Reviewerly/exHarmony huggingface dataset:
Fake Profile Detection Using Machine Learning

Platforms for social media like Facebook, Twitter, Instagram, and others have a big impact on our lives. All across the world, people are actively engaged in it. But, it also needs to address the problem of false profiles. Fake accounts are regularly made by people, software, or machines. They are employed in the spread of rumors and illegal actions like phishing and identity theft. This project uses several machine learning techniques to discriminate between fake and authentic Twitter profiles based on characteristics such as follower and friend counts, status changes, and more. Twitter profile dataset, classifying genuine accounts as TFP and E13 and fake accounts as INT, TWT, and FSF. In this section, the author talks about neural networks, LSTM, XG Boost, and Random Forest. The important traits are picked to judge the veracity of a social media page. The architecture and hyperparameters are also discussed. Lastly, after the models have been trained, results are generated. As a result, the output is 0 for true profiles and 1 for fake profiles. It is possible to disable or delete a fake profile when it is found, preventing cyber security issues.
Building Neural Machine Translation Systems for Multilingual Participatory Spaces

This work presents the development of the translation component in a multistage, multilevel, multimode, multilingual and dynamic deliberative (M4D2) system, built to facilitate automated moderation and translation in the languages of five European countries: Italy, Ireland, Germany, France and Poland. Two main topics were to be addressed in the deliberation process: (i) the environment and climate change; and (ii) the economy and inequality. In this work, we describe the development of neural machine translation (NMT) models for these domains for six European languages: Italian, English (included as the second official language of Ireland), Irish, German, French and Polish. As a result, we generate 30 NMT models, initially baseline systems built using freely available online data, which are then adapted to the domains of interest in the project by (i) filtering the corpora, (ii) tuning the systems with automatically extracted in-domain development datasets and (iii) using corpus concatenation techniques to expand the amount of data available. We compare our results produced by the domain-adapted systems with those produced by Google Translate, and demonstrate that fast, high-quality systems can be produced that facilitate multilingual deliberation in a secure environment.
Integration of structural, hydrogeological and thermal remote sensing data for the determination of geothermal capacity A case study of the Edremit (Balıkesir) Basin

Basins formed on active strike-slip faults are important prospect areas for geothermal energy exploration since the crust gets thinner in these areas and tectonic structures provide favorable conditions for heat-fluid circulation and transport. The Edremit (Balıkesir) Basin holds a great promise for the discovery of new geothermal energy sources. The objective of the recent study is to evaluate the geothermal capacity of the Edremit Basin utilizing tectonic, geological, and hydrogeological studies, combining remote sensing (land surface temperature-LST, hydrothermal alteration, and multi-temporal InSAR (MT-InSAR) assessments). We present structural literature data and the results of field mapping, which revealed the geometry, kinematics, and dynamics of structural features, geological units as geothermal system components, thermal and cold water chemistry, and thermal infrared remote sensing analysis. For the purpose of assessing new targets and recent geothermal capacity, these data are combined and evaluated. According to the outcomes of the study, the fault pattern in the Edremit Basin is generated by N-S extension, which produced E-W dominant striking normal faults with a heritage of paleostructures oriented in various directions. According to remote sensing analyses, the primary LST regions in the basin are defined by the active faults. Therefore, a high sodium sulfate ratio recorded in the chemical analyses of the water samples indicates a deep circulation and high possibility for the presence of thermal water. Consequently, our findings are consistent with the work to include thorough field geology surveys, structural patterns, LST, and water chemistry to refined exploration process.
An order insensitive optimal generalised sequential fusion estimation for stochastic uncertain multi‐sensor systems with correlated noise

Abstract The globally optimal generalised sequential fusion (GSF) algorithm in the sense of linear minimum variance for multi‐sensor stochastic uncertain systems is investigated by the authors. Specifically, in the GSF algorithm, the estimation of measurement noise is considered, and m a ( m a ≥ 1) sensors' measurement data are fused at the a th reception instant, which makes it very flexible and suitable for practical applications. The centralised and sequential fusion algorithms are special cases of the proposed GSF algorithm. Furthermore, for any m a , a = 1, 2, …, M , the estimated values of the GSF algorithm remain invariant and globally optimal. Moreover, the independence between the estimated values and fusion order is proved in the proposed GSF algorithm. Finally, simulation results are given to demonstrate the usefulness of the developed algorithm.
Optimal design and cost analysis of single-axis tracking photovoltaic power plants

