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Diagnostic theories are fundamental to information system (IS) practice and are represented as trees. While there are approaches for validating diagnostic trees, these validate the overall performance of the tree rather than identifying ways incorrect diagnoses can occur. It is important to fully validate diagnostic trees because even if the tree gives the correct decision “most of the time,” it is possible for incorrect decisions traveling down little-used branches of the tree to result in catastrophic decisions. In this article, we describe the process of using a variant of q-sorting to validate diagnostic trees. In this methodology, diagnostic trees that independent experts develop are transformed into a quantitative form, and that quantitative form is tested to determine the inter-rater reliability of the individual branches in the tree. The trees are then successively transformed to incrementally test if they branch in the same way. The results help researchers not only identify quality items for use in a diagnostic tree but also facilitate diagnoses of problems with those items and facilitate the reconciliation of discrepant trees by experts. The methodology validates not only the whole tree but also its subparts.

The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link. In large-scale multiobjective optimization, too many decision variables hinder the convergence search of evolutionary algorithms. Reducing the search range of the decision space will significantly alleviate this puzzle. With this in mind, this paper proposes a fuzzy decision variables framework for largescale multiobjective optimization. The framework divides the entire evolutionary process into two main stages: fuzzy evolution and precise evolution. In fuzzy evolution, we blur the decision variables of the original solution to reduce the search range of the evolutionary algorithm in the decision space so that the evolutionary population can quickly converge. The degree of fuzzification gradually decreases with the evolutionary process. Once the population approximately converges, the framework will turn to precise evolution. In precise evolution, the actual decision variables of the solution are directly optimized to increase the diversity of the population so as to be closer to the true Pareto optimal front. Finally, this paper embeds some representative algorithms into the proposed framework and verifies the framework’s effectiveness through comparative experiments on various large-scale multiobjective problems with 500 to 5000 decision variables. Experimental results show that in large-scale multiobjective optimization, the framework proposed in this paper can significantly improve the performance and computational efficiency of multiobjective optimization algorithms.

Providing a structured approach for complex product development (PD) is a challenging issue that necessitates utilizing sophisticated modeling tools and analysis techniques. One effective approach for optimizing the complex engineering system development is the cross application of modularization and sequencing analyses on the design structure matrix (DSM), which leads to a cost-effective and time-efficient PD process. In this regard, the current study presents an application-specific method for the simultaneous implementation of modularization and sequencing analyses on DSMs to find the most independent and excluded modules with the minimum feedback loops among them. To this end, we first formulate the total feedback length among the modules by introducing two new modules’ feedback criteria. Second, we propose a novel objective function combining these feedback criteria with the modularity index metric to simultaneously fulfill the sequencing and modularization goals. This objective function makes the proposed method application specific in the sense that the expert is able to adjust the importance weight of each criterion based on the project's characteristics. Third, an efficient metaheuristic algorithm named discrete particle swarm optimization simulated annealing (DPSOSA) is developed to find the optimal solution by minimizing the introduced objective function. Eventually, to evaluate the DPSOSA performance in different applications, we conduct comprehensive experiments on a total of 21 well-known real-world DSMs. The obtained results demonstrate the superiority of the proposed method over previous approaches proving its effectiveness in each of modularization, sequencing, and modularization-sequencing experiments.

PurposeThis study explores how and when intuitive forms of planning can be used in a family firm's succession process.Design/methodology/approachThe study uses an extended focus group meeting, consisting of individual, group and subgroup discussions with seven highly experienced external family business advisors in the Netherlands to gain a holistic understanding of the succession process and its underlying logic. The study also employs pre- and post-group questionnaires.FindingsThis study reveals that advisors perceive intuitive forms of planning as an integral part of the succession process, with the latter containing both intuitive and formal logic and activities. Both logics are used situationally and flexibly to deal with the uniqueness and unpredictability of the succession process and to build strong relations and manage relational dynamics in business families to address tasks, dilemmas and contingencies.Originality/valueThe succession process is an important part of business families' achievement of transgenerational intent. Creating commitment among potential successors begins when they are children, and understanding the role of the more intuitive forms of planning during the succession process will provide us with a more holistic perspective on its dynamics.

The interactive case study requires student teams to engage with the instructor using a structured decision analysis process in deciding whether to develop a new drug to treat blood clots in legs. There is role-playing in the interactive case study where student teams are decision consultants and the instructor serves as the decision maker, subject matter expert (SME), and coach. Student teams are responsible for managing the analytical process, framing the decision, collecting data from the SME (instructor), constructing the Excel model, assessing probabilities for the most-sensitive uncertainties from the SME, evaluating the Excel-based decision-tree model, and presenting evaluation results and recommendations to the decision maker (instructor). The goal of the case is to improve the analytical, modeling, and consulting skills of the students. The interactive case study is the culmination of a semester-long elective MBA course, entitled Decision Making Under Uncertainty. Since 2010, I have taught this course 31 times to 870 graduate students. History: This paper has been accepted for the INFORMS Transactions on Education Special Section on Cases Based on Real-World Projects from the INFORMS Journal on Applied Analytics. Supplemental Material: Supplemental materials are available at https://doi.org/10.1287/ited.2021.0262ca . The Teaching Note and solutions are available at https://www.informs.org/Publications/Subscribe/Access-Restricted-Materials .

