Reviewerly/exHarmony huggingface dataset:
The Analysis of Online Event Streams: Predicting the Next Activity for Anomaly Detection

Anomaly detection in process mining focuses on identifying anomalous cases or events in process executions. The resulting diagnostics are used to provide measures to prevent fraudulent behavior, as well as to derive recommendations for improving process compliance and security. Most existing techniques focus on detecting anomalous cases in an offline setting. However, to identify potential anomalies in a timely manner and take immediate countermeasures, it is necessary to detect event-level anomalies online, in real-time. In this paper, we propose to tackle the online event anomaly detection problem using next-activity prediction methods. More specifically, we investigate the use of both ML models (such as RF and XGBoost) and deep models (such as LSTM) to predict the probabilities of next-activities and consider the events predicted unlikely as anomalies. We compare these predictive anomaly detection methods to four classical unsupervised anomaly detection approaches (such as Isolation forest and LOF) in the online setting. Our evaluation shows that the proposed method using ML models tends to outperform the one using a deep model, while both methods outperform the classical unsupervised approaches in detecting anomalous events.
Improving Model Inference via W-Set Reduction

Model inference is a form of systematic testing of black-box systems while learning at the same time a model of their behaviour. In this paper, we study the impact of W-set reduction in hW-inference, an inference algorithm for learning models from scratch. hW-inference relies on progressively extending a sequence h into a homing sequence for the system, and a set W of separating sequences into a fully characterizing set. Like most other inference algorithms, it elaborates intermediate conjectures which can be refined through counterexamples provided by an oracle. We observed that the size of the W-set could vary by an order of magnitude when using random counterexamples. Consequently, the length of the test suite is hugely impacted by the size variation of the W-set. Whereas the original hW-inference algorithm keeps increasing the W-set until it is characterizing, we propose reassessing the set and pruning it based on intermediate conjectures. This can lead to a shorter test suite to thoroughly learn a model. We assess the impact of reduction methods on a self-scanning system as used in supermarkets, where the model we get is a finite state machine with 121 states and over 1800 transitions, leading to an order of magnitude of around a million events for the trace length of the inference.
Continuous Behavioral Authentication System for IoT Enabled Applications

In the modern time of digitization, Internet of Things (IoT) play a key role in many sectors like smart factory, smart health monitoring, smart tracking and smart cities; so, this is provding new opportunities for the research and application development. But the acceptance of IoT based applications impacted due to its security and privacy challenges. The security concerns become more critical when the unauthorized user accessing the network services using a paired network device (i.e. Smart phone or laptop). Therefore, an authentication system is required that continuously observe the activities of the end device user using a continuous authentication system. In order to address this challenge, in this paper a noble continuous behavioral authentication system has been introduced by considering the limitations of IoT devices such as battery power and other computational resources. The proposed authentication model involves the machine learning based techniques to analyze the application usages patterns to discover the misleading user behavior. In addition, the correlation coefficient is used for feature ranking. The ranked features are used with three different machine learning algorithms namely support vector machine (SVM) with sequential minimal optimization (SMO), radial basis function (RBF) based SVM-SMO, and a multi-layer perceptron (MLP). The experimental analysis has been carried out using a real world application usage dataset to demonstrate the effectiveness of the proposed continuous behavioral authentication system. The experimental results demonstrate the superiority of the RBF-SVM-SMO technique over other two machine learning models for the IoT-enabled continuous authentication model.
The Role of Intellectual Property Rights in the Technological Age

Abstract The last two decades have seen a huge technological development, also known as the technology explosion, making it easier for anyone to access any kind of information, including of course intellectual property, anywhere in the world, and this has created new challenges for the protection of intellectual property. But nowadays, technology not only allows access to any work of authorship or invention, it is also capable of creating works of authorship or inventions itself, leading legal scholars to debate whether works created by technology can be protected if they are not human‐made. As is well known, almost all national laws only recognise an author or inventor if it is a human‐made work, but what about a painting or invention created by artificial intelligence? Are such works becoming vulnerable and freely available to everyone today? The methodological basis of this article is based on general scientific approaches (analysis and synthesis, deduction and induction, comparison, analogy and a systemic and structural‐functional approach). The article also draws on court decisions, legislation, legal literature, publications and doctrine. Conclusions are drawn using the method of scientific induction and deduction. The aim of this article is to explain the importance of intellectual property protection in today's technological age, where intellectual property can be created not only by people but also by technology.
Prediction of Ether Prices Using DeepAR and Probabilistic Forecasting

