Miracle-dz/UniFAR-FTUs huggingface dataset:
Towards Identification, Classification and Analysis of Hate Speech on Social Media

The use of Internet and Social Media has increased exponentially during the last few years globally giving the opportunity to interact with other people and share ideas, thoughts, opinions etc. Large-scale data are shared every day through social media platforms with enormous speed and reach an incredibly huge number of people. Additionally, the possibility of writing anonymous posts and comments makes it even more easy to express and spread hate speech. Social media platforms, in order to improve the experience of their users, are trying to eliminate comments expressing hatred. In this thesis, we mainly focus on developing automated techniques to identify hate-speech. We start with Traditional techniques used for hate-speech detection, followed by advanced techniques for identifying hate-speech on social media. Lastly we attempt to identify cases where these automated systems can fail miserable and propose technique to make these systems robust. Traditionally, various social media platforms, such as Wikipedia, Facebook, YouTube etc, employ hundreds of staff members as part of their human review team which manually read every reported post and decide if it is inappropriate for the users. Nowadays, having administrators detecting which comments are offensive or relying on user reports are ineffective methods not only due to the large-scale of data produces through social media and also due to the fact that during the last years an increase of hatred is noticed in the modern societies. As of 2018, there are about 317,000 status updates on Facebook every 60 seconds making it harder than finding a needle in a haystack every 60 seconds! We commence our research work with the problem of hate-speech detection on social media. Popular techniques like handcrafted feature-based Machine Learning and rule-based approaches have their own disadvantages. The former method is hard to scale and generalize, while the latter fails to provide good results making it unreliable for use in production level systems. Motivated by the improvements in the computational resources and availability of large annotated datasets, we work towards building deep learning based approaches for hate-speech detection. We propose composite models which utilizes the capability of deep models for generating concise representations along with the power of traditional machine learning classifiers. Every word has at least one meaning when it stands alone. But the meaning can change depending on context, or even over time. A sentence full of neutral words can be hostile (“Only whites should have rights”), and a sentence packed with potentially hostile words (“F**k what, f**k whatever y’all been wearing”) can be neutral when you recognize it as a Kanye West lyric. Motivated by this idea, we then move towards building techniques that utilize the contextual information instead of using just the content for identifying hate-speech. We propose a Context-based neural network architecture that utilizes the
A Study of the Behaviour of the Mouth-Parts of Mosquitoes when taking up Blood from Living Tissue ; together with Some Observations on the Ingestion of Microfilariae.

(1939). A Study of the Behaviour of the Mouth-Parts of Mosquitoes when Taking Up Blood from Living Tissue; Together with Some Observations on the Ingestion of Microfilariae. Annals of Tropical Medicine & Parasitology: Vol. 33, No. 3-4, pp. 259-278.
Cross-level invariance in multilevel factor models

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Effect of Platelet-Rich Plasma on MMP-13, ARE and TGF β1 in MIA-induced Osteoarthritis in Rats

Ankle osteoarthritis (OA) is an inflammatory deterioration chronic disease; yet, OA pathogenesis is obscure. There is no absolute cure from OA and the present pharmacological medication options are constrained and associated with adverse aspect effects. Clinically, Platelet-rich plasma (PRP) is usually used to cure OA. The present study was designed to evaluate the role of PRP in the treatment of experimentally Monosodium iodoacetate (MIA)-induced ankle osteoarthritis in the rat model. Thirty male Wistar rats were divided into three groups (each of 10 rats). Rats of group I were injected with 1 mg MIA in the right ankle joint for two consecutive days, while those of group II were treated with saline instead of MIA; and group III (osteoarthritic +PRP) rats were injected with PRP in the ankle joint at 14, 21, and 28 days after MIA injection. Paw oedema, scoring of arthritis, Matrix metalloproteinase 13 (MMP13) level, antioxidant response element (ARE) level, and joint transforming growth factor beta1 (TGF β 1) were evaluated. PRP reduces expression of joint, MMP13, ARE level, and TGF β 1. PRP also significantly reduces the score of arthritis. The administration of PRP decreases paw oedema in MIA-induced OA rats. These results suggest that PRP has marked effect against OA in MIA-induced osteoarthritic rats which are mediated through the anti-inflammatory and antioxidant effects.
Interpretation of Modified Glucose Tolerance Test among Pregnant Malaysian Women

