Miracle-dz/UniFAR-FTUs huggingface dataset:
AIOPs based Predictive Alerting for System Stability in IT Environment

Many industries and organizations are moving away from legacy systems towards digital transformation to optimize their business processes. Artificial intelligence for IT operations (AIOps) plays a pivotal role in digital transformation. AIOps platforms utilize a large amount of data coupled with classical machine learning and cutting-edge analytic technologies. This will boost IT operations with proactive dynamic activities. The Micro Focus Operations Bridge (OpsBridge) monitors the health and performance of the systems in the infrastructure and applications across their IT environment and the hundreds of alerts are delivered to respective teams. These huge number of alerts create an alert noise. In this paper, we present an AIOps based automated predictive alerting system using logistic regression to monitor the system environment and reduce the alert noise. This predictive alerting will identify abnormalities in operational data and raise an alert on these abnormalities that could potentially impact an application or service.
Topic Categorization of Tamil News Articles

This project aims to develop a model in deep learning for ‘TOPIC CATEGORIZATION OF TAMIL NEWS ARTICLES’. The process of learning for regional languages such as Tamil, Hindi differs from that of English. The task of categorising Tamil news article by topic is a text classification task. Most of the applications, such as searching in web, filtering information, recognising language, readability rating, and analysis of sentiment, already use text categorization. These tasks heavily rely on neural networks. We did multiclass text classification in this study. Different techniques, ranging from machine learning to Deep Learning, are used to handle almost all NLP difficulties. Even so, language localization remains a mystery. For languages other than English, NLP issues are uncertain. Entity Extraction, prediction, classification, and OCR in sequence modelling are examples of difficulties. Because the number of individuals utilising local languages like Tamil, Hindi, Telegu in social media network is growing, it's critical to generate automatic classification process. The goal is to categorise Tamil news stories into similar subjects (Sports, Cinema, Politics). NB, CNN, SVM algorithms are implemented to classify the content of Tamil News articles based on the category done in the previous work. The performance of these algorithms are compared with RNN using Precision, Recall, F1-score are reported in this study.
A systematic review of fuzzy testing for information systems and applications

Fuzzy testing is a widely used software vulnerability automation detection technology with the advantages of easy deployment and lightweight, which can test the target software quickly and efficiently and has good adaptability to modern large-scale software. This paper first introduces the technical principles and classification of fuzzy testing, and then summarizes the four development stages of fuzzy testing from its introduction to the present. At present, fuzzy testing has been transformed from a single technology composition to a system engineering with multi-technology integration. In this paper, we will systematically review all the technologies that can be combined with fuzzy testing. Fuzzy testing has different requirement when applied in different fields. This paper focuses on the use and development of fuzzy testing in protocol and firmware vulnerability detection from a practical perspective and compares the advantages and disadvantages of various types of fuzzy testing tools. Finally, we analyze the current problems existing in fuzzy testing and hope to provide some references for this field.
Research of the Convolutional and Product Turbo Codes Characteristics in Conditions of Underwater Channel

Acoustic underwater communication is a promising direction in the development of communication systems with underwater objects, requiring the development of the applied signal code structures. This research aims to find the optimal turbo coding solution for improving noise immunity in underwater communications with orthogonal chirp modulation. This problem is relevant in several areas: for underwater monitoring, highly reliable communications with far remote subscribers as well as for the Internet of things – for normal functioning of ultra-low power water purity sensors.
AI Extension of SQuaRE Data Quality Model

Since machine learning AI is implemented as a software-intensive system, SQuaRE, an international standard for system and software quality, is appropriate as a framework to discuss its quality. Machine learning AI depends on the training data, but the SQuaRE data quality model of ISO/IEC 25012 does not take into account the characteristics of the training data. This paper reviews the issues that should be considered when constructing training data in the development of machine learning AI, including the preparation of original raw data, the requirements-driven formulation of training data, the bias in data distribution, and fairness. Then, we propose a training data quality model as an extension of the SQuaRE data quality model. Specifically, we show the mapping of the SQuaRE data quality characteristics to those of the training data, and introduce two new data quality characteristics, Adequacy and Provenance.
Hollow-Core Microstructured Optical Fiber Based Refractometer: Numerical Simulation and Experimental Studies

