Reviewerly/exHarmony huggingface dataset:
Productivity and quality enrichment through multi criteria trajectory optimisation of an industrial robot

To foster the fourth industrial revolution, a lot of efforts are being created to maximising the production by optimising the present manufacturing processes. One in every of such try is done to optimise activities of robot manipulators in industries. In this work, two new variants of intelligent algorithms, specifically, canonical particle swarm optimisation with mutation operator (CMPSO) and self accommodative differential evolution (SADE) are proposed to optimise the trajectory of a robot manipulator (MTAB ARISTO 6XT) to reduce production cost. Optimal trajectory is generated from the information of internal structure of robots by considering robot mechanics and dynamics. A multi criteria cost function represents the production price. The algorithms produce cost effective and collision-free trajectories by considering knot points on the trajectory. A productivity and economic study was carried out. The economic benefits yielded by CMPSO and SADE are good. The results proved the goodness of the proposed algorithms.
Recommendations

Abstract At the end of this book, we conclude that innovative universities should work in different ways to reach the classification of innovative universities.
Financial Sector Development and Poverty Reduction Nexus: Evidences from Nigeria

Previous studies have examined the effect of financial sector development on poverty reduction.This study is unique by adopting the UNDP broader measure of well-being, the Human Development Index (HDI) to provide an argument for financial sector development process and poverty reduction interrelationship in Nigeria for the period 1988-2017.Stationary properties of the series were tested by the ADF unit root test.The paper uses the Johansen Cointegration test to examine the existence of long run relationship among the variables.Generally, it was found that financial sector development has both positive and significant relationship with HDI used as proxy for poverty level.The result also indicates a significant positive relationship between aggregate credit (AGC) and HDI.This shows that financial sector development which manifests in the ability of the banking sector to facilitate borrowing and investment in income earning assets, and stimulate the private sector, in particular, small and medium scale enterprises (SMEs), impacts positively on HDI.Finding also shows that aggregate deposit (AGD) (deposit opportunities available to deposit money banks) is negatively related to HDI and significant.This perhaps suggests that the volume of deposit mobilization by deposit money banks in Nigeria is relatively low to what is needed to transform the economy as well as the standard of living of the people.Realising that Human development is a desideratum and sacrosanct in poverty reduction, the objectives of policies aimed at further strengthening the banking sector should be people focused.
Measuring the Financial Performance of a Telecommunications Corporation

The aim of this paper is to measure the financial performance of the Emirates Telecommunications Group Company, the largest provider of internet and telephony services in the United Arab Emirates, during the years 2016 to 2018. This paper empirically analyzes three groups of indicators, profitability, liquidity and capital structure. Due to its high degree of validity in its ability to dissect and measure various aspects of a firm’s financial health, financial ratio analysis was performed using data provided by the Emirates Telecommunications Group Company in its annual reports. Ratio analysis has consistently been recognized as the gold standard for investorsinterested in analyzing the financial performance of large institutions. Relevant ratios that reflect various aspects of the firm’s financial health have been analyzedand compiled in order to reach a conclusion regarding the overall financial standing of Emirates Telecommunications Group Company. The findings indicate a noticeable improvement in the level of profitability in 2018 when compared to the previous years, possibly indicating a strengthening in the firm’s ability to control costs in its pursuit of higher profit margins. Liquidity, or cash on hand levels have slightly declined for the three years under study. The Profitability, liquidity and capital structure analysis clearly shows a stable and largely positive trend for the years 2016 through to 2018, highlighting an improvement in the firm’s management of resources and pointing towards a positive outlook for the company and good news for investors should current trends continue. Due to the public availability of the data required as well as the validity of this type of analysis, current and prospective investors alike are encouraged to employ ratio analysis in order to measure the viability of their investments as well as discover new investment opportunities where they may arise.
Introducing JSP and Tomcat

Web applications (together with apps) can be considered the cornerstone of the modern technology both in private and government organizations. In fact, by interacting with a remote server, it is possible to find the needed information or to purchase something online and, in general, to use many services for your job or your personal life.
REMOVAL ORANGE G DYE FROM AQUEOUS SOLUTIONS USING GRAPHENE OXIDE/MAGNESIUM OXIDE NANO COMPOSITE