The majority of projects—even ultimately successful ones—run into significant problems during their development. While organizations have a variety of mechanisms at their disposal to correct projects that are experiencing difficulties, one of the most radical is replacing the project manager. Replacing a project manager “mid-stream” involves a major change to an ongoing project with the potential benefits of onboarding an individual with a different perspective or set of managerial and/or technical skills. Using agency theory as our critical evaluative lens and a qualitative data collection methodology, we interviewed 19 key informants who had experience as part of project manager replacement efforts. This article reports of the dynamics of replacing project managers, identifying the critical decision criteria and mechanisms involved in such decisions. We found that three themes emerged with regard to project manager replacement decision making: 1) replacement is a common correction practice for troubled projects; 2) replacement is viewed by decision makers and team members alike as a message for change; and 3) in reestablishing processes and trust in governance, project size is an important moderator when deciding on a course of action. We finally propose a process model, based on our analysis, which identifies the critical antecedents, effects, and consequences of project manager replacement.

In the fashion industry, garment sizing is a challenging issue and involves different problems resulting in lost profits. The design of a sizing system is a complex task involving both anthropometric data and subjective data, such as comfort or fit. With the emergence of big data, companies can collect valuable information about their consumers and, thus, enhance their sizing systems. Although several methods have been proposed in the literature, they do not integrate consumer comfort and fit. The method proposed in this article aims to design a suitable sizing system, while taking into account both morphological features and consumer preferences. Consumer preferences are extracted from a satisfaction survey, and are then integrated into an optimization process. The main contribution of the proposed model is twofold: a comprehensive evaluation of sizing systems that are based on consumer preferences is proposed and an optimization algorithm is developed to perfectly fit the problem. The proposed system is implemented and evaluated on the measurement database of the CAESAR project and a satisfaction survey conducted by a French fashion retailer. A comparison with up to date techniques and the existing sizing systems demonstrates that our model provides significant improvements for consumer fit and coverage.

This article examines convergence by providing a systematic literature review using scientometric method. Despite the steady growth of convergence literature, we still have an incomplete understanding of convergence concepts as well as the processes of industry convergence (IC). Our systematic review highlights several key findings. First, our review highlights six clusters of research on convergence. Second, our analysis suggests that the process of convergence does not necessarily progress in the three- to four-step supply-side process as postulated in the literature, i.e., from scientific convergence, technology convergence, market convergence, and finally to IC. Although existing literature suggests that IC occurs as a result of supply-side convergence, we expand on this proposition and put forth market-driven convergence processes. Third, we provide a typology of the convergence concepts to bring clarity and avoid indiscriminate use of the various convergence concepts. Finally, our article offers new debates on the literature of convergence, providing scholars with research directions that extend beyond the standard convergence processes. Our systematic review is relevant to scholars and managers as the study highlights a taxonomy of convergence scholarship, a typology of convergence concepts, and a re-evaluation of the IC process model to drive the research forward.

Daily schedule recommendation is an intelligent approach to recommend multiple suitable activity locations and activity sequences for users based on their needs in a day. In such a scenario, training the model using traditional methods requires centralized data collection from individual users, which may be prohibited by data protection acts, such as GDPR and CCPA. In this paper, we address the problem of daily schedule recommendation utilizing the deep reinforcement learning model in a federated learning framework (FedDSR). And curriculum learning is applied to guide the training process towards better local optimization and better generalization. For the uploaded local parameters, a similarity aggregation algorithm is proposed to improve the quality of the model. The experimental results show that the proposed FedDSR model is superior and effective to multiple baselines on two real datasets Geolife and Chengdu. Comparing with baselines, our method not only ensures that the parties do not need to share data and thus achieve joint modeling, but also can exceed ~18% under evaluation metric perimeter and improve ~0.72% under evaluation metric ADTS.

Music is commonly regarded as one of the fine arts, but aesthetic responses to music are still poorly understood. The aim of this study was thus to shed light on the psychological process through ...

Numerous studies have shown that teachers significantly shape student’s learning context. Socially and emotionally competent teachers tend to develop supportive and encouraging relationship with their students by promoting intrinsic motivation, focusing on student’s strengths and abilities, mediating through conflict situations and encouraging appropriate communication and prosocial behaviour. Teachers with good communication skills will create a more successful teaching and learning ambience for the students, and without communication, the teaching and learning process will not take place. Educators, parents and public today recognize the need for an educational agenda to improve academic performance, but also to enhance students’ social and communicational competence. The aim of the present study was to examine social and communication competence of the students of educational sciences and teacher education at the University of Zagreb Department Of Croatian Studies. As part of the most important aspects of the future educational work, the participants pointed out independence at work, the awareness that they would contribute to the future of the society and the awareness that they play a useful role in society. Participants also expressed optimism about their communication skills with students, planning and organizing teaching process and maintaining discipline, and to some less extent their competences for successful cooperation with students’ parents. Results were discussed in terms of possibilities to contribute to future teachers’ education.