The increasing penetration of photovoltaic technology in the electricity market requires the development of a methodology that facilitates the optimisation of photovoltaic plants with single-axis trackers. This paper presents an optimisation methodology that takes into account the most important design variables of single-axis photovoltaic plants, including irregular land shape, size and configuration of the mounting system, row spacing, and operating periods (for backtracking mode, limited range of motion, and normal tracking mode). Equations for the determination of the optimal row spacing and operating periods have been developed and is presented in detail. A packing algorithm that takes into account the irregular land shape and the possible configurations of the mounting systems is also presented. The objective function is the total area of the photovoltaic field and the optimisation is performed by a packing algorithm. As the economic aspect of energy generation also plays a key role in decision-making, the levelised cost of energy has been used to assess the economic viability of the optimal layout of the mounting systems. The results show that the proposed methodology and packing algorithm are able to optimise the photovoltaic plant with single-axis solar tracking and provide reliable results after a reasonable computation time. The methodology was demonstrated in detail for a Spanish photovoltaic plant (Granjera photovoltaic power plant), including the optimal layout of the mounting systems and the cost analysis for this layout. The optimal layout of the mounting systems could increase the amount of energy captured by 91.18% in relation to the current of Granjera photovoltaic power plant. The mounting system configuration used in the optimal layout is the one with the best levelised cost of energy efficiency, 1.09. The presented optimisation methodology can be utilised to facilitate the optimal design of commercial photovoltaic plants with single-axis trackers. Therefore, questions such as: what is the optimal distribution of mounting systems?, how much energy will this distribution produce?, and at what cost will it produce it?, can be answered by using the proposed methodology.
Scraping and Analyzing Data of a Large Darknet Marketplace

Darknet marketplaces in the Tor network are popular places to anonymously buy and sell various kinds of illegal goods. Previous research on marketplaces ranged from analyses of type, availability and quality of goods to methods for identifying users. Although many darknet marketplaces exist, their lifespan is usually short, especially for very popular marketplaces that are in focus of law enforcement agencies. We built a data acquisition architecture to collect data from White House Market, one of the largest darknet marketplaces in 2021. In this paper we describe our architecture and the problems we had to solve, and present findings from our analysis of the collected data.
Equipment Quality Information Mining Method Based on Improved Apriori Algorithm

Equipment quality-related data contains valuable information. Data mining technology seems to be an efficient method for extracting knowledge from large amounts of data. In this paper, a general method for equipment quality information mining based on association rule is proposed for complex equipment. Due to the shortcomings of classical association rule mining algorithms such as long running time and high memory consumption, the candidate itemset generation process is optimized, and an improved Apriori algorithm is proposed. Taking five experimental data sets as the object, the performance of the algorithms is tested using time complexity and spatial complexity as evaluation criteria. Comparative experiments show that the improved algorithm had advantages. To further implement data processing and information representation, a matrix-based strong association rule extraction algorithm was proposed. Taking a certain type of equipment as an example, a simulation experiment was conducted using the method proposed in this article in reliability test data sets, and some interesting knowledge was obtained through mining, verifying the effectiveness of the method. The research in this article seems promising with respect to improving the scientific level of equipment support.
Influence of Teaching Digital Competence and Professional Competencies on the Professional Performance of the Soccer Coach

Abstract The main objective of this article is to design a structural equation model to evaluate the predictor variables of professional performance in soccer coaches (PPSC), digital teaching competence (DTC) and professional competences of soccer coaches (PCSC). A sample of 612 coaches was taken from the different training levels of the Valencian Community (FFCV) in Spain, managed by UEFA.. The results of the causal relationship model showed a good overall fit of the model. A statistically significant positive relationship was found to exist with the professional performance of soccer coaches in both the use of digital competencies (DTC) and the professional competence of soccer coaches (PCSC). A statistically significant positive relationship was also observed between the use of digital competencies and the professional competence of soccer coaches. From the results obtained, it can be affirmed that the change generated by UEFA in the training of coaches in emerging technologies increases their professional performance.
Artificial intelligence to assist specialists in the detection of haematological diseases

