Showing papers in "International Journal of Service Science, Management, Engineering, and Technology in 2020"

Deep Learning in Plant Diseases Detection for Agricultural Crops: A Survey

Abstract: Deep learning has brought a huge improvement in the area of machine learning in general and most particularly in computer vision. The advancements of deep learning have been applied to various domains leading to tremendous achievements in the areas of machine learning and computer vision. Only recent works have introduced applying deep learning to the field of using computers in agriculture. The need for food production and food plants is of utmost importance for human society to meet the growing demands of an increased population. Automatic plant disease detection using plant images was originally tackled using traditional machine learning and image processing approaches resulting in limited accuracy results and a limited scope. Using deep learning in plant disease detection made it possible to produce higher prediction accuracies as well as broadened the scope of detected diseases and plant species considered. This article presents a survey of research papers that presented the use of deep learning in plant disease detection, and analyzes them in terms of the dataset used, models employed, and overall performance achieved.

Agile Innovation: The Complementarity of Design Thinking and Lean Startup

Abstract: In light of digital transformation and a stronger application of artificial intelligence, many firms try to increase the agility of their innovation processes. In this regard, they particularly rely on design thinking or on the lean startup approach to reduce some of the deficits of established innovation processes, such as the stage-gate model. This conceptual article shows that merely focusing on design thinking and lean startup in isolation will not enable companies to fully leverage the benefits of agile innovation. Because of the complementarity of design thinking and lean startup, executives should simultaneously pursue these approaches in order to achieve results that are more than the sum of isolated design thinking or isolated lean startup initiatives. This complementarity derives from the specific benefits of design thinking in the front end of the innovation process combined with the particular benefits of lean startup in the back end.

Impact and Role of Motivation Theories in Continuous Improvement Environments: A Reflection of Literature

Abstract: A large number of the well-performing organizations in the world are believed to have better environments that produce highly motivated employees to attain their goals. As noted in the various literatures reviewed in this study, improved working conditions in companies, such as Dell Technologies and Apple Inc., are responsible for the overall organizational success within the highly contested market. When employees are motivated, they also work towards improving the conditions within the workplaces, so the relationship between the two is mutual. In addition, pay satisfaction, job design, and internal communication of an organization contributes to its workforce motivation, which results in improved productivity for the entire organization. On the other hand, highly motivated employees encourage organizational managers to offer better payment to their organizations, to attain satisfactory job design, and to improve internal communication. Even though this study utilized few articles in the literature review, its findings significantly contribute to the modern literature. However, future studies should consider using more materials and multiple variables to improve the quality and reliability of the outcomes.

Service Sector Performance, Industry and Growth in Nigeria

Abstract: This article extends previous empirical studies on service-industrial sector interactions and their impact on growth. It provides evidence from quarterly time series data using OLS, from 2010 to 2016 to account for new subsectors introduced from 2010 following the rebasing of the Nigerian economy. The article employs a disaggregated model to capture the individual productivities of subsectors. Series stationarity was determined with the ADF and PP test, thereafter Johansen technique was applied. The results indicate that while both services and the industrial sector contributed significantly to the economic growth (GDP) of Nigeria, some subsectors i.e. public administration, professional, scientific and technical services, transport (road, rail, pipeline, air, water), utilities (electricity, gas, and water supply, sewage, waste management) were found to be deficient. Finally, this article draws some policy implications to further strengthen the service and industrial sectors so as to maximise the potentials therein through the prescription of sector-specific policies.

An Overview of the IoT Coordination Challenge

Abstract: The Internet of Thing has been identified as one of the emerging technologies in IT. It interconnects and integrates large numbers of digital and physical entities by capability of appropriate information and communication technologies, to enable building enormous useful and unimaginable services and applications. However, building new IoT services or applications is a fastidious task since it is faced to several challenges such as interoperability, context-awareness, discovery, availability, decision-making. In this article, the authors are interested in coordination challenges that are still open despite the efforts of international organizations and scientific research groups. In fact, the authors outline a recent literature review of existing IoT coordination approaches. In the literature, researchers tend to use orchestration or choreography as a way to meet this challenge. A classification and the vision on this topic are presented. The authors propose an approach that is more likely to respond to the co-ordination challenge.