In this paper, we numerically and experimentally propose a novel hollow-core microstructured optical fiber (HC-MOF) biosensor for refractive index determination. The sensing mechanism of the proposed sensor is based on photonic bandgap effect and the location of transmission maxima of the fiber, which is strongly depend on the liquid analyte RI filled in the fiber core. The proposed HC-MOF biosensor demonstrates the spectral sensitivity of 5636.3 nm/RIU with a RI detection range of 1.333 to 1.3385 for different ratios of plasma in blood serum in our experimental studies. The HC-MOF proposed here can detect similar liquid analytes with RI close to 1.33. The proposed sensor with a high sensitivity, ease of operation and the possibility of real-time sensing has a strong potential for detection of liquid analytes and biomolecules with possible applications in medicine, chemistry, and biology.
Edible Mushroom Identification Using Machine Learning

Mushrooms are fungi, and there are many different types of fungi, including moulds and crusts, but there has been minimal research on whether they are edible or dangerous. Learning machine will be able to classify if a mushroom is poisonous or not using data mining as one of the approaches for obtaining computer-assisted knowledge. Naive Bayes, Decision Tree (C4.5), Support Vector Machine (SVM), and Logistic Regression are four comparison of the finest categorization techniques in data mining currently available. The research approach employed was an experiment with a WEKA-assisted tool that was used to compare the four algorithms. The Lepiota mushroom and Agaricus data were used to conduct the testing. Source of the dataset used is collected from the Kaggle website. The C4.5 algorithm, however, has the maximum level of accuracy compared to the other algorithms, according to the results of the testing, from the speed aspect it is noted that the C4.5 algorithm is faster than the other algorithms. This algorithm results in showing maximum accuracy of 93.34%
Clock Domain Crossing Verification Challenges

Clock Domain crossing is one of the critical aspects of chip design. As the technology node shrink, more IPs can be squeezed into single chip, the chip architecture complexity also increases with more features included. Hence, clock domains crossing probability also increase as more clocks domains co-exists within single chip. The need of clock domain crossing verification also increases and no longer enough to be simple design review and verification. The intend of this paper is to provide an overview of clock domain crossing verification challenges and watchouts. Hopefully it will benefit the community.
Inserção de Agilidade nos Processos de Teste de uma Organização de Software MPS.BR: Um relato de experiência

Software testing processes are usually affected by requirements and risks changing requirements throughout the software project. Changes must be done with flexibility and agility without disrupting effort, test coverage and software quality. Therefore, this paper describes our experience on introducing agility in software testing processes into a MPS.BR organization level C. The preliminary results indicate that using appropriate standards for the construction of Test Cases and Scripts promotes reuse, and increasing the project visibility improves process understanding between development and testing teams. Resumo. Processos de testes de software são usualmente afetados pela necessidade de pronto atendimento a mudanças de requisitos e sua adequação ao conjunto de riscos do projeto. Atender a estas mudanças com flexibilidade e agilidade, sem afetar o esforço, cobertura de testes e a qualidade final do produto é um desafio. Assim, relatamos nesse trabalho nossa experiência na introdução de agilidade em processos de testes de software em uma organização nível C MPS.BR. Os resultados obtidos até o momento indicam que o uso de padrões adequados para construção dos Casos e Roteiros de Teste promove seu reuso, e o aumento de visibilidade do projeto melhora o entendimento do processo entre as equipes de desenvolvimento e testes.
Separators in Continuous Petri Nets

