring new possibilities for expanding Chinese public cultural participation and promoting public social education and cultural quality education. Finally, it discusses the further development of immersive media technology in the field of cultural education from the aspects of optimizing the ecology of immersive media development, promoting the R&D and application of immersive media technology, and promoting the integration of immersive media with cultural participation space and cultural education content.
SIM: an improved few-shot image classification model with multi-task learning

Few-shot learning (FSL) has been widely used for image classification when the training samples are limited and can effectively avoid overfitting. Most FSL methods are based on metric learning and tend to learn features that are conducive to classifying known classes of images. However, these methods cannot capture the semantic features that are important for classifying new classes of images. To address this issue, we proposed an improved few-shot image classification model based on multi-task learning termed SIM. It combines the self-supervised image representation learning task with the supervised image classification task, thus utilizing the complementarity of these two tasks. SIM has two stages, i.e., the pre-training stage and the meta-training stage. In pre-training stage, we learned the representation of training images via supervised learning and self-supervised learning. Then, in meta-training stage, we trained a linear classifier based on the learned representation. According to the experiments on four data sets, including three natural image data sets and one marine plankton image data set, SIM outperformed existing methods and achieved quite good performance in complex application scenarios.
Riemannian gradient methods for stochastic composition problems

In the paper, we study a class of novel stochastic composition optimization problems over Riemannian manifold, which have been raised by multiple emerging machine learning applications such as distributionally robust learning in Riemannian manifold setting. To solve these composition problems, we propose an effective Riemannian compositional gradient (RCG) algorithm, which has a sample complexity of O(ϵ-4) for finding an ϵ-stationary point. To further reduce sample complexity, we propose an accelerated momentum-based Riemannian compositional gradient (M-RCG) algorithm. Moreover, we prove that the M-RCG obtains a lower sample complexity of Õ(ϵ-3) without large batches, which achieves the best known sample complexity for its Euclidean counterparts. Extensive numerical experiments on training deep neural networks (DNNs) over Stiefel manifold and learning principal component analysis (PCA) over Grassmann manifold demonstrate effectiveness of our proposed algorithms. To the best of our knowledge, this is the first study of the composition optimization problems over Riemannian manifold.
Modeling Learner Behavior Analysis Based on Educational Big Data and Dynamic Bayesian Network

With the deep integration of information technology and education teaching, the learning revolution represented by MOOCs, Khan Academy and flipped classroom is strongly impacting the ecology of traditional education, highlighting the importance of large-scale online education in the process of reshaping education. In the context of large-scale online educational big data, in order to analyze learners' multi-grain feature performance in terms of learning motivation, emotional attitude, cognitive level and social environment, this paper analyzes and extracts scenario-oriented key features of learners by quantifying learning behavior data on the basis of a custom-built real online learning behavior data set, in order to realize the dynamic construction of learners' personalized learning overview and investigate the multidimensional association mapping construction method of generalized resources, build learner-oriented behavior analysis models and perform performance prediction through machine learning dynamic Bayesian networks, and establish better learning path planning after studying the bidirectional influence mechanism between learners and knowledge system.
A LINDDUN-Based Privacy Threat Modelling for National Identification Systems

The international focus on attaining identity for all has fostered advances in technological developments that have given rise to changing demands on the architecture and deployment of national identification (NID) systems. In particular, national identity management solutions are now expected to respond to a fully modular architecture and to be flexible in the integration of the various building blocks, including the case where the building blocks are provided by different vendors. Another important demand is linked to the increasing concerns about privacy and the potential for unethical or harmful uses of personally identifiable information (PII). This has forced national identity management infrastructures to be compliant with relevant legislation, regulations as well as best practices. In this paper, we investigate how to integrate privacy principles and requirements into a fully modular national identity management architecture implementing a specific use case that deploys the OSIA standard for seamless integration of its building blocks. We employ the LINDDUN methodology to identify privacy threats to the selected use case, elicit mitigation strategies and suggest appropriate privacy enhancing solutions.
Modeling the correlation between the workload and the power consumed by a server using stochastic and non‐parametric approaches

Abstract In this article, we address a critical concern of the growth of computer servers number and the resulting power expenditures in data centers by analyzing the statistical metrics related to the workload executed in each physical node. The aim is to build a stochastic model for power consumption estimation based on historical data. Relying on in‐depth investigation and experimental testing of the power consumption features and performance of the various workload datasets, we propose a model that considers the workload and the power consumed to be executed by a server as random variables. Based on the properties of the probabilistic distribution function of each random variable, we establish the correlation relationship between the workload and the power consumption using a non‐parametric approach. Our use of a non‐parametric method to learn a given probability model is challenging because it requires estimating the full distribution from the available data samples. The accuracy of our approach is demonstrated by estimating the energy consumption of various workloads. The experimental and simulation results show that our model outperforms many existing approaches in terms of accuracy, and it can be applied to a wide variety of workloads.
Towards an IPFS-Blockchain based Authentication/Management System of Academic Certification in Western Balkans

This paper presents an implementation which uses decentralized technologies such as blockchain and IPFS on the issuance and management of the academic certificates. The focus is on improving the actual process, as well as increasing integrity and trustworthiness of ownership of those certificates. The prototype we have developed is called BCert and its purpose is to add the certificates to blockchain network and to offer the possibility to verify them and make accessible to the potential stakeholders. It will also be used to verify that it is possible to upload a minimum of 32 certificates at one time and to evaluate speed of upload. BCert will aim to make the deployment and verification of those certificates quick, fast, secure, and efficient. It uses IPFS (Interplanetary File System) to have private logs and to increase efficiency. Additionally, based on the Technology Transfer concept, we predicted a model related to the launch of the technology to the market. The study was conducted online with 421 responses chosen randomly from several public and private universities in Albania and in Western Balkans.