Artificial intelligence, particularly the growth of neural network research and development, has become an invaluable tool for data analysis, offering unrivalled solutions for image generation, natural language processing, and personalised suggestions. In the meantime, biomedicine has been presented as one of the pressing challenges of the 21st century. The inversion of the age pyramid, the increase in longevity, and the negative environment due to pollution and bad habits of the population have led to a necessity of research in the methodologies that can help to mitigate and fight against these changes. The combination of both fields has already achieved remarkable results in drug discovery, cancer prediction or gene activation. However, challenges such as data labelling, architecture improvements, interpretability of the models and translational implementation of the proposals still remain. In haematology, conventional protocols follow a stepwise approach that includes several tests and doctor-patient interactions to make a diagnosis. This procedure results in significant costs and workload for hospitals. In this paper, we present an artificial intelligence model based on neural networks to support practitioners in the identification of different haematological diseases using only rutinary and inexpensive blood count tests. In particular, we present both binary and multiclass classification of haematological diseases using a specialised neural network architecture where data is studied and combined along it, taking into account the clinical knowledge of the problem, obtaining results up to 96% accuracy for the binary classification experiment. Furthermore, we compare this method against traditional machine learning algorithms such as gradient boosting decision trees and transformers for tabular data. The use of these machine learning techniques could reduce the cost and decision time and improve the quality of life for both specialists and patients while producing more precise diagnoses.
Face Recognition Based Student Attendance System

The term 'biometrics' refers to a measurable characteristic that is unique to an individual such as fingerprints, facial structure, the iris or a person's voice. This project presents a face image based biometric system that records the attendance of a person by using a hand held face image biometrics. Attendance is a concept that exists in different places like institutions, organization’s, hospitals, etc. during the start and end of the day to mark a person’s presence. In early days and even now in many places attendance is recorded manually in attendance registers by calling out the names. This results in waste of time and human effort. Also there are many fraudulent issues that happen when we use a register. A student attendance system using face recognition and Grassmann classification is a biometric system that can automate the attendance taking process in a classroom setting. This system involves collecting a dataset of images of each student in the classroom and using face detection and alignment algorithms to detect and align the faces in each image. The Grassmann classification algorithm is then used to extract features from the images and classify them as belonging to a particular student. When a student enters the classroom, their face is captured by a camera and compared to the images in the dataset. If a match is found, the student is marked as present. This system has the potential to save time and increase accuracy in the attendance taking process. However, it also raises concerns about privacy and the security of the collected data. Proper safeguards must be put in place to ensure that the system is used ethically and responsibly
A systematic review on security and safety of self-adaptive systems

Cyber–physical systems (CPS) are increasingly self-adaptive, i.e. they have the ability to introspect and change their behavior. This self-adaptation process must be considered when modeling the safety and security aspects of the system. This study collects and compares security attacks and safety hazards on self-adaptive systems (SAS) described in the literature. In addition, mitigation and treatment strategies, as well as the modeling and analysis approaches, are investigated. We conducted a systematic literature review on 21 selected papers. The selection process included a database search on four scientific databases using a common search string (1430 papers), forward and backward snowballing (1402 papers), and filtering the results based on predefined inclusion and exclusion criteria. The coding scheme to analyze the content of the papers was obtained through research questions, existing domain-specific taxonomies, and open coding. Safety and security are not jointly modeled in the context of self-adaptive systems. The adaptation process is often not considered in the attack and hazard analysis due to naïve assumptions and modeling. The proposed approaches are mostly verified and validated through simulation often using simple use cases and scenarios. A thorough and joint modeling approach for safety and security in self-adaptive systems is still an open challenge that needs to be addressed. Further work is needed to address the gap between safety and security modeling in self-adaptive systems. Editor's note: Open Science material was validated by the Journal of Systems and Software Open Science Board.
Development of Anemia Cells Recognition System Using Raspberry Pi

Anemia is the most predominant blood disease globally and is caused by iron deficiency resulting to fatigue. Thalassemia is the shortage of production of essential protein, the hemoglobin, in the blood that distribute oxygen throughout our body. Codocytes or Target cells and Elliptocytes are types abnormal red blood cells that are most commonly associated with anemia and Thalassemia. Traditional method of manually determining these abnormal RBCs from a blood smear is labor intensive and can be subjective. This paper automates the recognition of codocytes and elliptocytes from blood smear images. The recognition system uses image processing and support vector machine to be able to classify the Codocytes and Elliptocytes in the PBS. The average accuracy for the classification of PBS images that contain codocytes and elliptocytes is 94.31%. This will help advance further researches on abnormal red blood cell detections and aid in identifying early pathognomonic determinants of anemia and Thalassemia.
SkillRec: A Data-Driven Approach to Job Skill Recommendation for Career Insights