Leroux has proved that unreachability in Petri nets can be witnessed by a Presburger separator, i.e. if a marking $\vec{m}_\text{src}$ cannot reach a marking $\vec{m}_\text{tgt}$, then there is a formula $\varphi$ of Presburger arithmetic such that: $\varphi(\vec{m}_\text{src})$ holds; $\varphi$ is forward invariant, i.e., $\varphi(\vec{m})$ and $\vec{m} \rightarrow \vec{m}'$ imply $\varphi(\vec{m}'$); and $\neg \varphi(\vec{m}_\text{tgt})$ holds. While these separators could be used as explanations and as formal certificates of unreachability, this has not yet been the case due to their worst-case size, which is at least Ackermannian, and the complexity of checking that a formula is a separator, which is at least exponential (in the formula size). We show that, in continuous Petri nets, these two problems can be overcome. We introduce locally closed separators, and prove that: (a) unreachability can be witnessed by a locally closed separator computable in polynomial time; (b) checking whether a formula is a locally closed separator is in NC (so, simpler than unreachability, which is P-complete). We further consider the more general problem of (existential) set-to-set reachability, where two sets of markings are given as convex polytopes. We show that, while our approach does not extend directly, we can efficiently certify unreachability via an altered Petri net.
"Out of band" authentication using an Android device

The focus of this thesis is to address the fact that people have very little control of the process when authenticating themselves to an electronic device. Usually a username and a password have to be provided during the process, and the user has no choice but to trust that this information will not be stolen or abused. One of the solutions to this problem is to use a device like a smartphone, which a user could employ to authenticate itself to any other device with internet access, without having to supply it with any personal information. This thesis analyses the solutions that employ this method, comparing them to my own implementation which uses an Android application to read a QR code and login.
BECCS capability of dedicated bioenergy crops under a future land‐use scenario targeting net negative carbon emissions

Bioenergy with Carbon Capture and Storage (BECCS) is a key component of mitigation strategies in future socioeconomic scenarios that aim to keep mean global temperature rise below 2°C above preindustrial, which would require net negative carbon emissions in the end of the 21st century. Because of the additional need for land, developing sustainable low‐carbon scenarios requires careful consideration of the land‐use implications of deploying large scale BECCS. We evaluated the feasibility of the large‐scale BECCS in RCP2.6, which is a scenario with net negative emissions aiming to keep the 2°C temperature target, with a top‐down analysis of required yields and a bottom‐up evaluation of BECCS potential using a process‐based global crop model. Land‐use change carbon emissions related to the land expansion were examined using a global terrestrial biogeochemical cycle model. Our analysis reveals that first‐generation bioenergy crops would not meet the required BECCS of the RCP2.6 scenario even with a high‐fertilizer and irrigation application. Using second‐generation bioenergy crops can marginally fulfill the required BECCS only if a technology of full postprocess combustion CO2 capture is deployed with a high‐fertilizer application in the crop production. If such an assumed technological improvement does not occur in the future, more than doubling the area for bioenergy production for BECCS around 2050 assumed in RCP2.6 would be required; however, such scenarios implicitly induce large‐scale land‐use changes that would cancel half of the assumed CO2 sequestration by BECCS. Otherwise, a conflict of land use with food production is inevitable.
A neighbor's trust based model for prevention of black hole attack in wireless modile ad hoc networks

Implementing Ad hoc networks are becoming very prevalent during recent years. Security is the most important issue for developing mobile ad hoc networks (MANETs). They expose to various kinds of attacks because of their unique nature in which every node can easily join to network or leave it. Black hole attack is the most probable attack in MANET. In this research we proposed a model for prevention of this attack. It judges on route replies coming from the intermediate node based on a trusted third party which is the destination node. If the source node received an acknowledgement on the route replies sending by an intermediate node, from destination during a specific time, it decides that the path is safe and intermediate node is not malicious. Meanwhile a counter will be set for counting the number of times that each intermediate node introduced a wrong route reply. Every node that proposes a wrong route reply will be recorded in a black list. The process also will be checked for all one hop neighbors of the suspicious node and the history of these nodes will be gathered in the black list, if they proposed a wrong route reply during the route discovery process. When the counter for each node exceed from a specific value, the suspicious nodes will be introduced as black holes and an alarm will be notified to all nodes in the network to remove these malicious nodes from their routing tables. Experimental results show that this model proposes a high rate of packet delivery ratio, with 88% improving network throughput compared with a network exposing in attack situation and decreasing the rate of end to end delay with 38% less than attack situation.
Design and Numerical Simulation of Experimental System for Multiple Elastic-plastic Impacts