Nanocomposite comprised of grapheme oxide (GO) nanosheets and magnesium oxide (MgO) nanoparticles were synthesized by a sol-gel method.The GO/MgONCs synthesized weight ratio of GO /MgO 1:1 The synthesized samples were characterized using (FT-IR,X-ray diffraction, AFM ,SEM) The results showed that MgO particles was decorated on GO.The adsorption of Orange G dye from aqueous solution onto GO/MgO NCs as adsorbent by batch method, under various experimental conditions including contact time , GO/MgO NCs dosage ,pH, temperature, and initial Orange G dye concentration was investigated .Adsorption isotherms been used to test the adsorption data for (Langmuir, Freundlich, Dubinin, Temkin) as it was fit to Langmuir isotherm and type S according to Gilles classification .Thermodynamic functions data such as (ΔH°, ΔG°, ΔS°) of the adsorption process were calculated, which show the adsorption endothermic process and the value of ΔG° was negative this indicate that the adsorption happened spontaneously while the value of ΔS° is positive that meaning the movement of dye molecules is unrestricted.Kinetic data were fit to pseudo-second order mode.
Feature Extraction for Face Recognition using Edge Detection and Thresholding

Face recognition is first and foremost step in video surveillance applications which include human behavioral analysis, event detection, border security and ATM banking. Most of the time, it is very difficult to get good facial features from the particular image frame and it often requires sophisticated algorithm for face identification and recognition. Robust face detection system is still a more challenging job because of complex environments including illumination changes, background clutter and occlusions. This article presents a novel feature extraction algorithm for face recognition using edge detection and thresholding. Initially, the incoming image is preprocessed to smoothen the image features and it is converted in to grayscale image to reduce the computational complexity of post processing steps. In feature extraction step, the image is completely iterated throughout the spatial coordinates and the edges are detected using thresholding technique. The optimum threshold for global thresholding is identified by calculating the maximum between-class variance in the given image. The extracted edge features are invariant under scale and illumination changes and thus it ensures the robust binary mask for face identification. Finally, the foreground features are obtained using morphological operations and the face is highlighted in subsequent incoming image frames. The proposed method can be deployed in public places such as malls, ATM centers and airports for security applications. Experimental results clearly indicate that the proposed approach works well under complex situations.
Exploration of Task Scheduling Algorithms in Cloud Computing Environments

Cloud computing is a model that provides computing environment where a shared pool of resources are provisioned to customers as a service via the internet. Task scheduling forms the essential part of cloud environment. The scheduling of task in cloud mainly focuses on reducing average waiting time, Makespan and maximizes resource utilization which in turn reduces the response time of the system. This paper study various scheduling algorithms in cloud computing environments.
Step 5: Text Mining and Recommender Systems

One of the key areas of artificial intelligence is natural language processing (NLP), or text mining as it’s generally known, which deals with teaching computers how to extract meaning from text. Over the last 2 decades, with the explosion of the Internet world and the rise of social media, there is plenty of valuable data being generated in the form of text. The process of unearthing meaningful patterns from text data is called text mining. This chapter covers an overview of the high-level text mining process, key concepts, and common techniques involved.
Integrated SURF and Spatial Augmented Color Feature Based Bovw Model with Svm for Image Classification

In this paper, Bag-of-visual-words (BoVW) model with Speed up robust features (SURF) and spatial augmented color features for image classification is proposed. In BOVW model image is designated as vector of features occurrence count. This model ignores spatial information amongst patches, and SURF Feature descriptor is relevant to gray images only. As spatial layout of the extracted feature is important and color is a vital feature for image recognition, in this paper local color layout feature is augmented with SURF feature. Feature space is quantized using K-means clustering for feature reduction in constructing visual vocabulary. Histogram of visual word occurrence is then obtained which is applied to multiclass SVM classifier. Experimental results show that accuracy is improved with the proposed method.
Generalized Functional Responses in Habitat Selection Fitted by Decision Trees and Random Forests

Species Distribution Models (SDMs) are important regression tools in the ecological sciences that can support distribution predictions using different environmental variables.Most of the research in the area of SDMs has assumed that regression coefficients in these models are fixed.However, species respond differently to different habitats depending on the habitat availability, meaning that regression coefficients change as functions of habitat availability, a phenomenon known as a functional response in habitat selection.The generalized functional response (GFR) is a varying-coefficient extension of the basic SDM framework, designed for more robust forecasts of species distributions in a rapidly changing world.The original GFR model formulated the varying regression coefficients using a polynomial function approach, which led to improvements of forecasting performance in many applications.The purpose of this paper is to improve the out-of-sample performance of the GFR model using a decision tree and Breiman's random forest algorithm.We compare the original GFR model with a decision tree and random forests using the GFR model by applying both models to a real population dataset on house sparrows.The results revealed a noticeable improvement in terms of out-of-sample 2 in the decision tree and the random forest approaches over the original GFR model.
Why Is Nigeria -- The “Giant of Africa” -- Still Poor?