Understanding the skill sets and knowledge required for any career is of utmost importance, but it is increasingly challenging in today’s dynamic world with rapid changes in terms of the tools and techniques used. Thus, it is especially important to be able to accurately identify the required skill sets for any job for better career insights and development. In this paper, we propose and develop the Skill Recommendation (SkillRec) system for recommending the relevant job skills required for a given job based on the job title. SkillRec collects and identify the skill set required for a job based on the job descriptions published by companies hiring for these roles. In addition to the data collection and pre-processing capabilities, SkillRec also utilises word/sentence embedding techniques for job title representation, alongside a feed-forward neural network for job skill recommendation based on the job title representation. Based on our preliminary experiments on a dataset of 6,000 job titles and descriptions, SkillRec shows a promising performance in terms of accuracy and F1-score.
Connecting AI Technologies as Online Services to a Humanoid Service Robot

This paper addresses the possibility to use a variety of sophisticated Artificial Intelligence (AI) driven technologies with a humanoid service robot without the need to have a deeper knowledge of the respective AI technology. Application interfaces are used that are capable to exchange data between the robot and highly complex AI services running on a remote server. Such application programming interfaces (APIs) can be applied by users who have little or no experience with the linked AI techniques. The consequences would be a reduction of barriers in the sense of AI democratization for in-house developers lacking special expertise and the facilitation of development for a wider user base including those without specialized knowledge of AI. We describe some application examples in a generic case study, how they are built and the different APIs that are used. Technical problems and limitations of our attempts are also mentioned.
PF-YOLOv4-Tiny: Towards Infrared Target Detection on Embedded Platform

Infrared target detection models are more required than ever before to be deployed on embedded platforms, which requires models with less memory consumption and better real-time performance while considering accuracy. To address the above challenges, we propose a modified You Only Look Once (YOLO) algorithm PF-YOLOv4-Tiny. The algorithm incorporates spatial pyramidal pooling (SPP) and squeeze-and-excitation (SE) visual attention modules to enhance the target localization capability. The PANet-based-feature pyramid networks (P-FPN) are proposed to transfer semantic information and location information simultaneously to ameliorate detection accuracy. To lighten the network, the standard convolutions other than the backbone network are replaced with depthwise separable convolutions. In post-processing the images, the soft-non-maximum suppression (soft-NMS) algorithm is employed to subside the missed and false detection problems caused by the occlusion between targets. The accuracy of our model can finally reach 61.75%, while the total Params is only 9.3 M and GFLOPs is 11. At the same time, the inference speed reaches 87 FPS on NVIDIA GeForce GTX 1650 Ti, which can meet the requirements of the infrared target detection algorithm for the embedded deployments.
An Improved Granulated Convolutional Neural Network Data Analysis Model for COVID-19 Prediction

As COVID-19 poses a major threat to people’s health and economy, there is an urgent need for forecasting methodologies that can anticipate its trajectory efficiently. In non-stationary time series forecasting jobs, there is frequently a hysteresis in the anticipated values relative to the real values. The multilayer deep-time convolutional network and a feature fusion network are combined in this paper’s proposal of an enhanced Multilayer Deep Time Convolutional Neural Network (MDTCNet) for COVID-19 prediction to address this problem. In particular, it is possible to record the deep features and temporal dependencies in uncertain time series, and the features may then be combined using a feature fusion network and a multilayer perceptron. Last but not least, the experimental verification is conducted on the prediction task of COVID-19 real daily confirmed cases in the world and the United States with uncertainty, realizing the short-term and long-term prediction of COVID-19 daily confirmed cases, and verifying the effectiveness and accuracy of the suggested prediction method, as well as reducing the hysteresis of the prediction results.
Cognitive Granular-Based Path Planning and Tracking for Intelligent Vehicle with Multi-Segment Bezier Curve Stitching

Unmanned vehicles are currently facing many difficulties and challenges in improving safety performance when running in complex urban road traffic environments, such as low intelligence and poor comfort performance in the driving process. The real-time performance of vehicles and the comfort requirements of passengers in path planning and tracking control of unmanned vehicles have attracted more and more attentions. In this paper, in order to improve the real-time performance of the autonomous vehicle planning module and the comfort requirements of passengers that a local granular-based path planning method and tracking control based on multi-segment Bezier curve splicing and model predictive control theory are proposed. Especially, the maximum trajectory curvature satisfying ride comfort is regarded as an important constraint condition, and the corresponding curvature threshold is utilized to calculate the control points of Bezier curve. By using low-order interpolation curve splicing, the planning computation is reduced, and the real-time performance of planning is improved, compared with one-segment curve fitting method. Furthermore, the comfort performance of the planned path is reflected intuitively by the curvature information of the path. Finally, the effectiveness of the proposed control method is verified by the co-simulation platform built by MATLAB/Simulink and Carsim. The simulation results show that the path tracking effect of multi-segment Bezier curve fitting is better than that of high-order curve planning in terms of real-time performance and comfort.