Miracle-dz/UniFAR-FTUs huggingface dataset:
The Future of Customer Experience in the Information Age of Artificial Intelligence - Get Ready for Change

— In the growing global economy, where the total number of digital products and services sky rocket day-by-day, managing information technology systems, even for billion-dollar companies can be as challenging as it is complex. Enterprises are constantly seeking to accomplish the mission of enriching customers through outstanding service by supplanting legacy networking systems with today's most advanced technology solutions. In a strategically competitive business world, organizations need to be conscious about the real-time trends and behavior patterns of customers, which helps them to consistently deliver high quality goods and services addressing the key demands anytime and anywhere. Nowadays, with the innovation of technological breakthroughs such as data science, machine learning and artificial intelligence, offering excellent customer service to clients is not a daunting task, however, if the service is not delivered on time, it can result in a loss of opportunity and can therefore wipe out significant profit margins from such customers. In order to gain insights by processing the data in real-time, Information based decision-making strategy, which lies at the center of gravity offers the competitive advantage for several organizations, regardless of its size and business sector. In the era of artificial intelligence, human civilization is already making progress where one machine is sitting in front of the other machine reshaping organizations today from the core of its business processes into the future-generation intelligent platforms that essentially engages businesses to communicate with clients through enhanced, unique customer relationship channels. This article provides detailed information on how the entire customer engagement process is augmented into something productive and promising which truly allows businesses to upscale in terms of market share and return on investment utilizing the new human-to-machine communication network of digital assistants named “Chatbots” powered by artificial intelligence .
Near infrared and visible face recognition based on decision fusion of LBP and DCT features

Visible face recognition systems, being vulnerable to illumination, expression, and pose, can not achieve robust performance in unconstrained situations. Meanwhile, near infrared face images, being light- independent, can avoid or limit the drawbacks of face recognition in visible light, but its main challenges are low resolution and signal noise ratio (SNR). Therefore, near infrared and visible fusion face recognition has become an important direction in the field of unconstrained face recognition research. In order to extract the discriminative complementary features between near infrared and visible images, in this paper, we proposed a novel near infrared and visible face fusion recognition algorithm based on DCT and LBP features. Firstly, the effective features in near-infrared face image are extracted by the low frequency part of DCT coefficients and the partition histograms of LBP operator. Secondly, the LBP features of visible-light face image are extracted to compensate for the lacking detail features of the near-infrared face image. Then, the LBP features of visible-light face image, the DCT and LBP features of near-infrared face image are sent to each classifier for labeling. Finally, decision level fusion strategy is used to obtain the final recognition result. The visible and near infrared face recognition is tested on HITSZ Lab2 visible and near infrared face database. The experiment results show that the proposed method extracts the complementary features of near-infrared and visible face images and improves the robustness of unconstrained face recognition. Especially for the circumstance of small training samples, the recognition rate of proposed method can reach 96.13%, which has improved significantly than 92.75 % of the method based on statistical feature fusion.
Fractal‐based autonomous partial discharge pattern recognition method for MV motors

On-line partial discharge (PD) monitoring is being increasingly adopted to improve the asset management and maintenance of medium-voltage (MV) motors. This study presents a novel method for autonomous analysis and classification of motor PD patterns in situations where a phase-reference voltage waveform is not available. The main contributions include a polar PD (PPD) pattern and a fractal theory-based autonomous PD recognition method. PPD pattern that is applied to convert the traditional phase-resolved PD pattern into a circular form addresses the lack of phase information in on-line PD monitoring system. The fractal theory is then presented in detail to address the task of discrimination of 6 kinds of single source and 15 kinds of multi-source PD patterns related to motors, as outlined in IEC 60034. The classification of known and unknown defects is calculated by a method known as centre score. Validation of the proposed method is demonstrated using data from laboratory experiments on three typical PD geometries. This study also discusses the application of the proposed techniques with 24 sets of on-site PD measurement data from 4 motors in 2 nuclear power stations. The results show that the proposed method performs effectively in recognising not only the single-source PD but also multi-source PDs.
Implementation of DJ Rule Based Algorithm for Dhuni- Vishleshan of Compound Punjabi Words