Ethereum is a decentralized public blockchain powered by its native cryptocurrency, the Ether ( $$\varXi $$ ), which is second to Bitcoin (BTC) in market capitalization. To ensure the integrity of the network, Ethereum requires a fee for every transaction. This fee is called gas (by analogy to the fuel used by cars) and can fluctuate based on supply and demand. This volatility stepped up a number of initiatives to predict future gas prices. The paper proposes a novel solution beyond current state-of-art using DeepAR. This is a model built on Recurrent Neural Networks with the ability leverage hundreds of related time series to produce more accurate predictions. Our implementation uses features extracted from the Ethereum MainNet as well as off-chain data to achieve accurate predictions.
Coevolutionary Approach to Sequential Stackelberg Security Games

The paper introduces a novel coevolutionary approach (CoEvoSG) for solving Sequential Stackelberg Security Games. CoEvoSG maintains two competing populations of players’ strategies. In the process inspired by biological evolution both populations are developed simultaneously in order to approximate Stackelberg Equilibrium. The comprehensive experimental study based on over 500 test instances of two game types proved CoEvoSG’s ability to repetitively find optimal or close to optimal solutions. The main strength of the proposed method is its time scalability which is highly competitive to the state-of-the-art algorithms and allows to calculate bigger and more complicated games than ever before. Due to the generic and knowledge-free design of CoEvoSG, the method can be applied to diverse real-life security scenarios.
The role of the scientific library in the development of information&#x0D; literacy

The article presents the system for the development of information literacy for different groups of users of the scientific library and librarians (using the example of training activities held in Belarus Agricultural Library). The main informational skills and abilities that students, graduate students, scientists and specialists should possess are listed. The characteristics of seminars and presentations on the development of information literacy are given, with a description of the practical and theoretical parts. The influence of the level of information competence of users on the quality of scientific research is emphasized. The role of information literacy in the formation of professional consciousness of library specialists is shown. The advantages of the remote form of holding events in modern conditions are revealed. The necessity of constantly increasing the level of information skills in scientific libraries is substantiated.
CHARACTERISTICS OF THE PEDAGOGICAL MODEL OF FORMATION OFSTUDENTS’ SOCIAL RESPONSIBILITY BY MEANS OF PROJECT TECHNOLOGIES

The article deals with the theoretical substantiation of the pedagogical model of students' social responsibility by means of project technologies, which contains the following structural components: purpose, tasks, principles, pedagogical conditions, criteria (criteria, indicators and levels of formation), technological support of project activities and a result. The purpose of the model is to form the social responsibility of students. As a result of the implementation of this model, an increase in the level of formation of indicators of social responsibility of students is envisaged. The objectives of the pedagogical model are the following: forecasting the content, directions and means of forming social responsibility of students; developing and implementing pedagogical conditions for the formation of social responsibility of students; developing project technologies as a factor in the formation of social responsibility of students. The content of the principles of the model implementation is detailed and the following characteristics are singled out: personal orientation of the educational process, dialogicity, integrativity, nonlinearity and formation. The authors carry out characterization and description of criteria for the formation of social responsibility of students and relevant indicators of their manifestation: cognitive (awareness of the nature and specifics of social responsibility and its manifestations; ability to analyze social, moral and professional situations, qualities and evaluate them from the standpoint of their own moral principles); evaluative-emotional (readiness to evaluate social phenomena, to provide objective evaluative judgments, self-report and self-evaluation; emotional stability, openness and ability to empathize); worldview (system of social attitudes and socially approving beliefs; transformation of beliefs into personal qualities of students (independence, objectivity, organization, etc.); behavioral(active life and civic positions that find practical confirmation; participation in socially significant public affairs of the group). The authors singled out pedagogical conditions that optimize the process of forming social responsibility of students in the institution of higher education: improving the forms of social and educational work; increasing the share of forms focused on awareness and acceptance of values of social responsibility; modeling of educational and production situations related to the need for socially responsible actions; using design technologies to attract students to socially significant activities.
The activities of a military university cadets in the context of the inclusion of case technologies in the process of independent training