This paper begins with a short review of the issues cited by Africa scholars to explain the relative lag in economic development in Sub-Saharan Africa, which includes Nigeria. Next, some vital statistics of Sub-Saharan Africa and Nigeria are provided, followed by a brief review of Nigeria’s pre- and post-independence history. The second part -- the heart of the paper -- examines the major long-term issues that are adversely impacting the growth in living standards in Sub-Saharan Africa, with particular attention to Nigeria: demography, urbanization, insufficient infrastructure, regional, ethnic and religious stresses, over-reliance on commodities to finance indispensable government services such as education and health, and the poisonous effects of widespread public and private corruption
Construction of load balancing scheduling model for cloud computing task based on chaotic ant colony algorithm

In order to overcome the problem of low scheduling balance and long time in traditional load scheduling model for cloud computing task, a load balancing scheduling model for cloud computing task based on chaotic ant colony algorithm is proposed. Task scheduling strategy is selected through task scheduling framework to achieve parallel task scheduling. Based on chaotic ant colony algorithm, cloud computing resources are deployed, and the load objective function of cloud computing task is constructed. Based on the constructed objective function, a load balancing scheduling model for cloud computing tasks is established, thereby achieving load balancing scheduling for cloud computing tasks. The experimental results show that the model has a high scheduling balance, the scheduling time is always less than 5 ms, and the scheduling efficiency is high. This model is more suitable for the balanced scheduling of cloud computing resources, which is feasible.
An approach towards development of a migration enabled improved datacenter broker policy

Cloud computinghas left its remarkable note on the computing world over the last few years. Through itseffectiveness, litheness, scalability &amp; availability cloud computinghas changed the nature of computer systemdeployment. The Quality of Service (QoS) of a cloud service provider (CSP) is an important element of research interestwhich includes different critical issues such as proper load, minimization of waiting time, turnaround time, makespanand suppressing the wastage of bandwidth of the system. The Datacenter Broker (DCB) policy helpsassigning acloudletto a VM. In present study, we proposed an algorithm, i.e., Migration enabled Cloudlet Allocation Policy(MCAP) for allocation of cloudlets to the VMs in a Datacenter by taking into accounttheload capacity of VMs andlength of the cloudlets. The experimental results obtained using CloudSim toolkit under extensive loads that establishperformance supremacy of MCAP algorithm over the existing algorithms.
A LEBERT-Based Model for Named Entity Recognition

Recently, many works have tried to augment the performance of Chinese named entity recognition (NER) using word lexicons. Because traditional named entity recognition models cannot integrate lexical information into embeddings, the LEBERT-BiLSTM-CRF model is proposed for elementary mathematics text NER. Lexicon Enhanced BERT(LEBERT) integrates external lexicon knowledge into BERT layers directly by a lexicon adapter layer. We can get the embedding after training the LEBERT model. Then put it into BiLSTM for feature extraction. Finally, the CRF is used for correction. Experiments on datasets show that LEBERT-BiLSTM-CRF outperforms BiLSTM-CRF baselines, and F1 scores reached 95.02%. Compared with other NER models, the LEBERT-BiLSTM-CRF model also performs better.
Information Extraction from Text using Text Mining

We're living in an technology of extended strain and intellectual problems. The extended diploma of strain &amp; force outcome in preference of to the form of people showing suicidal inclinations and therefore a larger shape of peoples are commit suicides pressure may be brought on able to family argument, task un satisfaction, healths troubles, and masses of others. inside the worldwide of modem computing, human beings feel loose to percentage their view and emotions over societal media for friends and family member via service together with text. to the kept nature and busy schedule of citizens it is pretty not easy to have interaction with pals and own relations people in individual, consequently community media structures are taken into consideration because of to the truth the maximum utilize platform for conversation. The purpose of this paper an estimate the suicidal incidents of a person thru using records mining technique to the textual content text somebody send to related humans. thru way of analyzing the additives of to the textual content messages we are capable of estimate the suicidal dispositions of a person without a doubt so important steps may be taken that allows you to keep the life of to the priority in this paper i referred to about text mining, tokenizations finding, emoji conversion, feeling assessment, estimation mining, KNNalgorithms
Advanced Tamil POS Tagger for Language Learners