A set of experimental system is designed for testing the sub-impact behavior and the systematic dynamical responses in the flexible impact process.The numerical 3-D dynamic finite element simulations show that the experimental system can excite multiple elastic-plastic impacts and effectively simulate the sub-impacts of several typical impact systems.The influence of the materials and the supporting ways of the test beam on the impact process is simulated and the performance index for testing the impact force and beam displacement are determined.
STRUCTURE DAMAGE IDENTIFICATION RESEARCH OF WIND TURBINE BLADE BASED ON THE AXIAL MODE SHAPE DIFFERENCE

Space structure damage identification is a relatively complex process,it has to consider three dimensional vibrations of the structure.In order to identify the structure damage of wind turbine blade,a new standard which is named the axial mode shape difference is put forward for the complex space structure of wind turbine blade.The wind turbine blade structure models of no damage and damage is established by ANSYS,and the influence disciplines of axial mode shape difference is researched under the different of wind turbine blade damage,the method is applied to recognize the damage location of wind turbine blade model.The result shows that the axial mode shape difference can identify damage location,a feasible method is put forward to identify the wind turbine blade structural damage by axial mode shape difference.
CJON BOOK EXCERPT SERIES

For decades, efforts to maintain adequate levels of RNs in the healthcare workforce have centered on recruitment. The current workforce challenges and opportunities related to the national shortage of RNs in health care are outlined in chapter 9. This chapter will focus on creating a culture of retention as one key strategy to decrease nurse turnover and cost. Surely the answer to RN vacancies lies in a strong and solid recruitment effort; however, once recruited, greater emphasis must be placed on retention. Consider that for every two nurses recruited, one or more could leave. Tens of thousands of dollars are spent each year on nurse advertising and recruitment strategies to hire RNs at all levels and across multiple specialties. Turnover comes at a high price to the organization and to the staff left behind who just spent weeks, physically and emotionally, precepting and acculturating the new hire in orientation and to the work environment. Nurse managers fi nd themselves facing an ever-challenging global nursing shortage. The current shortage of registered nurses in the United States is expected to intensify as baby boomers age and the need for health care grows (American Association of Colleges of Nursing, 2003). This shortage, to rival none in history, is sparked by a multitude of factors: aging nursing pool, fewer women choosing nursing as a career, and others outlined in chapter 9. To meet this challenge, healthcare employers have initiated multiple strategies, from foreign nurse recruitment and sign-on bonuses to creative benefi t packages, such as maid service, dry cleaning pick-up and delivery, and car washing. Although these tangible CJON BOOK EXCERPT SERIES
Exploring Visual Representations for Sign Language Translation

Sign Language Translation (SLT) task has been addressed in multiple approaches in recent years. In this work we aim to investigate the impact of using different types of visual sign language representation for SLT. For this investigation we use the state-of-the-art in SLT, the Sign Language Transformers model. We compare the translation output performance of two types of body pose estimation models as our skeleton extractor, and 2D CNN features trained on the test dataset. These later perform best, and I3D features outperform the pose estimation-based ones.
“The Induction Dose with Ketamine and Midazolam Co-Induction to Propofola”