Smart grids (SGs) have been widely recognized as an effective way to achieve sustainable energy transformation. SG construction plays an important role in environmental protection and promoting world economic and social development. However, Chinese SG field is still in the technology research and development stage, with immature technology, collaborating with leading partners to achieve effective integration for grid operation control technology, flexible transmission, and other equipment seems to be a major challenge. Therefore, the core content of SG is to form a collaborative innovation network to interactively and instantly connect various partners, which will greatly enhance the interactive operations of power grid and improve the overall efficiency of SG. Under such a background, this article attempts to construct a collaborative innovation network for Chinese SG field and investigate dynamic evolution of this network, in order to form a communicating platform that allows collaboration and operations among different partners. Based on the application data of collaborative patents from National Intellectual Property Administration, PRC, this article employs social network analysis method and visualize the temporal and spatial evolution of collaborative innovation network for Chinese SG field. The results show that there is obvious stage characteristic in the temporal evolution process of the network, there are significant differences in the breadth and depth of interregional collaboration concerned with the spatial evolution, meanwhile, the patent collaboration relationships are diverse, and State Grid Corporation of China is always at the core of the network. Finally, combined with China's actual situation, some policy recommendations are raised.

With the rapid development of the Internet, readers tend to share their views and emotions about news events. Predicting these emotions provides a vital role in social media applications (e.g., sentiment retrieval, opinion summary, and election prediction). However, news articles usually consist of objective texts that lack emotion words, making emotion prediction challenging. From prior studies, we know that comments that come directly from readers are full of emotions. Therefore, in this article, we propose a deep learning framework that first merges article and comment information to predict readers' emotions. At the same time, in the prediction process, we design a pseudo comment representation for unpublished news articles by the comments of published news. In addition, a better model is required to encode articles that contain implicit emotions. To solve this problem, we propose a block emotion attention network (BEAN) to encode news articles better. It includes an emotion attention mechanism and a hierarchical structure to capture emotion words and generate structural information during encoding. Experiments performed on three public datasets show that BEAN achieves the state-of-the-art average Pearson (AP) and accuracy (Acc@1). Moreover, results on four self-collected datasets show that both the introduction of emotional comments and BEAN in our framework improve the ability to predict readers' emotions.

Lately lawyers and medical professionals pay more attention too the process of minor patient healthcare. The research shall address the issues of legal relationship between minor patients and medical professionals, consent to treatment of minor patients and communication problems including the scope of information which the doctor can provide to the minor patient’s relatives to protect themselves and patients. Legislation prescribes that the information provided by the medical professional to the minor patient must be not only easy-to-understand, but also be consistent with the patient’s age maturity. However, in Latvia, for example, there are no guidelines that specify how medical professionals can determine the patient’s maturity. In the course of provision of medical services to the minor persons legal disputes involving communiucation failures between the minor patients, their relatives, legal representatives and the doctors arise increasingly frequently. The research will look into issue of communication problems in healthcare. The aim of the research is to provide insight into challenges of legal relations betweem minor patients and medical professionals and communication problems in healthcare.

Engineering design is traditionally performed by hand: an expert makes design proposals based on past experience, and these proposals are then tested for compliance with certain target specifications. Testing for compliance is performed first by computer simulation using what is called a discipline model. Such a model can be implemented by finite element analysis, multibody systems approach, etc. Designs passing this simulation are then considered for physical prototyping. The overall process may take months and is a significant cost in practice. We have developed a Bayesian optimization (BO) system for partially automating this process by directly optimizing compliance with the target specification with respect to the design parameters. The proposed method is a general framework for computing the generalized inverse of a high-dimensional nonlinear function that does not require, for example,\ gradient information, which is often unavailable from discipline models. We furthermore develop a three-tier convergence criterion based on: 1) convergence to a solution optimally satisfying all specified design criteria; 2) detection that a design satisfying all criteria is infeasible; or 3) convergence to a probably approximately correct (PAC) solution. We demonstrate the proposed approach on benchmark functions and a vehicle chassis design problem motivated by an industry setting using a state-of-the-art commercial discipline model. We show that the proposed approach is general, scalable, and efficient and that the novel convergence criteria can be implemented straightforwardly based on the existing concepts and subroutines in popular BO software packages.

Introduction. To represent our national goods at the Western market it is necessary to ensure, first of all, the competitiveness of local products through complex mechanization of technological processes, labour costs reduction, increasing yields and quality of received products. The aim of the research is to provide the comparative analysis of technology and machines for potato harvesting, to determine the main factors that influence the agricultural technical indicators of harvesting equipment, to develop technological process patterns and to examine the strategies for improving the potato root-and-harvest machines. Methods and methodology. The research was carried out on the basis of technological and constructional analysis of technology and machines for potato harvesting. The methods of comparison and mathematical modelling of technological processes were used in the study. The research studies of Ukrainian and foreign scientists on technology and machines for potato harvesting were used as information base of our paper. Results. A comparative analysis of technology and machines for potato harvesting made it possible to determine the main factors that influence the agricultural performance of harvesting machinery and to develop the technological process for a digger, a double-loader digger and a harvester (harvesting machines). A theoretical model of the technological processes of harvesting machines includes both uncontrolled and controlled factors that influence the choice of a potato harvesting technology of a digger, a double-loader digger and a harvester. Nevertheless, the first factors are connected, primarily, with the conditions of potato growth and its biological properties, and the second factors deal with the organizational and technical measures, including the improvement of the working bodies of machines (cutting, dirt digging through, top separation, longitudinal hump with cut roller and other constructive and kinematic parameters), quality of received products, length and terms of potato storage, available farming human resources, free vehicle in the period of harvesting, farm potato storage, farm equipment for potato cleaning and sorting.

