Reviewerly/exHarmony huggingface dataset:
Security Using Blockchain in IoT-Based System

Devices containing sensors, computing power, software, and other technologies are known as the “Internet of Things” (IoT). IoT devices may connect to and share data with one another over the Internet or another communication network. You can secure your company’s Internet-connected devices, as well as the networks they access, against cyberattacks and breaches by implementing an IoT security solution. What makes your IoT systems safe is their security discipline. Protecting IoT against attacks and breaches is one of the primary functions of security solutions for the IoT. Your IoT solution’s availability, integrity, and confidentiality are all safeguarded by IoT security. For educational institutions, mobile devices and IoT are making it feasible to strengthen campus security, monitor crucial resources, and give students more information. Smart lesson plans, rather than the dreary old ones, may be created by teachers using this new technology. According to this study, one way to protect educational institutions from cyber-assaults is via the use of IoT. Security, reliability, and performance qualities will be evaluated by comparing current studies to those that have been proposed.
Bounding the Family-Wise Error Rate in Local Causal Discovery Using Rademacher Averages

Many algorithms have been proposed to learn local graphical structures around target variables of interest from observational data. The Markov boundary (MB) provides a complete picture of the local causal structure around a variable and is a theoretically optimal solution for the feature selection problem. Available algorithms for MB discovery have focused on various challenges such as scalability and data-efficiency. However, current approaches do not provide guarantees in terms of false discoveries in the MB. In this paper we introduce a novel algorithm for the MB discovery problem with rigorous guarantees on the Family-Wise Error Rate (FWER), that is, the probability of reporting any false positive. Our algorithm uses Rademacher averages, a key concept from statistical learning theory, to properly account for the multiple-hypothesis testing problem arising in MB discovery. Our evaluation on simulated data shows that our algorithm properly controls for the FWER, while widely used algorithms do not provide guarantees on false discoveries even when correcting for multiple-hypothesis testing. Our experiments also show that our algorithm identifies meaningful relations in real-world data.
Interactive Toolbox for Two-Dimensional Gaussian Mixture Modeling

Research data obtained during economics or human studies experiments often displays a complex distribution. Even in the two-dimensional case, the statistical identification of subgroups in research data poses an analytical challenge. Here we introduce an interactive R-based tool called “AdaptGauss2D”. It enables a valid identification of a meaningful multimodal structure in two-dimensional data. With a human-in-the-loop approach, a Gaussian mixture model (GMM) can be fitted to the data. The interactive interface allows a supervised selection of the number and parameters of the GMM based on various visualizations. Integrating a Human-in-the-loop into the process of modeling two-dimensional gaussian mixtures enables the expectation-maximization (EM) algorithm to adapt to more complex GMM compared to the standard non-interactive approach. The work demonstrates that the interactive modeling process for GMM improves the quality of the model in contrast to non-interactive modeling. The improvement is shown using the datasets of EngyTime and a large flow cytometry dataset. The R package “AdaptGauss2D” is available on GitHub https://github.com/Mthrun/AdaptGauss2D .
A Blockchain-Based Approach for Audit Management of Electronic Health Records

The maintenance of proper health records is essential to patient health care. Electronic Health Records (EHR) are now replacing traditional Manual Health Records. Audit logs or Audit Trails are a record of events and changes done in a system. Majority of hospitals are required to maintain an audit trail of each and every EHR. Currently, the audit trail is stored in relational databases, which can be easily modified and trust can be lost in the process. Also, third-party audit trails are inefficient, costly, and time-consuming. Replication on Blockchain would be a viable method for securing audit trails so that they are secure, transparent, and immutable without the need for third-party intervention. In this manuscript, we have proposed an Audit Management System where immutable audit trail of EHR can be generated on Blockchain. In this system, a physician or other medical authority having access to this audit trail can easily verify all the consultations, procedures and prescriptions given to the patient in a chronological manner.
Node4Chain: Extending Node-RED Low-Code Tool for Monitoring Blockchain Networks

Blockchain is a distributed, secure and leading technology that enables the immutability, traceability and transparency of data. Nevertheless, integrating blockchain network monitoring with other systems is a difficult task that requires a vast knowledge of the technology. To deal with this challenge, in this demo we present Node4Chain, a novel extension of the Node-RED low-code tool that allows for defining graphical flows that specify data inputs, outputs and processing logic needed to monitor in real time blockchain networks, such as Ethereum, Binance or Polygon, as well as integrating it with other systems and technologies.
Designing and Implementation of Online Judgment System