Background: Propofol is a commonly used intravenous (IV) induction agent. The induction dose of propofol is 1.5 to 2.5 mg/kg in healthy adults producing unconsciousness, depending upon concomitant medications (i.e. opoid analgesics), the patient’s age and physical status, and the extent of surgical stimulation. Objective: To assess the induction dose with ketamine and midazolam co-induction to propofola. Methods: This was a prospective, randomized, double-blind and interventional study conducted at Department of Anesthesiology, Sher-e-bangla Medical College Hospital, Barishal, Bangladesh from January to June 2019. Sixty adult patients undergoing elective surgery to be performed under general anesthesia were randomized to receive 0.3 mg/kg of Ketamine or 0.03 mg/ kg of Midazolam intravenously as co-induction agent. A minute after administration of co-induction agent, anesthesia was induced with Propofol 40 mg bolus then 10 mg every 10 seconds until the loss of verbal response. The hemodynamic response at 0, 1, 2, 5 minutes respectively and the induction dose of Propofol were noted. Results: The mean arterial pressure heart rates were significantly lower at 1, 2 and 5 minutes in midazolam group. However, mean arterial pressure and heart were within the physiological range in both the groups. Propofol dose requirement for induction between the two groups was similar (p>0.05) but co-induction significantly decreased the induction dose of Propofol as compared to standard recommended dose for induction. Conclusion: Our study showed that hemodynamic variables were maintained within the physiological range with midazolam and ketamine co-induction. However, lesser degree of decrease in mean arterial pressure was seen with ketamine but the heart rate was higher. A similar reduction of induction dose of propofol was achieved with both the drugs.
Ensuring Privacy Preservation Access Control Mechanism in Cloud based on Identity based Derived Key

—Cloud computing is a dominant technology that involves massive amounts of data storage and access via the internet. Because there is a large amount of data stored in data centers, it is critical to implement appropriate access control mechanisms over data stored in a cloud. Today, there are numerous access control mechanisms available to provide conﬁdentiality, privacy, and data origin authentication in a cloud environment. The available access control techniques may have a higher computational overhead and lack security concerns. In this paper, we designed and implemented a privacy-preserving access control in cloud computing using derived key identity-based encryption. The proposed method may reduce computational overhead while generating the key while also increasing the robustness of cryptographic keys. During the key generation process, the trusted key canter (TKC) is involved. The experimental results show that the proposed method reduces computational overhead and provides an easy way to implement an access control mechanism in a cloud environment.
Prevalence and Human Papillomavirus Genotypes Distribution among HIV-Positive and -Negative Young Women Aged 20 to 37-Years-Old with Cervical Cancer in Abidjan, Côte d’Ivoire

Introduction: Human papilloma virus (HPV) infection is the major etiologic agent of cervical cancer. Both human papillomavirus (HPV) and human immunodeficiency virus (HIV) are classified as carcinogens. We describe the prevalence and distribution of HPV genotypes among HIV-positive and -negative women aged 20-37 years-old in Côte d’Ivoire. Material and methods: Women were recruited among those who come to the gynaecology department of the Military Hospital, Abidjan with a positive acetic acid test. HIV testing was performed on the basic of the Retrovirus Côte d’Ivoire testing algorithm. HPV DNA was detected with the Linnear Array Hpv Genotyping Test (Roche Diagnostics, Indianapolis, IN, USA). Results: A total of 105 women were recruited. DNA testing was done on 87 sample specimens who had a positive beta globin. Fifty-five women (55) were positive for HPV, giving an overall prevalence of 63.2%. The HPV rates was higher among HIV-positive women (39.1%) than HIV-negative women (24.1%). The most prevalent HPV were 16 (20%), 18 (12.7%). Infections with multiple HPV types were more common in HIV+ (45.5%) than HIV- (12.7%). HPV high risk (HR) accounted for 72.7% of the genotypes detected. Conclusion: Prevalence of HPV and HR types were high in young women. Screening strategies incorporating HPV genotyping and vaccination should be effective in preventing cervical cancer in both HIV-positive and -negative women living in Côte d’Ivoire.