Miracle-dz/UniFAR-FTUs huggingface dataset:
Execution of Multi-Perspective Declarative Process Models Using Complex Event Processing

The Internet of Things (IoT) enables continuous monitoring of phenomena based on sensing devices as well as analytics opportunities in smart environments. Complex Event Processing (CEP) comprises a set of techniques for making sense of the behavior of a monitored system by deriving higher level knowledge from lower level system events. Business Process Management (BPM) attempts to model processes and ensures that executed processes con-form with a predefined sequence. In IoT scenarios frequently a large number of events has to be analyzed in real-time to allow an instant response. While BPM reaches its limits in such situ-ations, CEP is able to analyze and process high volume streams of data in real-time. The evaluation and execution of rules and models of both paradigms are currently based on separate formalisms and are frequently implemented in heterogeneous systems. The presented paper integrates both domains by proposing an execution approach for multi-perspective declarative process process models completely based on CEP. The efficiency of the combined paradigms is validated in an implemented demonstration with simulated and real-life sensor data.
A Multilabel Classifier for Text Classification and Enhanced BERT System

Received: 30 November 2020 Accepted: 25 March 2021 Now-a-day, a vast variety of reviews are published on the web. As a result, an automated system to analyze and extract knowledge from such textual data is needed. Sentiment analysis is a well-known sub-area in Natural Language Processing (NLP). In earlier research, sentiments were determined without considering the aspects specified in a review instance. Aspect-based sentiment analysis (ABSA) has caught the attention of researchers. Many existing systems consider ABSA as a single label classification problem. This drawback is handled in this study by proposing three approaches that use multilabel classifiers for classification. In the first approach, the performance of a model with hybrid features is analyzed using the multilabel classifier. The hybrid feature set includes word dependency rule-based features and unigram features selected using the proposed twophase weighted correlation feature selection (WCFS) approach. In the second and third approaches Bidirectional Encoder Representation from Transformers (BERT) language model is used. In the second approach, a BERT system is enhanced by applying max pooling on target terms which specify an aspect of a review instance and a multibit label is given as input to the BERT system. In the third approach, the basic BERT system is used for word embedding only and classification is done using multilabel classifiers. In all approaches, the label used for all training instances specifies aspects with its sentiments. The experimentation shows that the results gained using the system proposed in the first approach are comparable to the results gained using the BERT system. The experimental results depict that the Enhanced BERT system gives better results compared to the existing systems.
Design of a Laboratory Method for Rapid Evaluation of Experimental Flocculants

Reports of novel organic polymeric flocculants have become commonplace. The method used to test the effectiveness of these flocculants is most often the flocculation of a kaolin suspension in a jar test. The widely varying versions of this method that appear in the literature suffer from a range of weaknesses. The present research uses well-defined kaolin and confines testing to conditions in which the kaolin suspension is stable in the absence of a flocculant. The research examines all aspects of the conduct of the method, including clay dosing, mixing, settling time, and measurement to improve the sensitivity, reproducibility, and robustness of the method, and takes steps to avoid pitfalls that can reduce the validity of the method. Innovations include careful selection of the buffer system and instrument characteristics. Kaolin Clarification Effectiveness is introduced as a metric that gives a meaningful indication of the relative value of a novel flocculant while emphasizing the critical importance of.
GELATION OF SILK FIBROIN

Refined silk fibroin obtained from waste cocoons was dissolved in a mixture of ethanol and neutral salt (calcium chloride) solution and the solution thus obtained was centrifuged and then acidified with an organic acid after dialysis. The pH of the resulting solution was in the acid side of the isoelectric point (pl=3.8-3.9). Fibroin gelation was carried out in an air-conditioned room. The gel strength and gelling rate were investigated under different conditions such as pH and concentration of fibroin solution. Structural features were studied by electron microscopy and infrared spectroscopy.
Update on neuroimaging in non-Alzheimer's disease dementia: a focus on the Lewy body disease spectrum

Purpose of review An accurate differential diagnosis between Alzheimer's disease (AD) and non-AD dementia is of paramount importance to study disease mechanisms, define prognosis, and select patients for disease-specific treatments. The purpose of the present review is to describe the most recent neuroimaging studies in Lewy body disease spectrum (LBDS), focusing on differences with AD. Recent findings Different neuroimaging methods are used to investigate patterns of alterations, which can be helpful to distinguish LBDS from AD. Positron emission tomography radiotracers and advanced MRI structural and functional methods discriminate these two conditions with increasing accuracy. Prodromal disease stages can be identified, allowing an increasingly earlier diagnosis. Summary Neuroimaging biomarkers can aid in obtaining the best diagnostic accuracy in LBDS. Despite the main role of neuroimaging in clinical setting is to exclude secondary causes of dementia, structural and metabolic imaging techniques give an essential help to study in-vivo pathophysiological mechanisms of diseases. The importance of neuroimaging in LBDS is given by the increasing number of imaging biomarker developed and studied in the last years.
Unveiling the neuroimaging-genetic intersections in the human brain