In this paper we prepare extensive analysis of creation process for online judge solution. Design of architecture is inspired by methods used widely in professional applications and by experiences with existing systems of this kind. We present our vision of all system elements—web application, server application and executors. To create web application we use Material Design technology. Scalability is given by use of Google Kubernetes and Docker technologies. There is considered wide range of probable security threats, and after extensive testing eBPF solutions was chosen. In first version of system C++, Java and Python are supported. System offers also amazing extensibility by plugins, in these way even support of new programming language can be added.
Presaging Cancer Stage Classification by Extracting Influential Features from Breast/Lung/Prostate Cancer Clinical Datasets Based on TNM Model

Detecting cancer and as well as classifying the stages of the cancer are the most important part for prognosis and imparting rightful clinical treatment. Study shows that numerous computational models have already been developed which are performing well enough for early detection and prediction of the disease; however, despite considerable efforts and progress, there remains a lack for accurately identifying the stages during the time of detection of the disease. The objective and notable approach of our study are to analyze the clinical data to accurately classify the tumor (T) stages of cancer at the time of detection. Here, in this work, we have proposed a model based on the standard tumor, node, metastasis (TNM) model and identified some important factors (IFs) (perimeter, area, radius, etc.) from the dataset with the help of state-of-the-art standard ML/DL algorithms. Thereafter, to identify the T stages of the cancer, the proposed model finds some additional influential factors (such as concavity, texture, compactness) which are highly correlated with some important factor like circumferential measurement. The accuracy, specificity, sensitivity, recall, and F score are measured for the prediction of disease and compared with previous experimental results of early detection which shows that our model performs better than others. Finally, after accurately classifying the stages, the accuracy of each of the cancer T stages has been calculated with three datasets. The accuracy of each stage of the proposed model for three dataset shows that for breast cancer, the outcome is T1-95.1%, T2-99.3%, and T3-100%; for lung cancer, it is T1-98.7% and T2 and T3-100%; and for prostate cancer, it is T1-92.1%, T2-95.4%, and T3-96%.
A Systematic Literature Review on Multi-modal Medical Image Registration

Context: In today’s health care, multi-modal image registration increasingly important role in medical analysis and diagnostics. Multi-modal image registration is a challenging task because of the different imaging conditions that changes from one imaging modality to another. Objective: The purpose of this work is to determine the current state of the art in the field of medical image registration shedding light on techniques that have been used to register medical image combinations from different modalities and the importance of combining different modalities in automatic way in the medical domain. Method: To fulfill this objective we chose a Systematic Literature Review (SLR) as method to follow. Which allows to collect and structure the information that exists in the field of multi-modal image registration. Results: Several automatic solutions based on different registration techniques were proposed according to each specific modality combination. Conclusion: The results provide the following conclusions: First, the machine learning in the recent years plays an important role in the automatic registration process. An important number of research propose a learning-based registration solution. Second, There few solutions in literature that tackle the automatic registration of histology - CT modality combination. Finally, the existing research work propose registration solutions for only combination of two modalities. A very few number of work suggest a tri-modality combining.
GNNSampler: Bridging the Gap Between Sampling Algorithms of GNN and Hardware

Sampling is a critical operation in Graph Neural Network (GNN) training that helps reduce the cost. Previous literature has explored improving sampling algorithms via mathematical and statistical methods. However, there is a gap between sampling algorithms and hardware. Without consideration of hardware, algorithm designers merely optimize sampling at the algorithm level, missing the great potential of promoting the efficiency of existing sampling algorithms by leveraging hardware features. In this paper, we pioneer to propose a unified programming model for mainstream sampling algorithms, termed GNNSampler, covering the critical processes of sampling algorithms in various categories. Second, to leverage the hardware feature, we choose the data locality as a case study, and explore the data locality among nodes and their neighbors in a graph to alleviate irregular memory access in sampling. Third, we implement locality-aware optimizations in GNNSampler for various sampling algorithms to optimize the general sampling process. Finally, we emphatically conduct experiments on large graph datasets to analyze the relevance among training time, accuracy, and hardware-level metrics. Extensive experiments show that our method is universal to mainstream sampling algorithms and helps significantly reduce the training time, especially in large-scale graphs.
FUSPAQ: A Function Selection Platform to Adjust QoS in a FaaS Application