The purpose of the study is to justify the feasibility and prospects of including case technology in the process of independent training of cadets in a military higher educational institution. Scientific novelty lies in justifying the increase in the effectiveness of independent training through the implementation of case study technology. The article analyzes the impact of case technology on the development of professionalism of future officers, determines the stages of work on cases, examines the activities of cadets and the tasks that are solved during each stage. As a result, the approaches to the organization of independent training of cadets in the process of implementing the case-training technology are identified; the capability of case technology, as a means of forming competencies such as design, organizational and communicative, is indicated; the features of the cadets' activity aimed at the formation of the above-mentioned competencies are revealed.
Process Dynamics Modeling on Polishing Unit of Artificial Neural Network-Based Produced Water Treatment System

Modeling on the polishing unit of the PT. SIPL produced water treatment system was carried out using an Artificial Neural Network (ANN). ANN is used because there is no mathematical model capable of modeling biological processes in polishing units. The ANN used is a feedforward backpropagation structure, with the Levenberg-Marquardt algorithm. The ANN model has 4 input nodes at the input layer, temperature, pH, COD, TSS and 1 output node at the output layer in the form of COD effluent. The method to determine the best hidden node does not yet exist, so 20 variations of the hidden node are carried out to find the best validation RMSE value. The simulation results show that the best RMSE value is in the ANN with a 4-13-1 structure (Input layer-hidden layer-output layer). The resulting RMSE value is 14.7919, which is better than the reference ANN model which is 59.48. The ANN model used is also able to produce COD values below the maximum level of wastewater quality standards. Thus, the dynamics of the polishing unit process can be shown by the ANN model 4-13-1 with the influencing parameters, temperature, pH, COD, and TSS.
Solar DC Power System Monitoring for Thermoelectric Mini-Fridge Using Blynk App

Due to the vast number of data generated, time-consuming data collection, and ease of access concerns, online monitoring of the solar PV plants is necessary. The adoption of microcontrollers and loT technologies for data acquisition systems make PV system monitoring efficient. In this paper, the online monitoring system prototype for a stand-alone solar PV system emphasizing DC power parameters: voltage, current, power, and total energy has been designed, built, and implemented. The developed prototype utilizes an ESP32 DEVKIT V.1 board and the Blynk app for online display on a smartphone. The experimental PV plant comprises a 120 W<inf>p</inf> solar panel, a 20 A MPPT solar charge controller, and a 12 V/100 Ah AGM deep cycle VRLA battery. The PZEM-017 DC energy sensor is used and located at three measurement points: the solar panel output, battery, and a thermoelectric mini-fridge. The results show that the PV system monitoring displays appropriately for the operation of the PV plant, e.g., without and with the DC load.
A Rule Based Approach for Detection and Correction of Grammar Errors in Written Active Voice Sinhala Sentences

Sinhala is the official language and major spoken language in Sri Lanka. The Sinhala language is one of the rare languages having diglossia, in which the written variety differs from the spoken variety. The written variety of Sinhala is very complex because it is defined by many grammar rules. Due to this complexity, there is not a specific tool to detect grammar errors in typed sentences as a web-based solution for grade five students to practice answering essay questions. This paper proposes a grammar checker module that helps primary school students who learn in Sinhala medium to identify grammar errors in input sentences. The proposed module also defines the type of errors and provides suggestions to correct errors in subject or verb or both. Therefore, this system enables to improve written competency in Sinhala learning and minimize grammar errors. A rule-based approach has been used for implementing the grammar module and the system is evaluated by the recall, precision, and accuracy, based on grammar error detection, error type identification, and error suggestion aspects. Precision, recall, F1 score for the grammar detection is 1, error type identification accuracy is 93.81% and suggestion accuracy is 83.85% within the defined scope.
Understanding the multiclass classification of lymphomas from simple descriptors

The Lymphoma treatment can be more effective when its type is correctly diagnosed. Several researches have focused on developing accurate classification of the disease from histological images. However, they often use complex features and black box models, making them difficult for specialists to understand. In this work, we use morphological and non-morphological descriptors extracted from cell nuclei for multi-class classification of non-Hodgkin lymphomas. Three ensemble methods were evaluated and the best one achieved an average accuracy 0.956 using linear regression. This performance is close to the state of the art, even our model using simpler descriptors. We also employ explainable artificial intelligence techniques to provide understandable explanations for model&#x2019;s decisions.
Attacks Opportunities in the Cyber Physical Space and the Role of Cybersecurity