Purpose of review The prevalence of new public datasets of brain-wide and single-cell transcriptome data has created new opportunities to link neuroimaging findings with genetic data. The aim of this study is to present the different methodological approaches that have been used to combine this data. Recent findings Drawing from various sources of open access data, several studies have been able to correlate neuroimaging maps with spatial distribution of brain expression. These efforts have enabled researchers to identify functional annotations of related genes, identify specific cell types related to brain phenotypes, study the expression of genes across life span and highlight the importance of selected brain genes in disease genetic networks. Summary New transcriptome datasets and methodological approaches complement current neuroimaging work and will be crucial to improve our understanding of the biological mechanism that underlies many neurological conditions.
Customer Churn - Prevention Model

The strategy of any organization is based on the growth of its customer base, and one of its principles is that selling a product to an existing customer is much more profitable than acquiring a new customer. However, this approach has several opportunities for improvement, since it usually has a totally reactive approach, which does not give opportunity to the areas specialized in customer experience and recovery, to give an effective response for that moment, since the customer is gone at the time of the intervention. This happens because usually a diagnostic analysis of customers who have stopped buying products or services in a defined period, commonly three (3) periods or months, is performed. This thesis work challenges the way to face this problem, and proposes the development of a complete solution, which does not focus exclusively on the prediction of churn, as is usually done in the state of the art research, but to intervene in different interactions that can be carried out with customers. The above focused not only to prevent customer churn, but to generate an added value of continuous improvement in sales processes, increase customer penetration, leading to an improvement in customer experience and consequently, an increase in customer loyalty. Dataset: DOI number or link to the deposited dataset in cases where the dataset is published or set to be published separately. If the dataset is submitted and will be published as a supplement to this paper in the journal Data, this field will be filled by the editors of the journal. In this case, please make sure to submit the dataset as a supplement when entering your manuscript into our manuscript editorial system. Dataset License: CC BY-NC
A Deep Learning Approach Combining CNN and Bi-LSTM with SVM Classifier for Arabic Sentiment Analysis

Deep learning models have recently been proven to be successful in various natural language processing tasks, including sentiment analysis. Conventionally, a deep learning model’s architecture includes a feature extraction layer followed by a fully connected layer used to train the model parameters and classification task. In this paper, we employ a deep learning model with modified architecture that combines Convolutional Neural Network (CNN) and Bidirectional Long Short-Term Memory (Bi-LSTM) for feature extraction, with Support Vector Machine (SVM) for Arabic sentiment classification. In particular, we use a linear SVM classifier that utilizes the embedded vectors obtained from CNN and Bi-LSTM for polarity classification of Arabic reviews. The proposed method was tested on three publicly available datasets. The results show that the method achieved superior performance than the two baseline algorithms of CNN and SVM in all datasets. Keywords—Sentiment analysis; Arabic sentiment analysis; deep learning approach; convolutional neural network CNN; bidirectional long short-term memory Bi-LSTM; support vector machine; SVM
Investigation of the Long-Term Antibacterial Properties of Titanium by Two-Step Micro-Arc Oxidation Treatment

Recently, biofilm formation caused by bacterial adhesion and colonization has been recognized as the major cause of failure in orthopedic and dental implant surgeries. In this study, a customized micro-arc oxidation (MAO) treatment technique was developed to obtain desirable antibacterial properties on Ti surfaces. The two-step MAO treatment was applied in the fabrication of specimens with Ag and with/without Zn in their surface oxide layer. In order to simulate practical usage, surface analyses and immersion tests were performed to evaluate the incorporation of Ag and Zn into the resulting oxide layer and ion release behavior, respectively. Additionally, the antibacterial properties of the specimens after long-term immersion in physiological saline were evaluated using Gram-negative facultative anaerobic bacteria. The MAO-treated specimens containing Ag and Zn exhibited excellent antibacterial properties against Escherichia coli, which were sustained even after 6 months of immersion in physiological saline to simulate practical usage. Moreover, the Ag ions released from the surface oxide indicate the antibacterial properties of the specimen in the early stage, while the release of the corrosion products of Zn demonstrates its antibacterial properties in the later stage.
Optimal Parallel Algorithms in the Binary-Forking Model