Dhuni-vishleshan describes the process by which one word is broken into many words. It is the new software for Punjabi language. Various rules can be made and after that we can implement the words according to their rules. In Punjabi, words are a sequence of characters. There is a little amount of work is completed in this area. A word can be of two types-simple and compound. A simple word consists of roots. A compound word is also called as co-joined word can be broken up into two or more words. The problem to which this paper is concerned, is breaking up of Punjabi compound words into constituent words. In this paper, the rules for breaking the compound words into simple words have been applied. The problem of this paper is to break the compound word into constituent words with the help of rules of 'dhuni- vishleshan' in Punjabi.
INFLUENCE OF THE RESILIENCE FACTOR ON THE QUALITY OF ENGINEERING AND DESIGN PROJECTS IN SCHOOLS

The project-based approach to teaching in the context of engineering and design has become popular. The field of project management signals resilience competence as fundamental for 21st century leaders and for teams involved in complex projects, but there are still gaps in research in this area. This study then proposed to investigate the relationship between resilience and quality of engineering and design projects. The methodological approach mixes strategies of qualitative and quantitative research with students and teachers of private Brazilian schools that have a prototyping space to carry out this type of projects. The results point to positive correlations between resilience and project quality.
A Theoretical Review of CRM Effects on Customer Satisfaction and Loyalty

This paper has been devoted to theoretically examining the Customer Relationship Management (CRM) agenda which has been dominating the business world in recent years. Customer relationship management is a new competitive weapon for organizations for serving internal and external customers. Today’s organizations are focusing on conquering the minds of customers, to make them satisfied and loyal with the help of sophisticated, well-organized CRM efforts. This paper is to explore the CRM agenda in organizational interfaces and its effectiveness with regards to customer satisfaction and customer loyalty. The related literature demonstrates that there is a significant association among effective CRM, customer satisfaction and customer loyalty.
Monitoring System of Mechanical Activity of the Heart and Goelocalization of the Patient Based on the Mqtt Cloud

The queue of patients for consultation and appointments that we observe in our health centers is growing in size from day to day given the exponential growth of the human population. Medical systems based on telemonitoring have been developed to remotely monitor the health status of patients. In this work, we constructed a medical prototype for remote monitoring of the mechanical activity of the heart and for geolacalization of the patient based on Message Queue Telemetry Transport (MQTT). The prototype will be used by individuals or by health centers without a doctor. The geolacalization will enable us to locate the injured individual. The physiological data and GPS coordinates are collected using a portable kit and are transmitted directly to an MQTT cloud using a Wi-Fi hotspot. A web server hosted in the kit allows the user to enter the service set identifier (SSID) of the access point through a smartphone. MQTT cloud will take care of the storage, geolacalization and transmission of the signal from the mechanical activity of the heart to the doctors. Almost all smart terminals with a web browser can easily access data (smartphone, tablet, computer). Tests carried out on individuals reveal that the proposed prototype is reliable in sampling, real-time transmission and geolacalization in street map and imagery, which can help in diagnosis and rapid intervention.
Professional certifications and occupational licenses: evidence from the Current Population Survey

This article uses data from the Current Population Survey to analyze the role of professional certifications and occupational licenses in the U.S. labor market. It discusses the prevalence of these credentials among the employed by age, gender, race, ethnicity, educational attainment, and occupation. This analysis also explores the relationships between certifications, licenses, and earnings. Finally, the article presents new data on certification and licensing by detailed occupation and whether the credential is required for one’s job.
Teacher Readiness Level Facing Technology Demands, Pedagogical Skills and Development of School Culture in the 21st Century

: Teachers in the 21st century face more complex challenges and difficult work for them, for example: increasing diversity students and parents, higher demands for quality education, standards of teaching and learning processes with higher outcomes and others. This article aims to explain the importance of integrating the role of technology and pedagogical skills in the 21st century learning process and building a school culture that can shape the attitudes, personality, and character of students. The method used in this research is descriptive research. Descriptive research is research that is used to describe a situation or phenomena as they are. Data sources are obtained through several articles, journals, proceedings, and books. The results of this study show that teachers must have broader skills in teaching, managing classes effectively, building, developing, and managing relationships with students and the school community, believing the use of technology to be more effective in the learning process and building a good school culture to shape the morale of students.
The Evaluation Of Outdoor Learning Activities In Primary School