In the emerging technology Natural Language Processing, machine translation is one of the important roles. The machine translation is translation of text in one language to another with the implementation of Machines. The research topic POS Tagging is one of the most basic and important work in Machine translation. POS tagging simply, we say that to assign the Parts of speech identification for each word in the given sentence. In my research work, I tried the POS Tagging for Tamil language. There may be some numerous research were done in the same topic. I have viewed this in different and very detailed implementation. Most of the detailed grammatical identifications are made for this proposed research. It is very useful to know the basic grammar in Tamil language.
Assessment of Student Translators' Texts from Nepali into English: Language Quality and Degree of Task Completion

This study attempts to assess the quality of English translations by English-major M.Ed.students specializing in Translation Studies.The study adopted the combination of error analysis and holistic method to assess the quality of target texts (TTs) elicited through the production task carried out by 30 purposively selected students.Findings show that most of TTs were undermined by grammatical errors and syntactic inaccuracies revealing student translators' substandard English competence.The study thus sees the urgency of incorporating English teaching into the translation course to strengthen student translators' production skill in English.
Hybrid Svd Model For Document Representation

Document clusters are the way to segment a certain set of text into racial groups. Nowadays all records are in electronic form due to the problem of retrieving appropriate document from the big database. The objective is to convert text consisting of daily language into a structured database format. Different documents are thus summarized and presented in a uniform manner. Big quantity, high dimensionality and complicated semantics are the difficult issue of document clustering. The aim of this article is primarily to cluster multi-sense word embedding using three distinct algorithms (K-means, DBSCAN, CURE) using singular value decomposition. In this performance measures are measured using different metrics.
Block chain Technology for Smart City and its Security Threats

The current modern year shows quick growth in metropolitanization and population growth of the world that causes various impacts on environmental, social and economic problems. The problems aroused in society influence the livelihood of the people and excellence of existence. “Smart City” passes many opportunities to resolve these urban inconveniences and unsolved issues. The chief fact of a smart city is to create the finest use of resources gathered from the public, make available with reliable services to the citizens with enriched quality and efficiently enhance the day to day life standard of peoples. In the smart city establishment Information and Communication Technology (ICT) shows greater impact. Block chain is a intensifying technology with many excellent aspects likely intelligibility, automation and security. A characteristic of block chain is supportive in improving the standard services and advances the expansion which applies information technology that integrates several areas of interest. Block chain manages the business, physical and social infrastructures to provide effective services to needed users. With the advancement of technologies namely interconnected networks, cloud computing and the Internet of Things (IoT), for smart cities can carry creative as well as novel solutions. Smart cities can offer people with interface and association between the general public as well as the local government. Despite several possible remunerations, digital interruption postures many objections related to privacy and along with information security. Here in this paper, we discussed in detail about blockchain technology and the security framework that assimilates the smart devices and blockchain technology together.
Conceptual Approaches of Prospective Pedagogy

The theoretical and methodological foundation of Prospective Pedagogy has become one of the key issues of contemporary pedagogy, when exploring various aspects of the science of education related to personality formation, to deal requiments for present and future. The analysis perspectives of the prospective education presented the degree of complexity in terms of the characteristics that were inserted in the research, as conditions of its conceptualization. Studies of general epistemology state four main conditions that a certain field of knowledge must meet in order to acquire the status of science: to have its own research object, to develop a conceptual and explanatory system; to have its own methods and techniques; investigation of the object of study reunited in a scientific methodology; and, to have a praxiology of the field, in the sense of influencing, directing, and controlling the phenomena it studies. Thus, it confirms the theoretical conceptualization of the paradigm of prospective education, as science of education.
A hybrid approach for preserving privacy for real estate data

In the present digital world, usage of the internet has increased many folds as users have become dependent on the cloud-based applications. The disclosure of personal information on such platforms becomes a prospective threat for an attack. Researchers have used randomised data distortion technique with addition of random noise to conceal the sensitive data from an unauthorised adversary. This perturbation technique has relevance for the numerical datasets only. In this paper, we propose a hybrid model of two phases encoding with additive random noise value for ensuring non-disclosure of private and sensitive information and maintaining an effective balance between data privacy and data utility. The proposed technique has been tested on different data sizes of the real estate industry in terms of efficiency and effectiveness in preserving privacy and data utility. The proposed algorithm has been evaluated in terms of privacy level and information loss. It has proved effective in comparison with other privacy-preserving techniques such as perturbation and encryption in terms of space complexity and efficiency.