Technology is coming into our everyday lives and the way fast our bodily and our on-line world are beginning to come together. It will assist you find out how our on-line world, basically a digital world, is beginning to turn out to be a truth with our growing reliance on it. It particularly specializes in a number of the extreme worries which can be additionally developing with this spatial integration, some examples of ways a few cyberattacks cause deaths in actual life, given the quantity of assault may be an actual chance and the way smooth its miles to execute such attacks. This paper elaborated the all locate methods to mitigate such attacks, however, particularly speak the seriousness of this integration.
VSpace: Vehicle Spatially-Aware Sensing via Acoustic Doppler Effect

Motivated by safety challenges resulting from distracted pedestrians, we design and implement VSpace, a system that achieves spatially-aware sensing of oncoming vehicles based on the Doppler Effect of vehicle sound. VSpace can figure out the natural velocity, direction, and minimum distance of an oncoming vehicle by acoustic Doppler profiling online. VSpace works in four major steps: (1)Vehicle acoustic signal processing; (2)Vehicle classification; (3)Vehicle sound Doppler profiling and (4)Vehicle spatial status reconstruction. All implementation of VSpace is based on a COTS smartphone with a microphone. We evaluate VSpace with comprehensive experiments of 20 different vehicles in 240 practical cases, in which VSpace achieves an accuracy of 91.67% in vehicle classification and 10.85%, 13.81%, 11.59% relative error for natural velocity, direction, and minimum distance calculation.
Centralized Students’ Authentication for Higher Education Systems

Increase in the adoption of ICT for service provision renders higher educational institutions in Nigeria vulnerable to numerous security challenges. The information security threats addressed in this paper encompasses identity theft and identification frauds. The high number of students patronizing these institutions seeking for knowledge results in the challenges of identifying authentic students. The use of students’ identification number and password is no longer sufficient for authentication of students. Therefore, this paper proposed a centralized authentication model for higher education system (CAMHES) for Nigeria. The model uses multi-factor authentication combining students’ identification number, students’ fingerprint biometric and smartcard technology for students’ authentication. The solution authenticates the identity of genuine students to eliminate impersonation in Nigerian higher education systems. Although, this solution provides authentication that is extremely difficult to replicate, it is recommended that the students’ biometric data captured must be strictly kept safe.
BPNN anomaly data detection algorithm based on gray wolf algorithm to optimize K-means clustering

Aiming at the situation that the K-means clustering algorithm tends to fall into the local optimal solution during the clustering process, and the clustering results are prone to errors, this paper proposes a clustering algorithm based on gray wolf optimization-means, Realize the initial selection of K-means cluster centers through the global optimization ability of the gray wolf optimization algorithm. And update the cluster centers through iterative wolf <tex>$\alpha$</tex> to optimize the K-means clustering algorithm. Aiming at BP neural network as a supervised learning algorithm, prior knowledge of data is required for training, due to the different data types generated by different events, the applicability of BP neural network is not strong, the paper proposes a combination of K-means clustering algorithm based on gray wolf algorithm optimization and BP neural network. Cluster the initial data set through K-means clustering, and label the clustered data, then import the labeled data as a training set into the BP neural network for training, and obtain the final detection model to realize online detection of large amounts of data. The experimental results show that the algorithm proposed in this paper on the IBLK dataset and the Taihu Lake water quality dataset is compared with the traditional K-means clustering algorithm and the random forest algorithm based on firefly optimization proposed in [12] in the IBLK dataset and Taihu Lake. Experimental verification on the water quality data set, the detection rate was increased by 8.9&#x0025;, 17.7&#x0025; and 1.15&#x0025;, 12.6&#x0025;; the false alarm rate was reduced by 8.1 &#x0025;, 19.3&#x0025; and 1.12&#x0025;, 13.6&#x0025; respectively.
An RGB-D Based Approach for Human Pose Estimation

With depth information more easily accessible even on mobile devices, leveraging RGB and depth information for RGB-D training provides a new way to enhance human pose estimation performance. In this paper, we propose an RGB-D based approach for human pose estimation. The main contributions of this paper are: 1) improving the accuracy and robustness of the model by utilizing depth image, 2) establishing a lightweight network architecture to improve the performance in detection speed, which makes it suitable for deployment on mobile devices. Qualitative and quantitative analyses on experimental results demonstrate that our model outperforms Open-Pose by 34% in detection speed, reduces model size to 42% at the same time. Our model also provides some advantages in specific background environments.