The Legal Dilemma in Governing the Privacy Right of E-Commerce Users: Evidence From the USA Context

Abstract: Protecting users' privacy rights has become a great challenge during the age of technological advancement in areas of digital media and digital communication, such as the internet and e-commerce. Dissemination of personal data over networks has become quite easy, widespread, and uncontrollable. This has created various concerns for online consumers in regard to privacy breaches and made it quite difficult for current regulations and statutes to address data confidentiality violations in many national states. Therefore, the paper discusses one of the contemporary challenging issues: the challenge of new technology and e-commerce to the right to privacy. The aim of this paper is to investigate the implementation of the right to privacy and the effectiveness of the current USA legal system in governing online transactions by drawing on the various notions embedded in the concept of privacy in general and e-privacy in particular. The method adopted for the legal perspective is case studies, where the USA legal context will be explored.

Comprehensive Contemplation of Probabilistic Aspects in Intelligent Analytics

Abstract: In Big Data analysis, the application of machine learning has proven to be a revolutionary. The systematic review of literature shows that research has been carried out on the domain of big data analytics particularly text analytics with the inclusion of machine learning approaches. This extensive survey deals with the data at hand that provides different ways and issues while combining the machine learning approaches with the text. During the course of the survey, various publications in the field of synchronous application of machine learning in text analytics were searched and studied. Classification framework is proposed as the contribution of machine learning in text analytics. A classification framework represented the various application areas to motivate researchers for future research on the application of two emerging technologies.

Ensuring the Success of Management Accounting Change in IT Departments of Public Organizations

Abstract: Organizational changes in information technology (IT) departments of public organizations are a frequent phenomenon. In contrast to private companies or public administrations, public organizations have some unique characteristics. There is, however, only little research available on how these characteristics need to be considered when analyzing organizational changes. Based on four organizational attributes affecting IT accounting change, this contribution applies a new, holistic framework to a case study. This framework allows in-depth insights into IT management accounting departments to systematically reveal the effects that contribute to a successful organizational transformation. Future research in the field of change in IT management accounting of public organizations will have benefits from applying this new framework to analyze practical examples and, from this, to develop valuable recommendations both for researchers and practitioners. KEywORDS IT Management Accounting, Organizational Transformation, Public Sector, Single Case Study, StateOwned Enterprise

Economic Decision-Making in Private Corporations Versus Public Sector: How to Compare Both Sectors

Abstract: This research takes a comparative analysis approach to study the process of economic decision-making within the private sector and the public sector. There are four main research objectives that guided this article. First, it aims to identify the different kinds of decision-making methods. Second, this article analyzes the economic decision-making processes that stakeholders have to make in public and private firms. Third, this r seeks to illustrate that establish effective decision-making and financial performance relate. Lastly, the article will offer effective economic decision-making procedures in private and public organizations, so as to make recommendations and to guide these businesses. To do so, there is a literature review in this research to find the best economic decision-making processes. Data collection tools were created in reference to the literature review that directed the structuring of the variables, and the study based the quantitative analysis on the adopted descriptive methodology. The sample was comprised of 100 respondents from China, and since 95% responded, that was a total of 95 responses. Based on the formulated study hypothesis and the research objectives, the collected data was examined for descriptive and inferential statistical analysis. In general, the findings showed that cost-benefit analysis was the favored economic evaluation method, and the respondents specified that they their internal and external economic decisions directly influence the company's operations. When focusing on how organizational performance is affected by effective economic decisions, the findings established that there was a key component for a better economic analysis outcome in the public and private firms: accounting information. Additionally, evaluating the number of processes in public and private firms led to findings that revealed the following: every decision in the public sector requires many approvals. These approvals greatly hinder economic decisions and decision-making. Social, cultural, and environmental aspects influence the decision process significantly, so they must be addressed immediately.

Developing the Assessment Questions Automatically to Determine the Cognitive Level of the E-Learner Using NLP Techniques

Abstract: The key objective of the teaching-learning process (TLP) is to impart the knowledge to the learner. In the digital world, the computer-based system emphasis teaching through online mode known as e-learning. The expertise level of the learner in learned subjects can be measured through e-assessment in which multiple choice questions (MCQ) is considered to be an effective one. The assessment questions play the vital role which decides the ability level of a learner. In manual preparation, covering all the topics is difficult and time consumable. Hence, this article proposes a system which automatically generates two different types of question helps to identify the skill level of a learner. First, the MCQ questions with the distractor set are created using named entity recognizer (NER). Further, based on blooms taxonomy the Subjective questions are generated using natural language processing (NLP). The objective of the proposed system is to generate the questions dynamically which helps to reduce the occupation of memory concept.