Aim: Is to find the mean and two standard deviation of the serum blood sugar among pregnant women while running the modified oral glucose tolerance test (MOGTT) as screening for gestational diabetes mellitus (GDM) & to compare the readings with other protocols adopted in diagnosing GDM. Method: A cross sectional study among pregnant women running routine MOGTT at 24-28 weeks’ gestation. A total of 149 women participated in 4 months period. The test included 5 ml of venous blood sample taken after fasting for 8 hours and a second blood sample 2 hours after having 200 ml of 75 g glucose solution within 10 minutes. Results: The mean for the fasting blood sugar is 4.32 mmol/L±0.52 making value of 2SD of 5.36 mmol/l. The mean of the 2 hours glucose level was . 11mmol/l±1.38 making the 2SD value of 8.87 mmol/l. Conclusion: Our results for the 1st reading in MOGTT is near to the value of the local protocol in diagnosing GDM. The 2 hours postprandial reading in the local protocol is fairly low when compared with our findings & with guidelines of nearby communities.
OBJECT DETECTION USING VIOLA JONES AND TEMPLATE MATCHING BASED ON STEREO CAMERA

This research developed a system that will be mounted on a humanoid robot to detect and retrieve an object in the form of cups, bottles, and cans to give to guests. The system has been created to identify objects through image image captured by the stereo camera Minoru. Object identification methods used in this research is the method of Viola and Jones method of the Template Matching with OpenCV 1.1 library. Minoru stereo camera also serves to determine the distance of the camera to the object to be detected. Results from this research is the system able to detect certain types of objects and object distance obtained from the cameras capture. The success rate for the detection of objects with Viola Jones method with a distance of 30 cm to 105 cm was 90%, with Template Matching method and the same distance then the success rate reaches 80%. The detection of one object among several objects for Viola Jones method with a distance of 45 cm to 105 cm was 90% and for the success of the Matching Template method reaches 60% while for the simultaneous detection of all three objects with a distance of 45 cm to 105 cm using the Viola Jones is 90 % and Template Matching method is 66%.
Modelling and Simulation of 3-DOF Lower Limb Rehabilitation Robot using Force Feed Forward Control

A R T I C L E I N F O A B S T R A C T Article history: Received: . 13 June, 2020 Accepted: 22 July, 2020 Online: 25 August, 2020 This research the authors design the intelligent control for 3-DOFs lower limb rehabilitation robot base on the complex dynamics equation. The Force Feed-Forward Method (FFM) is promote to control the of 3-DOFs lower limb rehabilitation robot including dynamics characteristics. The robot can sense the force of the therapist which exerted on the robot and patient’s leg, then produces necessary forces through joints at the hip, knee, and ankle. The force feedforward controller is used to compensate the force generated by the therapist to perform patient-active exercises. In this paper, firstly authors briefly introduce 3-DOFs lower limb rehabilitation robot, next the kinematics and dynamics equation of 3-DOFs lower limb rehabilitation robot established base on Lagrange-Euler method are presented, and then the control method is introduced. Last, the performance of the proposed control methods has been confirmed by numerical simulations of the robot in all three joints: hip, knee, and ankle.
Identifying Potential Clues On Covid-19 Through Coronavirus-Related Literature Using A Data-Driven Approach With The Help Of A Text Mining-Based Software, PredictSearch

- Huge amounts of scientific publications are produced daily in particular in many fields of medicine and biological science. Managing these data and deducing valuable information to identify significant clues to understand a physiological or pathological mechanism as well as to propose therapeutic solutions are urgently needed. Here we describe the use of a dedicated text mining-based software, PredictSearch (PS) to explore, through a literature survey, significant correlations between terms related to coronavirus infection. Our search highlighted some features of antiviral compounds such as chloroquine and glycyrrhizin and their impacts on apoptotic cell death, cell cycle and endocytic pathway in the course of coronavirus infection. In addition, the reported mechanisms through which the virus can avoid the interferon-induced antiviral state should pave the way to identify efficient therapies. This study demonstrates the importance of informatics tools such as PredictSearch dedicated to scientific literature survey to adapt previous knowledge to new health issues on a particular topical subject like Covid-19 pandemia.
A Prophylactic Effect of PNF Stretching on Mechanical Parameters of Deep Jump following Exercise-Induced Muscle Damage