Deep reinforcement learning is confronted with problems of sampling inefficiency and poor task migration capability. Meta-reinforcement learning (meta-RL) enables meta-learners to utilize the task-solving skills trained on similar tasks and quickly adapt to new tasks. However, meta-RL methods lack enough queries toward the relationship between task-agnostic exploitation of data and task-related knowledge introduced by latent context, limiting their effectiveness and generalization ability. In this article, we develop an algorithm for off-policy meta-RL that can provide the meta-learners with self-oriented cognition toward how they adapt to the family of tasks. In our approach, we perform dynamic task-adaptiveness distillation to describe how the meta-learners adjust the exploration strategy in the meta-training process. Our approach also enables the meta-learners to balance the influence of task-agnostic self-oriented adaption and task-related information through latent context reorganization. In our experiments, our method achieves 10%-20% higher asymptotic reward than probabilistic embeddings for actor-critic RL (PEARL).

In this article, attention is focused on problems of terminological lexicography, its methodological basis and relevance of the study of this area of knowledge. The reasons for the rapid development of terminographic science and its relevance in the educational activities of students of higher educational institutions are considered. Recommendations are given for introducing a new discipline into the educational process and developing a separate methodology for its more detailed study. The article aims to describe the methodology of this relatively young scientific discipline in general and to focus on the relevance of term inology research in particular. At the same time, make recommendations for the introduction of a new discipline in the educational process for students of higher education. At the present stage of the educational process, unfortunately, there are no subjects or electives in the higher education institutions that refl ect the problems of terminographic activity or its theoretical tasks. The main objectives of the article are to analyze the methodology of the new discipline and emphasize its relevance. The new discipline must fi rst and foremost use elements of descriptive, typological, statistical or even comparative methods. Considering the connection of terminography with other scientifi c disciplines, students will have the opportunity to become better acquainted with the principles and methods of accumulation of material of any branch, processing and transfer it by means of computer programs and other technical means, as well as, possibly, to assist in the creation of new lexicographic databases.

Different forms of coworking spaces have spread worldwide that use various artifacts (e.g., office settings, decor, furniture, equipment) to strengthen autonomy, flexibility, spontaneous interactions, and intrinsic motivation. However, the various manifestations of coworking spaces limit clearly defined boundary conditions for their successful implementation and demand a profound understanding of the interactions that take place among actors and artifacts. Using a flexible pattern matching approach, our study explains that artifacts can contour and create the possibilities for actors and define the borders, e.g., when, where, and with whom to communicate and work in coworking spaces. Our findings indicate that actors and artifacts are constitutively entangled in coworking spaces and play a key role in the community development process by fostering a shared understanding and identity, mutual interdependency, and common practices.

With the increasing prominence of smart mobile devices, an innovative distributed computing paradigm, namely Mobile Crowdsourcing (MCS), has emerged. By directly recruiting skilled workers, MCS exploits the power of the crowd to complete location-dependent tasks. Currently, based on online social networks, a new and complementary worker recruitment mode, i.e., socially aware MCS, has been proposed to effectively enlarge worker pool and enhance task execution quality, by harnessing underlying social relationships. In this paper, we propose and develop a novel worker recruitment game in socially aware MCS, i.e., Acceptance-aware Worker Recruitment (AWR). To accommodate MCS task invitation diffusion over social networks, we design a Random Diffusion model, where workers randomly propagate task invitations to social neighbors, and receivers independently make a decision whether to accept or not. Based on the diffusion model, we formulate the AWR game as a combinatorial optimization problem, which strives to search a subset of seed workers to maximize overall task acceptance under a pre-given incentive budget. We prove its NP hardness, and devise a meta-heuristic-based evolutionary approach named MA-RAWR to balance exploration and exploitation during the search process. Comprehensive experiments using two real-world data sets clearly validate the effectiveness and efficiency of our proposed approach.

Blockchain technologies are at the heart of digital innovation and are a harbinger of Industry 4.0. Consequently, popular press and academic researchers alike have focused on its importance. Yet blockchain technologies’ most promising efforts, cryptocurrency and smart contracts, are underpinned by blockchain mining. The blockchain mining service is undergoing change, cryptocurrencies like Ethereum and others are nearing the end of their minting. Smart contracts are in their infancy. The financial impetus for providing the mining service has changed. Here, we add to the literature through a deep financial analysis of blockchain mining regarding its long-term financial viability. Our methods include a financial cost analysis and an analysis of the financial viability of cryptocurrency through focus on Ethereum. It is found that blockchain miners, despite initial profitability, cannot maintain sustainable financial viability without substantial fees. This article is important to those academics who focus on understanding how service technologies and products underpin Industry 4.0. Finally, this article contributes to the practitioners’ decision-making process to embrace blockchain mining as a technological entrepreneur.