Identification of Telecom Volatile Customers Using a Particle Swarm Optimized K-Means Clustering on Their Personality Traits Analysis

Abstract: This research uses the telecom customers personality traits (extraversion, agreeableness, and neuroticism) to identify the volatile customers that always use the negative word of mouth (NWOM) in communications with others. Hence, a combination of text analysis and a personality analysis tool has been used to determine the customers personality factors from their chatting textual data, A particle swarm optimized k-means was used in the clustering process. The results provide an overview on how a chatbot conversation text represent the customer behavior. Optimizing the k-means cluster using partial swarm achieves a higher accuracy than using the traditional clustering technique.

A Survey on Blood Image Diseases Detection Using Deep Learning

Abstract: Blood disease detection and diagnosis using blood cells images is an interesting and active research area in both the computer and medical fields. There are many techniques developed to examine blood samples to detect leukemia disease, these techniques are the traditional techniques and the deep learning (DL) technique. This article presents a survey on the different traditional techniques and DL approaches that have been employed in blood disease diagnosis based on blood cells images and to compare between the two approaches in quality of assessment, accuracy, cost and speed. This article covers 19 studies, 11 of these studies were in traditional techniques which used image processing and machine learning (ML) algorithms such as K-means, K-nearest neighbor (KNN), Naïve Bayes, Support Vector Machine (SVM), and 8 studies in advanced techniques which used DL, particularly Convolutional Neural Networks (CNNs) which is the most widely used in the field of blood image diseases detection since it is highly accurate, fast, and has the least cost. In addition, it analyzes a number of recent works that have been introduced in the field including the size of the dataset, the used methodologies, the obtained results, etc. Finally, based on the conducted study, it can be concluded that the proposed system CNN was achieving huge successes in the field whether regarding features extraction or classification task, time, accuracy, and had a lower cost in the detection of leukemia diseases.

Deploying Cloud-Based Healthcare Services: A Holistic Approach

Abstract: Healthcare of individuals is very important; hence, the healthcare data needs to be managed very professionally. These requirements have become more stringent with the population aging and the growing attention to healthcare by the people. Cloud computing has emerged as a prominent solution to the computing demands of healthcare organizations, and a number of cloud-based solutions are available in the market. However, different users of the healthcare systems have different expectations from the cloud, and these must be taken into account while migrating to cloud. This article presents the implications of cloud computing solutions for the present day healthcare scenario. Prominent cloud-based healthcare services have been presented along with their specific applications. Considering the different users of healthcare systems, a holistic approach has been presented for the deployment of a cloud-based healthcare system. The deployment approach is suitable for all different levels of the healthcare organizations.

Organizational Design for Performance Management in State-Owned Enterprises

Abstract: Countries all over the world established state-owned enterprises to ensure improved efficiency and governance. Namibia is no exception, and with the government's desire for betterment of its citizens, many SOEs were established. After two decades of independence with the realization that state-owned enterprises (SOE) were not performing well due to mismanagement, red tape, and corruption, the State Owned Enterprises Governance Act 2006 was promulgated. This act aims to provide efficient governance and monitor performance in line with the government's aspirations of augmenting SOEs performance. Adopting a mixed cross-sectional descriptive case study research design, the aim of this article is to analyze and explore the influence of selected organizational design variables on strategy implementation. Further, within the context of resources-based view and dynamic capabilities of the firm, it aims to understand the influence of performance management on overall organizational effectiveness on a service sector SOE and thereby provide a basis for optimum performance and organizational effectiveness.