Purpose: Prophylactic effect of PNF stretching is performed with an expectation to reduce the risk of eccentric exercise-induced muscle damage and enhance drop jump performance.The purpose of this study was to examine the effects of proprioceptive neuromuscular facilitation stretching combined with plyometric training on deep jump in non-athlete male students. Method: The study design was a single blinded, randomized, concurrent parallel trial. Forty-two non-athlete male students were volunteered to participate in this study. Participants were assigned into plyometric (n=21) and plyometric & PNF (n=21) groups. Maximum and minimum vertical displacements, flight time, joint power, maximum landing force, peak take off instantaneous velocity were measured at baseline and 48 hours following the plyometric exercise protocol. A Vicon (200 Hz) motion analysis system with six T-Series cameras and two Kistler force plates (1000 HZ) were used to record kinematic and kinetic data, respectively. A two-way repeatedmeasure ANOVA (group x DOMS) was used to analyze data. Results: There was a significant difference between the pretest and the posttest scores in the intervention group for maximum vertical displacement (p=0.028), flight time (p=0.042), and Power average (p=0.026). Conclusions: This prophylactic treatment on mechanical parameters of deep jump during timing was useful. Eventually, results suggest that preventative treatments can have a significant effect on maintenance functional parameters or even helping to slow exercise-induced muscle damage for alleviating its symptoms.
Teachers experiences of stress and their coping strategies during COVID - 19 induced distance teaching

Mastering distance teaching imposed by the COVID-19 pandemic was challenging for many teachers. In the present cross-sectional survey, we assessed the level of stress that teachers experienced during the lockdown of schools in Germany, their strategies to cope with it, and external and internal barriers for distance teaching with an online questionnaire. Teachers were recruited for the study on the basis of nationwide professional networks (e.g. Eduserver – Education in Germany, The German Education Union (GEW)) as well as by advertising the study on homepages of associations for different special educational needs and in social networks (e.g. Facebook, Instagram). A total of 380 teachers from different school forms participated. They experienced medium to high levels of stress. More than 50 percent of them spent more than four hours daily on remote teaching, with secondary grammar school teachers experiencing significantly more stress and working more hours daily than special education teachers. The vast majority of them experienced technical barriers, but most of them felt able to cope functionally with the stress. Female teachers experienced significantly more stress, but coped with it more often in a functional way; teachers used more functional coping strategies when they expected external factors as barriers for distance teaching. The results imply that teachers’ digital skills should be developed, schools should be better equipped with the necessary computer hard- and software, and more research on psychological factors contributing to teachers’ willingness to use technologies for remote teaching in the pandemic and beyond should be done.
The Brazilian Institutional Environment: The Impact on Companies’ Strategies in the Oil and Gas Sector

Institutional theory explains how interactions between organizations and institutions shape economic activity and consequently managerial performance and strategies. The institutional setting of most emerging countries is in transition, whereby a relationship-based transaction mode is gradually yielding to rule-based transactions. This study investigates how the institutional environment in Brazil influences the strategy of oil and gas companies, based upon a case study approach to exploration and production in the oil and gas sector. The results obtained from secondary data and interviews with executives of the main Brazilian oil and gas companies and members of public organizations demonstrate that institutions have a limited influence on the decisions of these companies, since the regulatory dimension plays the biggest role in decision making.
Role of Machine Learning Algorithms Intrusion Detection in WSNs: A Survey

Machine Learning is capable of providing real-time solutions that maximize the utilization of resources in the network thereby increasing the lifetime of the network. It is able to process automatically without being externally programmed thus making the process more easy, efficient, cost-effective, and reliable. ML algorithms can handle complex data more quickly and accurately. Machine Learning is used to enhance the ability of the Wireless Sensor Network environment. Wireless Sensor Networks (WSN) is a combination of several networks and it is decentralized and distributed in nature. WSN consists of sensor nodes and sinks nodes which have a property of self-organizing and self-healing. WSN is used in other applications, such as biodiversity and ecosystem protection, surveillance, climate change tracking, and other military applications.Now-a-days, a huge development is seen in WSNs due to the advancement of electronics and wireless communication technologies, several drawbacks like low computational capacity, small memory, and limited energy resources infrastructure needs physical vulnerability to require source measures where privacy plays a key role.WSN is used to monitor the dynamic environments and to adapt to such situation sensor networks need Machine Learning techniques to avoid unnecessary redesign. Machine learning techniques survey for WSNs provide a wide range of applications in which security is given top priority. To secure data from attackers the WSNs system should be able to delete the instruction if any hackers/attackers are trying to steal data.
Analysis of scholarly productivity of the academic staff of East African School of Library and Information Science (EASLIS), Makerere University