Federated Learning (FL) has attracted significant interest given its prominent advantages and applicability in many scenarios. However, it has been demonstrated that sharing updated gradients/weights during the training process can lead to privacy concerns. In the context of the Internet of Things (IoT), this can be exacerbated due to Intrusion Detection Systems (IDS), which are intended to detect security attacks by analyzing the devices' network traffic. Our work provides a comprehensive evaluation of Differential Privacy (DP) techniques, which are applied during the training of an FL-enabled IDS for Industrial IoT (IIoT). Unlike previous approaches, we deal with non-iid data over the recent ToN_IoT dataset and compare the accuracy obtained considering different privacy requirements and aggregation functions, namely FedAvg and the recently proposed Fed+. According to our evaluation, the use of Fed+ in our setting provides similar results even when noise is included in the federated training process.

Spreadsheet-based tools provide a simple yet effective way of calculating values, which makes them the number-one choice for building and formalizing simple models for budget planning and many other applications.A cell in a spreadsheet holds one specific value and gives a discrete, overprecise view of the underlying model. Therefore, spreadsheets are of limited use when investigating the immanent uncertainties of such models and answering what-if questions. Existing extensions typically require a complex modeling process that cannot be smoothly embedded in a tabular layout.In Fuzzy Spreadsheet, a cell can hold and display the distribution of values. This integrated uncertainty handling immediately conveys sensitivity and robustness information. The fuzzification of the cells enables calculations not only with precise values but with distributions, and probabilities. We conservatively added and carefully crafted visuals to maintain the look and feel of a traditional spreadsheet while facilitating what-if analyses.Given a user-specified reference cell, Fuzzy Spreadsheet automatically extracts and visualizes contextually relevant information, such as impact, uncertainty, and degree of neighborhood, for the selected and related cells.To evaluate its usability and the perceived mental effort required, we conducted a user study.The results show that our approach outperforms traditional spreadsheets in terms of answer correctness, response time, and perceived mental effort for almost all tasks tested.

To date relatively few efforts have been made on the automatic generation of musical instrument playing animations. This problem is challenging due to the intrinsically complex, temporal relationship between music and human motion as well as the lacking of high quality music-playing motion datasets. In this paper, we propose a fully automatic, deep learning based framework to synthesize realistic upper body animations based on novel guzheng music input. Specifically, based on a recorded audiovisual motion capture dataset, we delicately design a generative adversarial network (GAN) based approach to capture the temporal relationship between the music and the human motion data. In this process, data augmentation is employed to improve the generalization of our approach to handle a variety of guzheng music inputs. Through extensive objective and subjective experiments, we show that our method can generate visually plausible guzheng-playing animations that are well synchronized with the input guzheng music, and it can significantly outperform \uline{the state-of-the-art} methods. In addition, through an ablation study, we validate the contributions of the carefully-designed modules in our framework.

In Ancient days there is no machine is used for digging the soil for plantation. The digging of soil for plantation is done manually by humans with the help of the hand auger and it takes more time and requires humans in large scale. This type of traditional process is very hard and it requires a high amount of human efforts. Due to these reasons, nowadays soil digging machines are used in agricultural field for decreasing the human efforts and do the work in efficient manner. The utilization of soil drilling machines in the agricultural fields takes a wide place. These machines are available in the market with different capacities. If the capacity of machine increases automatically cost of machine is also increases. At present in market soil drilling machines is available with diesel engines which are high in cost and heavier in weight. The aim of our project is to reduce the cost of the machine and make it as portable for easy carrying. So due to these reasons we are using petrol engine instead of diesel engine and we attach a iron frame to it for making it portable. This project involves design and fabrication of soil drilling machine. The design of soil drilling machine is done by NX 12 and CATIA V 5 R19 and the fabrication is completed by means of various fabrication techniques.

The Secured exams are one of the foremost troublesome challenges in impersonation Judgment. For any Educational Institution, conducting an online authenticated examination is a major task due to its diverse security components. The main objective of our paper is to provide maximum security and to avoid the malpractice due to impersonation which are happening during the conduct of the examinations. The main objective of our paper is to provide maximum security and to avoid the impersonation and it is related to malpractices which are happening during the conduct of the examinations. The methodology we have adapted deals with the automation process of all the manual verification processes which are done. This is done by the combination of IoT and Image Processing technology. The verification of image and fingerprint confirms the authenticity of the person. The final result is displayed in the IoT database which reduces man power and time consumption. This paper has proposed a technique which eliminates all the errors and illegal activities which happen at the initial stage of the examination process. This technique has proved to provide a better security rather than the manual checkup process.