Outdoor learning has become an important part of modern education. It is used as an interactive teaching and learning method, which comes as a completion to formal education situations. It is mainly based on the development of transversal and key competencies, as well as the development of life-skills, which are the basics of modern educations systems and processes. Even if it is becoming more and more known and used in preschools and primary school, there are many unsolved questions about this topic. One of these is the matter of evaluation. Being of great value in the practical field, it is hard to find the right ways to evaluate the activities, the participants and the results. The main instrument used was the focus group, and it was realized among a number of preschool teachers and primary school teachers from Arad. Most of the interviewed teachers, had already been in contact with outdoor learning and its belonging activities but had questions regarding the evaluation methods and instruments they should use. This is very important because if there isn’t a proper evaluation, the method may not reach the wanted finalities. Also, we have presented some of the most often used evaluation methods and instruments of outdoor learning. © 2019 Published by Future Academy www.FutureAcademy. org.UK
A general multilevel estimation framework: Multivariate joint models and more

A tremendous amount of work has been conducted in the area of joint models in recent years, with new extensions constantly being developed as the methods become more widely accepted and utilised, especially as the availability of software increases. In this talk I will introduce work focused on developing an over-arching general framework, and usable software implementation called megenreg, for estimating many different types of joint models. This will allow the user to fit a model with any number of outcomes, each of which can be of various types (continuous, binary, count, ordinal, survival), with any number of levels, and with any number of random effects at each level. Random effects can then be linked between outcomes in a number of ways. Of course, all of this is nothing new, and can be done (far better) with gsem. My focus, and motivation for writing my own simplified/extended gsem is to extend the modelling capabilities to allow the inclusion of the expected value of an outcome (possibly time-dependent) or its gradient or integral or general function of it, in the linear predictor of another. Furthermore, I develop simple utility functions to allow the user to extend to non-standard distributions in an extremely simple way with a little Mata function, whilst still providing the complex syntax users of gsem will be familiar with. I’ll focus on a special case of the general framework, joint modelling of multivariate longitudinal outcomes and survival, and in particular discuss some of the challenges faced in estimating such complex models, such as high dimensional random effects, and describe how we can relax the normally distributed random effects assumption. I’ll also describe many new methodological extensions, particularly in the field of survival analysis, each of which is simple to implement in megenreg.
An Efficient Platform and Communication Architecture for Event-triggered Cyber-physical Systems

The interaction of modern cyber technologies with the physical world is widely anticipated to bring significant societal and economic benefits. These cyber-physical systems combine computation and communication technologies to monitor and control complex physical processes, and therefore make it possible to develop smart products and services in industries such as manufacturing, healthcare and agriculture. The dynamics of many physical processes are inherently unpredictable. This implies that cyber-physical systems must be responsive to non-deterministic events, while remaining energy-efficient for long-term operation and adaptable to the dynamics of the physical process under observation. However, these stringent requirements make it difficult to realize efficient event-triggered cyber-physical systems in practice. In order to overcome this practical limitation, this thesis presents a new platform and communication architecture for the construction of efficient event-triggered cyber-physical systems with respect to responsiveness, energy-efficiency and adaptability. We make four main contributions: • We introduce a new platform architecture for composing energyefficient wireless embedded platforms using a time-predictable processor interconnect called Bolt. The design and prototype implementation of Bolt is presented, which exhibits a throughput of up to 3.3 Mbps and a negligible power overhead of 1. 3μW during periods of inactivity. • We propose a novel system design methodology for realizing eventtriggered cyber-physical systems, subject to responsiveness, energy efficiency and adaptability design constraints. A prototype of a wireless acoustic emission sensing system is presented, which demonstrates significant improvements to the state-of-the-art with respect to the responsive and energy-efficient detection of acoustic events and their multi-hop dissemination. • We design and implement Zippy, an asynchronous flooding primitive for the rapid dissemination of rare events through a multihop network with unprecedented energy-efficiency. The developed prototype exhibits a dissemination latency as low as 24.4 ms for an 8-bit event packet through a 3-hop network, and a power dissipation of 9. 6μW during periods of inactivity.
Computational Prediction of N-linked Glycosylation Sites on Plant Proteins