Distributed Approach to Process Satellite Image Edge Detection on Hadoop Using Artificial Bee Colony

Abstract: The remote sensing domain has witnessed tremendous growth in the past decade, due to advancement in technology. In order to store and process such a large amount of data, a platform like Hadoop is leveraged. This article proposes a MapReduce (MR) approach to perform edge detection of satellite images using a nature-inspired algorithm Artificial Bee Colony (ABC). Edge detection is one of the significant steps in the field of image processing and is being used for object detection in the image. The article also compares two edge detection approaches on Hadoop with respect to scalability parameters such as scaleup and speedup. The experiment makes use of Amazon AWS Elastic MapReduce cluster to run MR jobs. It focuses on traditional edge detection algorithms like Canny Edge (CE) and the proposed MR based Artificial Bee Colony approach. It observes that for five images, the scaleup value of CE is 1.1 whereas, for MR-ABC, it is 1.2. Similarly, speedup values come out to be 1.02 and 1.04, respectively. The algorithm proposed by authors in this article scales comparatively better when compared to Canny Edge.

A New Intra Fine-Tuning Method Between Histopathological Datasets in Deep Learning

Abstract: This article presents a new fine-tuning framework for histopathological images analysis. Despite the most common solutions where the ImageNet models are reused for image classification, this research sets out to perform an intra-domain fine tuning between the trained models on the histopathological images. The purpose is to take advantage of the hypothesis on the efficiency of transfer learning between non-distant datasets and to examine for the first time these suggestions on the histopathological images. The Inception-v3 convolutional neural network architecture, six histopathological source datasets, and four target sets as base modules were used in this article. The obtained results reveal the importance of the pre-trained histopathological models compared to the ImageNet model. In particular, the ICIAR 2018-A presented a high-quality source model for the various target tasks due to its capacity in generalization. Finally, the comparative study with the other literature results shows that the proposed method achieved the best results on both CRC (95.28%) and KIMIA-PATH (98.18%) datasets.

Applying Interval Fuzzy Petri Net to Failure Analysis

Abstract: In this article, the authors propose a new approach for modelling and failure analysis by combining the graphical representation provided by Petri nets and fuzzy logic. The graphical method is used for describing the relationship between conditions and events. The use of Petri nets in failure analysis enables replacing logic gate functions in fault trees. The Fuzzy logic technique allows natural language descriptions of process entities as well as an if-then rule-based definition of production. In addition, this study devises an alternative, a trapezoidal graph method in order to account for failure scenarios. Examples validating this novel method in dealing with failure analysis are also provided.

Withdrawal Prediction Framework in Virtual Learning Environment

Abstract: Making the most from virtual learning environments captivates researchers, enhancing the learning experience and reducing the withdrawal rate. In that regard, this article presents a framework for a withdrawal prediction model for the data of the Open University, one of the largest distance-learning institutions. The main contributions of this work cover two main aspects: relational-to-tabular data transformation and data mining for withdrawal prediction. This main steps of the process are: (1) tackling the unbalanced data issue using the SMOTE algorithm; (2) voting over seven different features' selection algorithms; and (3) learning different classifiers for withdrawal prediction. The experimental study demonstrates that the decision trees exhibit better performance in terms of the F-measure value compared to the other tested models. Furthermore, the data balancing and feature selection processes show a crucial role for guiding the predictive model towards a reliable module.

Trust Management: Social vs. Digital Identity

Abstract: Trust and identity are the fundamental issues to both the social as well as digital environments. An individual or a group require both of these identities to recognize, interact, and communicate in the present day social and digital worlds. In the social environment, the concept of trust and identity are different than in digital environments, but without a clear sense of identity, there can be a no ground for building the trust. Trust is helpful in supporting the identity to survive and to build relations with other identity in a particular environment. Trust management on the other hand provides a basis to establish the trust and ensure its continuity and longevity. This article compares the social and digital identities with respect to trust issues in the present-day digital scenario. Characteristics, identification process, and lifecycle of both the identities have been presented along with the threats. The work is very helpful in mapping the social scenario to the digital scenario.

Humanitarian Efficiency and Role of Relief Workers: Testing a Competency-Based Approach

Abstract: The effectiveness of humanitarian assistance often depends on the effectiveness of the resources utilized such as predictive logic, relief partners, logistics technology, and relief personnel. Analysis of the most recent yet more vulnerable disasters have pointed out that the relief workers often created a difference. Lack of appropriate access to standardized models to train the relief workers has created a need to develop a competency model that can further be validated with the relief organizations to create a standard. The current study aims to develop and test a hypothetical model that proposes a relationship between the competencies of the emergency relief workers, job performance, and job satisfaction through empirical analysis of primary data. The study reveals a good relationship between the competencies, job performance, and job satisfaction and shows a significant impact of the three variables on each other. The study culminates into recommending the key findings of the personnel to be able to improve the efficiency of the relief operation.