Due to dense social contacts, metropolises are considered as epicenters of contagious infectious diseases. Developing models for infection transmission and mitigation in metropolises is a challenging problem because of spatiotemporal variations in their population structures. Employing the fact that intra-urban mobility is mainly shaped by the transportation infrastructure and reflected in the traffic flux information, a novel multi-scale reaction-diffusion process is developed to model infection transmission in a metropolis. To mitigate the infection, the impact of three non-pharmaceutical preventive interventions (movement restrictions, social distancing, and proactive testing/screening) is analytically investigated on the reaction-diffusion process. We show that imposing movement restrictions between sub-urban areas is only beneficial when they have different reproduction numbers. The social distancing reduces the infection peak in sub-urban areas, but its impact on the entire metropolis is various: The social distancing reduces a metropolis's infection peak if its implementation disperses the reproduction number of sub-urban areas. We show that allocation schemes of proactive testing/screening teams to sub-urban areas have intractable impacts on the infection peak. This demonstrates the necessity of employing optimization techniques to design the best allocation scheme. Finally, three mathematical models and their solution approaches are developed to optimize the implementation of the interventions with the least economic burden. The performance of the optimization models is compared with two simpler implementation schemes (uniform and pro rata implementations of interventions) using the information of Sioux Falls metropolitan area in the U.S.

After 20 years of testing a framework for affective user responses to artificial agents and robots, we compiled a full formalization of our findings so to make the agent respond affectively to its user. Silicon Coppelia as we dubbed our system works from the features of the observed other, appraises these in various domains (e.g., ethics and affordances), then compares them to goals and concerns of the agent, to finally reach a response that includes intentions to work with the user as well as a level of being engaged with the user. This ultimately results into an action that adds to or changes the situation both agencies are in. Unlike many other systems, Silicon Coppelia can deal with ambiguous emotions of its user and has ambiguous ‘feelings’ of itself, which makes its decisions quite human-like. In the current paper, we advance a fuzzy-sets approach and show the inner workings of our system through an elaborate example. We also present a number of simulation experiments, one of which showed decision behaviors based on biases when agent goals had low priorities. Silicon Coppelia is open to scrutiny and experimentation by way of an open-source implementation in Ptolemy.

Blockchain technology has wide area of application, this article is very particular on emphasising another important field of application named Agro-products supply chain. Wherein this era, and the future needs to nurture the agriculture sector along with latest technologies, so that nation would make a revolution in the process of food supply from the farm to the plate. The main drawback in the agriculture supply chain is lack of transparency in food production and distribution, costly middleman, limited financial resources & disconnection between supplier and retailer. So this article proposes, suitable theoretical auction flow model to overcome all the issues highlighted in this agro-sector, we would like to give the solution by improving the transparency in the supply chain, by providing farmers direct access to suppliers through an appropriate auction system with an option of immediate payment on delivery & transparent transaction information to them. Further we can involve government as a central authority to overcome the technical aspects, education, policies and regulatory frame works, in turn they can generate job opportunities to the unemployed graduates.

Organic fertilizers have been used since the dawn of mankind to improve soil fertility and promote crop growth. However, the process of preparing organic fertilizers for use in agricultural activities is a labour-intensive and time-consuming procedure. Thus, in order to reduce labour costs and improve the efficiency of organic fertilizer preparation, automated organic fertilizer mixers were developed. For this research work, an improved organic fertilizer mixer based on the Internet of Things (IoT) technology capable of monitoring the status of fertilizer production remotely and providing updates and alerts to workers was developed. The aim of this work was to further reduce labour workload and costs, thus increasing efficiency of the organic fertilizer mixing process. In order to accommodate the IoT monitoring system, several modifications and design changes were carried out on the prototype of an automated organic fertilizer mixer. Tests conducted on the prototype unit showed that it performs as required. The IoT monitoring system successfully captures and uploads information on the weight of agricultural waste mixture in the storage drum to the IoT platform. This information can then be used to alert workers on when to add more agricultural waste material to the mixture or remove the processed organic fertilizer. Furthermore, an overall operational cost comparison of different organic fertilizer production methods showed that the automated organic fertilizer prototype provides operational cost savings of over 5 times when compared to current automated systems. Future work includes further automation to the agricultural waste filling and organic fertilizer removal process.

Video conferencing applications have become an integral part of today’s world for attending interviews, classes, meetings, and assorted gatherings as well in the COVID-19 era. Alongside the increased use of such applications to facilitate the process of conducting interviews, the quality interview has taken a hit overall. This is largely because prospective candidates resort to fraud by switching tabs and using their phones during the course of an interview, and so come through with flying colors despite a clear lack of skills. Consequently, deserving candidates with the requisite skill set lose out to impostors who manage to clear the interviews. In this paper, we propose an approach to make interviews straightforward and fair to all candidates. Our Online Interview Platform, a web application built using Node.js and Express.js, offers indispensable features that are prerequisites for an interview. These include a real-time collaborative code editor that uses an operational transformation algorithm which allows users to collaborate in real time, test and run code; a video/audio conferencing feature using Peer JS; a chat box for communication, and a real-time collaborative whiteboard that lets users design or draw diagrams. The features are included in the same tab, thus ensuring that the candidate does not switch tabs. Using this application, candidates will be screened based on their technical knowledge, appropriately assessed, and performance-based hiring decisions made. The proposed approach proved that the malpractices strictly restricted while comparing with existing approaches.