Glycosylation is an important form of protein post-translational modification where a glycan is attached to a protein via an enzymatic process. Experimental verification of glycosylation using wet lab techniques is expensive and time-consuming. While a number of computational prediction tools are available, none are trained using plant proteins. Since the mechanisms of glycosylation in plant and animal cells are known to differ, there is a need to develop a plant-specific predictor. In this thesis, we create such predictors of N-linked glycosylation using support vector machines and binary profile patterns derived from protein sequence windows as input feature data. The final classifier achieves a recall of 80.0% and 79.0% precision, as measured using a 10-fold cross-validation test. Our plant-specific classifier is more accurate on plant proteins than are other classifiers developed here and elsewhere. Finally, we have developed a web server to make the tool available to the research community.
From Natural Resource Boom to Sustainable Economic Growth; Lessons for Mongolia

Some resource-rich developing countries are in the process of harnessing immense mining resources towards inclusive growth and prosperity. Nevertheless, tapping into natural resources could be challenging given the large front-loaded investment, volatile capital flows and exposure to global commodity markets. Public investment is needed to remove the often-large infrastructure gap and unlock the economic potential. However, too rapid fiscal outlays could push the economy to its limit of absorptive capacity and increase macro-financial vulnerabilities. This paper utilizes a structural model-based approach to analyze macroeconomic impacts of different public investment strategies on key fiscal and non-fiscal variables such as debt, consumption, sovereign wealth fund, and real exchange rates. We apply the model to Mongolia and draw policy recommendations from the analysis. We find that fiscal policy adjustment, particularly moderating infrastructure investment and optimizing investment efficiency is needed to maintain macroeconomic and external stability, as well as to boost the long-term sustainable growth for Mongolia.
A Semantic Network Analysis of 4th Industry Revolution: News Frame for on Drone, AI, Big Data and IoT News

This paper explores the news coverage frames of the ‘Fourth Industrial Revolution’ in news media. Through analyzing the themes like drone, AI (AI), big data and IoT (IoT), the study conducted an analysis to figure out the characteristics and trends of Korean news frame about the Fourth Industrial Revolution. For this purpose, we extracted related contents and keywords in Korean media from Jan 1st, 2012 to Dec 31st, 2016 on a yearly bias. The data was analyzed using Korean language analysis program, Textom, and network analysis program, NodeXL. According to the results, it was found that there were different frame types for the Fourth Industrial Revolution in each year period from 2012 to 2016. In specific, the focus of the keywords ‘drone’ and ‘AI’ shifted from an ‘attack’ frame and ‘leisure’ frame to an ‘industry’ frame, while keywords ‘big data’ and ‘IoT’ had no major changes and consistently maintained one frame. However, entering 2016, a new frame may be trending as new media vocabulary related to ‘big data’ and ‘IoT’ continues to emerge.
INFLUENCE OF COMMERCIAL BANKS’ COLLATERALS LENDING STRATEGY ON THE GROWTH OF SMES IN KENYA

Purpose: The purpose of the study was to establish the influence of commercial banks’ collaterals lending strategy on the growth of SMEs in Kenya. Methodology: The researcher used purposive sampling to select respondents. The sample size was comprised of 352 respondents. The study used questionnaires to collect data from the field. Both quantitative and qualitative data gathered was coded and analyzed using Statistical Package for Social Sciences (SPSS) computer software. Descriptive statistics was used to analyze the data in frequency distributions and percentages which were presented in tables and figures. Inferential statistics were used to analyze qualitative data. Results: The study found out that commercial banks’ collateral have a negative and significant effect on SMEs growth. (r=-0.96, p=0.000). Unique Contribution to Theory, Practice and Policy: The study also recommended that commercial banks to flex terms and conditions for credits for the SMEs. The study found out that an increase in collateral and covenants leads to a decline in performance of SMEs. Therefore, commercial banks need to make credit terms to be friendly to the creditors in this case the SMEs owners. Small businesses often have difficulty in providing sufficient and good-quality collateral to banks. The banks should typically agree to accept collateral in any term and also should be more inclined to accept the balance of a checking account, finished commodity, guarantees of another company or a bank and securities as collateral.