Function as a Service (FaaS) development has numerous benefits for application deployment, management, and maintenance. However, the lack of control over the infrastructure and, often, over the FaaS platform itself, makes it necessary to look for external solutions that allow the operation of the application to be adapted to different requirements or changing execution conditions. In a FaaS application, the quality of service (QoS) is determined by the characteristics of the functions executed to perform each workflow operation. Deciding the most suitable functions providing a QoS is a complex process due to the high variability of possible function implementations, each giving different qualities. Leaving this task in the hands of the developer is not a good solution and makes it difficult to program the application. We present FUSPAQ, a framework for working with serverless architectures, which can automatically select the best functions executed at runtime to satisfy specific QoS requirements. With this objective, a Software Product Line approach is used, modeling the application’s tasks and operations using Feature Models that specify the variability of functions that can perform the same operation as a family of functions. We use Z3, a cross-platform satisfiability modulo theories (SMT) solver, to generate optimal configurations. As requirements can change over time, the system automatically adapts to these changes to continue maintaining the desired QoS. We test our approach with different QoS parameters, and analyse the value added to serverless frameworks.
Immediate Split Trees: Immediate Encoding of Floating Point Split Values in Random Forests

Random forests and decision trees are increasingly interesting candidates for resource-constrained machine learning models. In order to make the execution of these models efficient under resource limitations, various optimized implementations have been proposed in the literature, usually implementing either native trees or if-else trees. While a certain motivation for the optimization of if-else trees is to benefit the behavior of dedicated instruction caches, in this work we highlight that if-else trees might also strongly depend on data caches. We identify one crucial issue of if-else tree implementations and propose an optimized implementation, which keeps the logic tree structure untouched and thus does not influence the accuracy, but eliminates the need to load comparison values from the data caches. Experimental evaluation of this implementation shows that we can greatly reduce the amount of data cache misses by up to $$\approx 99\%$$ , while not increasing the amount of instruction cache misses in comparison to the state-of-the-art. We additionally highlight various scenarios, where the reduction of data cache misses draws important benefit on the allover execution time.
An Embedded Continual Learning System for Facial Emotion Recognition

While being a key element of human-human communication, face emotion recognition is an important challenge for human-computer interactions. Feature extraction and classification methods have been developed during the past decades in order to propose increasingly accurate emotion recognition algorithms. Nevertheless, in a changing environment where systems need to be continually adapted, the issue of catastrophic forgetting becomes a major challenge. Based on the bio-inspired continual learning algorithm Dream Net, we propose an embedded system for face emotion recognition. This system is innovative in its ability to learn incrementally on a NVIDIA Jetson Nano platform without catastrophic forgetting while preserving privacy and being agnostic to data. Live demonstration of this system can be done and users can test it in several modes of operation: emotion recognition or learning of new emotions.
Internet of Things Semantic-Based Monitoring of Infrastructures Using a Microservices Architecture

We live in an Internet-connected world, where tens of billions of smart entities are constantly sending information, the Internet of Things (IoT). In some cases, the communication of data messages is straightforward. Devices are connected in the same ecosystem, where it is pretty easy to agree on the communication data format. However, when devices have to communicate information to outside environments, each manufacturer defines its own data communication format. In the IoT domain, these cases are not covered extensively, and information homogenization must be done. In this thesis, we propose an ontology, an abstract interpretation of domain concepts and data, aiming to provide a solution for data heterogeneity in the domain. For this regard, we also propose a working environment where operators can manipulate all the system’s information, developed using a microservices architecture.
Discovery of Small Signals in Big Backgrounds

Discovery of new phenomena typically consists of finding a small signal of data in processes which naturally produce similar data with much larger rates; these latter “events” we call background. The statistical problem, or data analytics problem, lies in discerning the signal in the large background i.e., the proverbial needle in a haystack. We see here how the analysis benefits from high dimensionality via successively reducing the background as we go higher in number of dimensions.
Factorized Structured Regression for Large-Scale Varying Coefficient Models

Recommender Systems (RS) pervade many aspects of our everyday digital life. Proposed to work at scale, state-of-the-art RS allow the modeling of thousands of interactions and facilitate highly individualized recommendations. Conceptually, many RS can be viewed as instances of statistical regression models that incorporate complex feature effects and potentially non-Gaussian outcomes. Such structured regression models, including time-aware varying coefficients models, are, however, limited in their applicability to categorical effects and inclusion of a large number of interactions. Here, we propose Factorized Structured Regression (FaStR) for scalable varying coefficient models. FaStR overcomes limitations of general regression models for large-scale data by combining structured additive regression and factorization approaches in a neural network-based model implementation. This fusion provides a scalable framework for the estimation of statistical models in previously infeasible data settings. Empirical results confirm that the estimation of varying coefficients of our approach is on par with state-of-the-art regression techniques, while scaling notably better and also being competitive with other time-aware RS in terms of prediction performance. We illustrate FaStR’s performance and interpretability on a large-scale behavioral study with smartphone user data.
Gesture Analysis Using Image Processing: For Detection of Suspicious Human Actions