The study aimed to identify important variables associated with policies, processes and procedures for culturally diversified organizations. Further to identify which factor among the policies, processes and procedures is the most prerequisite for culturally diversified organizations. For the study, a survey method was employed. 153 Data were collected from companies in the service industry. For the study HR managers were considered as the samples. The data samples were chosen using a simple random sampling technique. From the result obtained, it was found that; the Retention Strategies, Talent Management and Salary Reviews policies are most important for the culturally diversified organization. Also, evaluation by observing how people spend their time in the job, interviewing with existing company people periodically and Understand the career path progression of employees and their desire are important process to be considered for a culturally diversified organization. Similarly, the Compensation system, hiring guidelines and Chain of Command procedures are considered to be important for evaluating procedure in a culturally diversified organization. It can be further perceived that the male HRs indicates Procedure and Policy are an important factor to be considered for a culturally diversified organization. While the female HRs indicates Procedure and Process are an important factor to be considered for a culturally diversified organization.

Amidst of the technologies applied to analyze and recognize a facial emotion, there are still clustered challenges that needs to be addressed while building advanced emotion recognition models with more accurate results. This study summarizes the technologies used in the development of various models for facial emotion recognition. Because the advancement in technology is abruptly changing and improving, only the studies for the past two years were included. The studies earlier in the past two years are most likely redundant to the studies prior to it. The study includes discussion of the datasets commonly used by the researchers, the nature of the datasets and its content, and the process of generating the data. Moreover, the highlight of this literature survey is to gather the state-of-the-art technologies and various methods that are applied to dataset for achieving highest possible accuracy rate. However, there are still drawbacks that needs to be addressed.

In the recent years the utilization of cloud to store and process the big data is increased rapidly. Security is the major concern faced by the big data in cloud environment. This paper proposed the authentication scheme for preserving the security and privacy of the big data in cloud environment. This paper considered the medical big data processing from the telemedicine system as the big data. The medical big data consists of huge volumes of medical records which are stores in the cloud. In the proposed mechanism follows mutual authentication and performs encryption operation between user and cloud environment. The proposed authentication mechanism has less computational load compared to the existing algorithms.

The agriculture industry is one that is highly resource- and labour-intensive. As such, farmers are increasingly turning to technology and automation to address this issue. However, agricultural robots are far too complicated, slow, and costly to be made publicly available. As a result, the agriculture sector still lags behind in integrating modern technologies. This research paper details the development of a low-cost agricultural robot for spraying fertilizers and pesticides in agriculture fields as well as for general crop monitoring. The prototype system is a two-wheeled robot that consists of a mobile base, a spraying mechanism, a wireless controller for controlling the robot movement, and a camera for crop health and growth monitoring as well as detecting the presence of pests in the agriculture field. Tests conducted on the prototype system show that while the productivity of the robot in terms of crop coverage is slightly lower than a human worker, the labour cost savings afforded by the agricultural robot prototype is much greater as it functions completely in an autonomous mode and only requires the operator to control the robot when placing it at the start of the crop path. Furthermore, the prototype system also provides greater resource savings and reduction in the contamination of underground water sources due to leeching process, thus achieving precision agriculture goals. Lastly, the excellent battery life of the prototype system ensures that there will be no increase in the operation times and reduction in the efficiency of the fertilizer and pesticide spraying process due to the recharging times when replacing human workers. Future recommendations include making the agricultural robot fully autonomous, using either a rail- or line-following system, to further reduce the labour requirements and costs.

In several fields of application, data science plays an important role. Fraud discovery has historically been a repetitive manual. Popular approaches to discover and deter fraud are analysis and computer assistance. Computers can use very simple means in the alert, for example, to flag all statements exceeding a previously defined threshold. The obvious intention is to prevent major losses by concentrating on the larger claims. Financial fraud is rapidly advanced and strategies for combating such attacks must also develop. Big data also created a new insight in terms of fraud detection and prevention strategies such as behavioural analysis and real-time detection. Even though technology made consumer banking much more convenient, new avenues for fraud have been developed. Financial fraud figures show that the annual losses of institutions and customers are millions of dollars in account fraud, credit card fraud, insurance fraud, fraud and other criminal activities. In this work we concentrate on Big data is the use for the collection of very broad sets of knowledge of advanced techniques and technologies. These data sets are often so broad and complex that using on-line database management software is difficult to process.

Data provenance is any information about the origin of a piece of data and the process that led to its creation. Most database provenance work has focused on creating models and semantics to query and generate this information. While comprehensive, provenance information remains large and overwhelming, which can make it hard for provenance systems to support data exploration. We present a new approach to provenance exploration that builds on data summarization techniques. We contribute two novel summarization schemes for the provenance of aggregation queries: Impact summaries, and comparative summaries. We show with experiments that our techniques incur little overhead compared to basic summaries. We conduct a survey to show that our approaches are useful to users.