Scholarly productivity measures the quantity of research outputs in peer reviewed journals and scholarly presentations in conferences or other similar gatherings of an individual or an institution. The study analyzed the scholarly productivity of the academic staff of East African School of Library and Information Science (EASLIS) for the last 15 years; 2004 to 2019. The objectives of the study were: to establish the scholarly productivity of EASLIS academic staff for the last 15 years (2004 to 2019), to understand the various bibliometric tools used in determining scholarly productivity, and to explore the bibliometric indicators in Publish or perish (PoP) software for the analysis of scholarly productivity. Mixed research method was adopted. Google scholar as a bibliometric tool and PoP software were used to retrieve the data of the 18 academic staff that were included in the study. The findings show that a total of 147 publications were made by 18 EASLIS academic staff from 2004 to 2019; with cumulative citations of 952; h-index of 44 and g-index of 81. Generally, PhD holders at EASLIS were found to have more impact in terms of scholarly productivity than their counterparts without PhDs, thereby making greater contributions towards the overall intellectual capital of the University. It is recommended that scholars publish their works in visible journals such that their scholarly works do not go unnoticed. Academic institutions are also encouraged to embrace the use of bibliometric tools such as Google scholar and PoP software to establish scholarly productivity and impact of their academic staff, and to use such statistics in guiding the recruitment and promotions of their academic staff.
From Brooklyn Barbers To Movie Stars: Using Introductions To Construct Embeddings Of People

People are central to narrative texts such as news; a given news story can contain mentions of hundreds of distinct individuals, some famous and some not. We introduce a novel method for representing person entities in text, by using introductions across a large news corpus as input to an embedding model. We demonstrate the utility of the model, Per2vec, for the task of named-entity linking of people mentions to a knowledge base, showing that the method is particularly suited for the under-explored task of recognizing when a person does not appear in the knowledge base. We compare Per2vec’s results to a state-of-the-art system on a challenging dataset and outperform it, and present two case studies.
The Relationship between Vascular Endothelial Growth Factor A (VEGFA) Gene Polymorphism and Pre-Eclampsia in Egyptian Pregnant Women

Pre-eclampsia (PE) is a complicated heterogeneous disorder associated with an increased risk of maternal death. There is a major variation in the literature about the genetics of pre-eclampsia (PE). This study aims to estimate the association between Vascular Endothelial Growth Factor A (VEGFA) gene single nucleotide polymorphism (rs1570360) and the development of PE in the Egyptian population. Genotyping was performed using the tetra-primer amplificationrefractory mutation system (ARMS-PCR) and the results were confirmed by the PCR-RFLP method in 100 patients with PE and 100 PE-free patients. Results revealed that GG (wild type) genotype of rs1570360 was identified in 100 patients, (42%) of them had pre-eclampsia. SNP genotype AA was found in 84 patients and (42%) of them had pre-eclampsia. The frequency of AG genotypes was significantly low (16 %). However, the odds ratio was 40.37 for homozygocity and 9.5 for heterozygous alleles. Our study concluded that the AA and AG genotypes were not associated with PE. There is a significant relationship between the AA allele polymorphism and the severity of PE.
Double layer back propagation neural network based on restricted Boltzmann machines for forecasting daily particulate matter 2.5

Particulate matter 2.5 (PM 2.5 ) pollution is an actual problem in the modern world and forecasting of the daily concentration of PM 2.5 is a challenging task for researchers. In this study, a novel neural network model that effectively forecasts daily PM 2.5 in Hangzhou city was developed in the form of a restricted Boltzmann machines double layer back propagation neural network model (RBM-DL-BPNN). Air quality index, the air pollutants, e.g., particulate matter 10 (PM 10 ), PM 2.5 , SO 2 , CO, NO 2 , O 3 , and meteorological parameters (temperature, dew point, humidity, pressure, wind speed, and precipitation) of Hangzhou city were used in this study to train and test three models: RBM-DL-BPNN, double layer back propagation neural network (DL-BPNN), and back propagation neural network (BPNN). The results of experiments and analyses performed indicate that RBM-DL-BPNN has a smaller mean absolute percent error (MAPE), smaller overall daily absolute percentage errors, and more results in terms of absolute percentage error within the range 0–50 % than DL-BPNN and BPNN.
DLL Side-loading: A Thorn in the Side of the Anti-Virus Industry

Dynamic-link library (DLL) side-loading is an increasingly popular cyber attack method that takes advantage of how Microsoft Windows applications handle DLL files. In such attacks, malware places a spoofed malicious DLL file in a Windows’ WinSxS directory so that the operating system loads it instead of the legitimate file. This paper describes the history of DLL Side-loading and its role in the malware and software engineering arenas. It also examines evolving trends along with similarities and differences between DLL Search-Order Hijacking, DLL-Hijacking, DLL pre-loading, and DLL side-loading. A technical analysis of the Trojan PlugX variant used to target Chinese political rights activists shows the DLL-side-loading technique in action. Finally, the paper recommends preventative measures to ensure that legitimate files are not exploited.