In recent years, interest in real-time video sequences for human action recognition has risen rapidly, and discrimination is required to identify which type of human action appears in the videos. The use of video for human action recognition is becoming widespread in many applications like monitoring of traffic, pedestrian detection, anomalous behavior identification of the human being, health monitoring, human–computer interaction, robotics, etc. Various combinations of feature representation are used to implement different feature detectors for the identification of features and to classify human actions. For testing the given data, Machine learning-based techniques such as unsupervised and supervised learning are utilized to estimate the task of identifying human behaviors. For classification, unsupervised learning techniques like data clustering, hierarchical learning, etc., are implemented, whereas to handle action classification, supervised learning techniques like SVM, random forest, KNN, Naive–Bayes, and artificial neural networks are used. In this paper, real-time applications for suspicious action detection, tracking, and recognition are surveyed and classified. We propose a new technology like MediaPipe Holistic which provides pose, face, and hand mark detection models where the suspicious movement of the human being is being detected by the system from a video that may be stored in memory or from real-time monitoring.
Design, Inclusion and Sustainable Development: Guidelines for the Creation of a People-Centred Urban Park

Nowadays society, increasingly oriented towards actions to enhance its urban contexts, tries to promote better lifestyles and increasingly inclusive social practices. The work proposed in this article presents the results of the research project: “Guidelines for the review of the park project aimed at social inclusion, sustainability and usability of the cultural and sporting activities of the Carpugnane area”, funded by the Municipality of Calenzano and conducted by the Ergonomics & Design Laboratory of the University of Florence. The research used the methodological tools of Ergonomics for Design, Human-Centred Design and Inclusive Design and the collected data allowed the design of a people-centred park, suitable for citizens of all ages, which is sustainable and inclusive. The approach, adopted in this work, has provided an effective strategy in allowing the assessment and design of urban environments, ensuring safety, psycho-physical well-being and healthy lifestyles for all, taking into account human diversity and social inclusion factors.
Demonstrator on Counterfactual Explanations for Differentially Private Support Vector Machines

We demonstrate the construction of robust counterfactual explanations for support vector machines (SVM), where the privacy mechanism that publicly releases the classifier guarantees differential privacy. Privacy preservation is essential when dealing with sensitive data, such as in applications within the health domain. In addition, providing explanations for machine learning predictions is an important requirement within so-called high risk applications, as referred to in the EU AI Act. Thus, the innovative aspects of this work correspond to studying the interaction between three desired aspects: accuracy, privacy, and explainability. The SVM classification accuracy is affected by the privacy mechanism through the introduced perturbations in the classifier weights. Consequently, we need to consider a trade-off between accuracy and privacy. In addition, counterfactual explanations, which quantify the smallest changes to selected data instances in order to change their classification, may become not credible when we have data privacy guarantees. Hence, robustness for counterfactual explanations is needed in order to create confidence about the credibility of the explanations. Our demonstrator provides an interactive environment to show the interplay between the considered aspects of accuracy, privacy, and explainability.
Comparative Analysis of Book Recommendation System Based on User Reviews Using Hybrid Methods

In recent times, Recommendation System (RS) performs a main position in assisting clients to discover the best books of their interest. A novel solution for recommending accurate books to the user was proposed by applying an integration of the Collaborative Filtering (CF) and Content-Based Filtering (CBF) with Popularity Index (PI) methods. It recommends books to the registered users in the system and also to the new users. Firstly, the input data which is document level reviews transferred from analysis stage to analyzes the sentiment of the user and removes noisy data. Now, grouping of reviewers is done dependent on the kind of review they are given and their personal details such as age, region and sex by utilizing MySQL. CBF system and CF system are used to filter reviews. In additional, PI technique is also used to retrieve data from popular books by ranking in the database. Finally, after filtering, Recommender suggests the books to the users based on their interests accurately which satisfies the customer with an average accuracy of 85%.
Deep Learning Taxonomy on Human Face Expression Recognition for Communication Applications

In social communication applications, the most challenging and power task is human face expression recognition system. Basically, communication with emotions and intentions in direct way is nothing but face expression. For the nonverbal communication one of the main characteristics in faces expressions. Face Expression Recognition (FER) techniques are introduced in this paper. This FER will divide into three stages mainly they are pre-processing, feature extraction and classification. Based on their major contributions techniques of FER are introduced. Performance is analyzed based on the complexity of algorithms of FER. From recent papers it reveals that classification techniques are more powerful and reliable with peculiar characteristics.