In this paper we develop optimal algorithms in the binary-forking model for a variety of fundamental problems, including sorting, semisorting, list ranking, tree contraction, range minima, and set union, intersection and difference. In the binary-forking model, tasks can only fork into two child tasks, but can do so recursively and asynchronously, and join up later. The tasks share memory, and costs are measured in terms of work (total number of instructions), and span (longest dependence chain). Due to the asynchronous nature of the model, and a variety schedulers that are efficient in both theory and practice, variants of the model are widely used in practice in languages such as Cilk and Java Fork-Join. PRAM algorithms can be simulated in the model but at a loss of a factor of $\Omega(\log n)$ so most PRAM algorithms are not optimal in the model even if optimal on the PRAM. All algorithms we describe are optimal in work and span (logarithmic in span). Several are randomized. Beyond being the first optimal algorithms for their problems in the model, most are very simple.
Socioeconomic and Geographic Variations that Impacts the Spread of Malaria

Malaria is a preventable disease that brings death to millions of people around the world. Among the victims of malaria epidemic, children under age of five are the most vulnerable. Malaria can influence where people live, work and also the family fertility decisions. In addition to the direct economic impact, Malaria can cause indirect socioeconomic impacts as it affects schooling, tourism, and sporting events. This epidemic has heavily impacted the developing countries, specially the ones that are in Sub-Sahara region of Africa. During our study, we explored the data on six indicators used by United Nations and World Heath Organization to evaluate the impact of Malaria and the Gross Domestic Product (GDP) data from chosen 34 countries. We conducted a cluster analysis on countries based on the number of confirmed deaths due to Malaria and the GDP using multiple clustering algorithms. We observe a negative relation with GDP and number of Malaria deaths.
A Review on SDR, Spectrum Sensing, and CR-based IoT in Cognitive Radio Networks

The inherent scarcity of frequency spectrum, along with the fixed spectrum allocation adopted policy, has led to a dire shortage of this indispensable resource. Furthermore, with the tremendous growth of wireless applications, this problem is intensified as the unlicensed frequency spectrum becomes overcrowded and unable to meet the requirement of emerging radio devices operating at higher data rates. Additionally, the already assigned spectrum is underutilized. That has prompted researchers to look for a way to address spectrum scarcity and enable efficient use of the available spectrum. In this context, Cognitive Radio (CR) technology has been proposed as a potential means to overcome this issue by introducing opportunistic usage to less congested portions of the licensed spectrum. In addition to outlining the fundamentals of Cognitive Radio, including Dynamic Spectrum Access (DSA) paradigms and CR functions, this paper has a three-fold objective: first, providing an overview of Software Defined Radio (SDR), in which the architecture, benefits, and ongoing challenges of SDR are presented; second, giving an extensive review of spectrum sensing, covering sensing types, narrowband and wideband sensing schemes with their pros and cons, Machine Learningbased sensing, and open issues that need to be further addressed in this field; third, exploring the use of Cognitive Radio in the Internet of Things (IoT) while highlighting the crucial contribution of CR in enabling IoT. This Review is elaborated in an informative fashion to help new researchers entering the area of Cognitive Radio Networks (CRN) to easily get involved. Keywords—Cognitive radio; cognitive radio networks; software defined radio; spectrum sensing; machine learning; CR-based IoT
Short-Term Energy Consumption Forecasting at the Edge: A Federated Learning Approach

Residential short-term energy consumption forecasting plays an essential role in modern decentralized power systems. The rise of innovative prediction methods able to handle the high volatility of users’ electrical load has posed the basis to accomplish this task. However these methods, which mostly rely on Artificial Neural Networks, require that a huge amount of users’ fine-grained sensitive consumption data are centrally collected to train a generalized forecasting model, with implications on privacy and scalability. This paper proposes an innovative architecture specifically designed to overcome this need. By exploiting Federated Learning and Edge Computing capabilities, many Long Short-Term Memory (LSTM) models are locally trained by different users based on their own historical energy consumption samples. Such models are then aggregated by a specific-purpose node to build a generalized model that is re-distributed for improved forecasting at the edge. For better forecasting, our proposed local training procedure takes as input relevant features related to calendar (i.e., hour, weekday and average consumption of previous days) and weather conditions (i.e., clustered apparent temperature), and the architecture can group users according to consumption similarities (using K-means) or socioeconomic affinities. We thoroughly evaluate the approach through simulations, showing that it can lead to similar forecasting performance than a state-of-the-art centralized solution in terms of Root Mean Square Error (RMSE), but with up to an order of magnitude lower training time and up to 50 times less exchanged data when samples are recorded at finer granularity than one hour. Nonetheless, it keeps sensitive data local and therefore guarantees users’ privacy.
Study of the Stability of Fixed-Speed Induction Generators