Applying ISPAR Model of Service Dominant Logic on Mentoring a Part of Training and Development Function of HRM Functions

Abstract: This is a conceptual article written to apply I-S-P-A-R model which was presented in 2009 by research scholars Maglio, Vargo, Caswel and Spohrer on the Mentoring in Service Dominant Logic (SDL) perspective. The author has taken a deep insight of mentoring which is a part of training and development: a function of the Human Resource Management in Good Dominant Logic (GDL) perspective. For this research, a wide range of literatures is reviewed and many disciplines have been explored which include mentoring roles, need, responsibilities, and context. Here, it is worthy to mention that mentoring and supervision are two different terms and both have different roles, too. Roles of supervisors are: boss, teacher, evaluator, expert and counselor; whereas mentoring consisted of assisting, befriending, guiding, advising and counseling. In service science, all the service systems do not fulfill the requirement to be a service system. There is also presented I-S-P-A-R which stands for Interact-Serve-Propose-Agree-Realize model of service system interactions episodes. This model is applied on mentoring in SDL perspective. At the end of this article, a conclusion is drawn and areas for further research have been mentioned.

Smart Home in Private Households: Status Quo, Discussion, and New Insights

Abstract: The growing digital transformation creates new ways of living. In recent years, intelligent smart homes (SHs) have increased rapidly. The article gives a status quo overview of the SH literature with regard to the interactions between SHs and their end-users and structures the literature into three categories (SH systems, SH application areas, and SH end-users) and 15 corresponding concepts. The analysis implies that SH is a broad and relevant research topic with different subjects, research gaps, and emerging benefits but also with challenges for all the players in the SH market. SH success depends on many critical success factors (CSF), such as acceptance of usability or interface design. Implications for research and practice to meet these challenges are presented. Several future research directions are suggested.

Being Spiritually Green: Investigating the Impact of Spiritually Motivated Environmentalism on Green Purchasing Intentions

Abstract: The article theoretically explores and empirically examines the relationship between spiritually motivated environmentalism (SME) and green purchasing intentions (GPI). Also, the mediating role of psychographic variables, namely environmental self-efficacy (ESE), environmental locus of control (ELOC), and environmental empathy (EE), were tested on the SME and GPI. A total of 223 Indian respondents filled out the administered questionnaire to validate the hypothesis, and collected data were analysed using SEM and Hayes's Parallel Multiple Mediation Model. The effect of SME was found significantly positive on GPI through ESE, ELOC, and EE. The findings from the study indicate that spirituality motivates green buying among consumers. Also, green purchasing augments in presence of consumers' self-efficacy, locus of control, and empathy towards environment.

Bio-CAPTCHA Voice-Based Authentication Technique for Better Security and Usability in Cloud Computing

Abstract: Cloud computing has gained increased interest in the last few years, where an increasing number of providers are converging to such a promising platform. However, the security issues are still a big concern in the cloud, where authentication is a major one. Much research has been conducted to secure the authentication, where some of them used biometric features (fingerprint, face, and voice, etc.). In general, the biometric authentication techniques have a noticeable advantage compared to the traditional techniques because biometric features are hard to be altered or forged. Nevertheless, a new generation of attacks threatens the biometric security by using brute force approaches. This article proposes a nontraditional authentication technique that was called Bio-CAPTCHA. The proposed technique uses a random voice-based password challenge that dynamically changes every time the user tries to login, which promises to significantly decrease the possibility of unauthorized access. The conducted Experimental and theoretical analysis confirms the high-security level of the proposed technique.

An Efficient Methodology for Resolving Uncertain Spatial References in Text Documents

Abstract: In recent decades, all the documents maintained by the industries are getting transformed into soft copies in either structured documents or as an e-copies. In text document processing, there is a number of ways available to extract the raw data. As the accuracy in finding the spatial data is crucial, this domain invites various research solutions that provide high accuracy. In this article, the Fuzzy Extraction, Resolving, and Clustering (FERC) architecture is proposed which uses fuzzy logic techniques to identify and cluster uncertain textual spatial reference. When the text corpus is queried with a spatial-keyword, FERC returns a set of relevant documents sorted in view of the fuzzy pertinence score. Any two documents may be compared in light of the spatial references that exist in them and their fuzzy similarity score is presented. This enables finding the degree to which the two documents speak about a specified location. The proposed architecture provides a better result set to the user, unlike a Boolean search where the document is either rated relevant or irrelevant.