This paper studies and compares the use of double-cage and single-cage models for rotor speed stability determination of fixed-speed induction generators and analyzes the influence of grid parameters and reactive power compensation. The study proves that the doublecage model must be used for fixed-speed induction generator simulation as the single-cage model gives erroneous results. The influence of the dynamic reactive power compensator position on fixed-speed induction generator stability is also studied, it is shown that the use of a dynamic compensator device improves the speed stability of the fixed-speed induction generators.
ADVANCES IN CYBER-PHYSICAL SYSTEMS Vol. 5, No.1, 2020 SYSTEMATIC MAPPING STUDY ON VERIFICATION AND VALIDATION OF INDUSTRIAL THIRD-PARTY IOT APPLICATIONS

The next industrial revolution commonly known as Industry 4.0 represents the idea of interconnected manufacturing, where intelligent devices, systems and processes exchange information, resources and artifacts to optimize the complete value-added chain and to reduce costs and time-to-market. Industrial software ecosystems are a good example how the latest digitalization trends are applied in the industry domain and how with the help of industrial IoT applications the production process can be optimized. However, the use of third-party applications exposes to a risk the systems and devices part of the manufacturing process. To address these risks a set of quality measures must be carried out in the ecosystem. This paper presents the results of a systematic mapping study carried out in the area of verification and validation of industrial IoT third-party applications. The goal of the study is to structure the scientific landscape and to provide an up-to-date snapshot of the current state of the research field.
A Review Study on the Design of an Exoskeleton Robot

Exoskeleton devices have a wide range of therapeutic and assistive utilities in life and they can provide essential support for limb movements with increased strength and endurance. This paper is presented to review the development of different approaches in controller design, actuation method, and transmission for exoskeletons. Here, the coupled nonlinear dynamics of the user and exoskeleton are a significant factor in developing and realizing effective control algorithms. So, after a short review of dynamic modeling of an exoskeleton, the application of some common controllers is provided. Also, due to the direct interaction of the user's body, it is necessary to utilize appropriate and convenient hardware components. In addition, a suitable combination of actuator and transmission systems can noticeably improve the performance, precision, and functionality of the robot. This review is represented to aid researchers in the fields of robotic therapy and exoskeleton devices in ascertaining their design process. Keyword: Exoskeleton; Control system; Actuator; Transmission.
Acquisition and User Behavior in Online Science Laboratories before and during the COVID-19 Pandemic

The COVID-19 pandemic has resulted in the closure of schools at every level, globally, forcing education to move online. Meeting the needs of students online for Science Lab classes, in particular, is a challenge since the physical labs are not available to the teachers or students. OLabs is a virtual Science Lab providing a complete learning environment of theory, experimental procedures, videos, animations, simulations, and assessments that capture real lab experiences with the relevant pedagogy. This study looks at the acquisition and behaviors of users, on the OLabs platform, during pre and COVID-19 times. Using Google Analytics, we observe that, during the pandemic time, users increasingly adopted OLabs as a new learning pedagogy for performing experiments as indicated by parameters like the number of users; the number of unique pages viewed per session; time spent on viewing content; bounce rate; and preference for content types such as theory, simulations, videos, and animations.
An Efficient Preprocessing-Based Approach to Mitigate Advanced Adversarial Attacks

Deep Neural Networks are well-known to be vulnerable to Adversarial Examples. Recently, advanced gradient-based attacks were proposed (e.g., BPDA and EOT), which can significantly increase the difficulty and complexity of designing effective defenses. In this paper, we present a study towards the opportunity of mitigating those powerful attacks with only pre-processing operations. We make the following two contributions. First, we perform an in-depth analysis of those attacks and summarize three fundamental properties that a good defense solution should have. Second, we design a lightweight preprocessing function with these properties and the capability of preserving the model’s usability and robustness against these threats. Extensive evaluations indicate that our solutions can effectively mitigate all existing standard and advanced attack techniques, and beat 11 state-of-the-art defense solutions published in top-tier conferences over the past 2 years.
EXPERIMENTAL AND NUMERICAL STUDY OF CAVITATION IN HEADFORM CONFIGURATIONS

1 Research Engineer, Hydro group, Aeronautical Systems, P.O. Box 2250; aviad.gofer@gmail.com 2 Research Engineer, CFD group, Aeronautical Systems, P.O. Box 2250; shlomy.shitrit@gmail.com A novel facility was constructed for investigating the motion of high-speed underwater bodies. The testing facility consists of a pneumatic piston capable of accelerating a specially designed underwater body to speeds as high as 30 m/s, hence achieving the cavitation range. The body maintains an approximately constant velocity for a few tens of milliseconds. Preliminary experimental work was performed to evaluate the performance of the novel testing equipment, using a high-speed camera to analyze different geometries at different velocities. Results showed good agreement in terms of cavitation bubble size when compared with numerical calculations using CFD.