Modified Ranking With Temporal Association Rule Mining in Supply Chains

Abstract: This article deals with data mining applications for the supply chain inventory management. ABC classification is usually used for inventory items classification because the number of inventory items is so large that it is not computationally feasible to set stock and service control guidelines for each individual item. Moreover, in ABC classification, the inter-relationship between items is not considered. But practically, the sale of one item could affect the sale of other items (cross selling effect). Hence, within time-periods, the inventories should be classified. In this article, a modified approach is proposed considering both time-periods and cross-selling effect to rank inventory items. A numerical example and an empirical study with a data set are used to evaluate the proposed approach. It is illustrated that by using this modified approach, the ranking of items may get affected resulting in higher profit.

Tracking How a Change in a Telecom Service Affects Its Customers Using Sentiment Analysis and Personality Insight

Abstract: Tracking the effect of change a telecom service on customer feeling is an important process for telecom companies. As a result of tangible growth and large competition among telecom companies, customer retention and satisfaction are the most important challenges faced by telecom companies nowadays. Customer retention can be achieved by identifying the feeling of the telecom customers after changing service and take care of the customers by modifying the services that aren't accepted by its customers. Hence, this article was done by using a combination of four stages of: text pre-processing, personality analysis, sentiment analysis, and a chatbot system. This article shows the effect of using the personality traits, agreeableness and emotional range, with sentiment analysis to help reaching a full description of customer feel. Combining the sentiment analysis Naïve Bayes technique in the natural language processing and personality insights pre-learning stage and adding feedback using the obtained results achieves higher accuracy than using the traditional sentiment analysis techniques.

A Fuzzy Logic-Based Model for Classifying Software Modules in Order to Achieve Dependable Software

Abstract: In this study, a fuzzy logic-based framework has been proposed to predict the situation of the software modules in the earlier phases of software lifecycle. The proposed model has taken into account domain experts' opinions and the available state of different software metrics as inputs. On the basis of dependability measures of software, different modules have been ranked earlier in the development process. Effect of the modules on the reliability, security, and availability of software has been judged by the proposed technique based on Mahalanobis distance metric. The study of software dependability in early phase assists the software developers to take corrective actions, which leads to minimize the testing efforts as well as development time. The proposed technique has been implemented on the promise software engineering repository data set. Performance of the proposed methodology is promising in identifying the fault-prone software modules. The result has also been compared with some known methodologies.

Handoff Schemes in Mobile Environments: A Comparative Study

Abstract: Vehicular ad-hoc networks are one of the most popular applications of Ad-hoc networks, where networks are formed without any sort of physical connecting medium and can be formed whenever required. It is an area in networks that has enjoyed a considerable amount of attention for quite some time. Due to the highly mobile environment where these networks find their usability, it can be understood that there are a lot of problems with respect to maintaining the communication links between the moving vehicular nodes and the static infrastructures which act as the access points (AP) for these moving vehicular mobile nodes (MN). The coverage area of each AP is limited and as such, the connections need to be re-established time and again between the MNs and the closest accessible AP. Handoff is the process involved here, which deals with selecting the optimal APs as well as the best network available for data transmission. In this article, the authors compare various handoff methods and categorize them based on the different approaches they follow.

A Novel Approach for Solving a Fully Rough Multi-Level Quadratic Programming Problem and Its Application

Abstract: The most widely used actions and decisions of the real-world tasks frequently appear as hierarchical systems. To deal with these systems, the multi-level programming problem presents the most flourished technique. However, practical situations involve some the impreciseness regarding some decisions and performances; RST provides a vital role by considering the lower and upper bounds of any aspect of uncertain decision. By preserving the advantages of it, in the present study, solving fully rough multi-level quadratic programming problems over the variables, parameters of the objective functions, and the constraints such as rough intervals are focused on. The proposed approach incorporates the interval method, slice-sum method, Frank and Wolfe algorithm, and the decomposition algorithm to reach optimal values as rough intervals. The proposed is validated by an illustrative example, and also environmental-economic power dispatch is investigated as a real application. Finally, the proposed approach is capable of handling the fully rough multi-level quadratic programming models.