Modular projects can be of higher quality, a safer, faster and more predictable construction process, and less environmental nuisance compared to a traditional on-site project. High-rise projects seem to be particularly suitable for modular building methods, however there are still some process-related barriers. Research so far has not focused on collaboration forms for modular high-rise projects. This paper examines which collaboration form fits best for a modular high-rise project by conducting an international case study research. From the case studies it is debatable whether the current PDMs meet the need for modular concepts; modular buildings will benefit more from a long-term collaboration with fixed partnerships. This requires a complete different approach to the construction industry. Until then, the best match should be sought by matching the customer profile to the PDM characteristics. The most suitable PDM is dependent on the client profile that can be determined by 17 selection criteria.

Nowadays, the additive manufacturing process had significant attention due to its great advantages that are being used for developing products and producing prototypes. This technology overcomes the various drawbacks of existing techniques and it becomes the most efficient approach. In the industrial sector, this technology is most important for individual designing products and extended with different functions. The performance of additively manufactured parts, on the other hand, is largely influenced by their surface integrity and dimensional accuracy. The surface integrity is an important issue because the additively manufactured products are affected by staircase effects resulting from layer-by-layer manufacturing. The rapid prototyping contains multiple methods such as VAT Photopolymerization and Poor surface quality is caused by the staircase effect in all additive manufacturing processes so therefore, the various post-processing efforts and controlling different process parameters of additive manufacturing are necessary on the produced component for improving surface quality and reducing surface roughness. To focus on improving the finish of an additively manufactured part's surface, there are numerous methods presented. This research described the various existing approaches of surface finishing with various efficacies such as reduction of surface roughness value, mechanical strength, and dimensional accuracy. Current post-processing methods for improving the surface characteristics of additively manufactured parts are discussed, with a classification based on the processes' mechanisms.

Deep Neural Network (DNN) in recent era offers new opportunities to manage the production systems with increasing complexity. In this paper, DNN is trained with discrete event simulation and a process based association to make the job shop an autonomous one. This intelligent system operates well in complex environment with constrained time limits while making optimal decisions. The DNN is henceforth combined with constrained time limits for the process of production control. The system is implemented typically in semiconductor manufacturing industries on complex job shops of a wafer fab case. The DNN is trained in such a way that it operates in complex environment with timing constraints that ships the job in accurate way without flaws in operation. The DNN rewards the selection with reduced time constraint, which tends to operates with most critical batch list. The study therefore shows that the DNN manages well the timing constraints than the standard benchmark technique.

Purpose: While new rich learning opportunities emerged through the introduction of Innovation Platforms (IPs) in agricultural value chains, the extent to which IPs enhance farmer experiential learning is still unclear.Design/methodology/approach: This paper brings clarity to the above question by interviewing 91 coffee IP farmers. Data were analyzed through content analysis to generate overarching themes for farmers’ experiences, learning activities, and outcomes.Findings: Results reveal that participation in IP learning activities generates farmers’ knowledge to cope with coffee value chain challenges. Specifically, farmers’ making-meaning of challenges and generating new solutions represents an iteration between individual critical reflection and experimentation of value chain activities. The IPs facilitated multi-directional knowledge flowsamong farmers by mobilizing necessary resources.Practical implications: As many Sub-Saharan Africa (SSA) governments cannot provide sufficient extension support, farmers increasingly rely on IPs whose processes stimulate members’ learning commitment and endeavors. Nevertheless, Governments can use these findings to tailor the design and implementation ofIPs to farmers’ experiential learning processes.Theoretical implications: The study contributes to experiential theory in the context of agriculture by advancing a model on how IPs can accelerate farmers’ experiential learning processes based on the challenges experienced.Originality/value: This article extends knowledge of experiential learning in IPs context.

Smart contracts are a vital component of applications being built by blockchain or distributed ledger technology. Smart contracts consists of computer code that composed set of rules agreed upon by the involved parties. When these predefined conditions or rules are satisfied by the transactions initiated by parties, the smart contract executes itself to complete the transaction. Normally, while performing transactions with agreements involving multiple parties, a third party have to be involve to verify all the information, which makes it complex and time consuming process. Smart contracts simplify this process by eliminating the third party and automating the process, enabling the stakeholders to perform transactions directly with each other. Smart contract itself is replicated among multiple nodes of a blockchain there by giving benefits of immutability, security and permanence. Most smart contracts are written in Solidity programming language due to its simplicity and conciseness. Attackers are attracted by the popularity of Solidity language and its vulnerability possibilities. For example, in the 2016 year, 60 million US dollars was theft by Decentralized Autonomous Organization (DAO) attack due to vulnerabilities present in smart contracts. There are few existing tools and papers on this area to find vulnerabilities on smart contracts but taking more time to predict. Thus, this paper presents different Deep learning techniques with satisfactory results to identify smart contract vulnerabilities which are Re-entrancy, Denial of Service (DOS) and Transaction Origin using binary, multi class and multi label classification techniques.

With the rapid enhancements in technology and the adoption of web services, there has been a significant increase in cyber threats faced by organizations in cyberspace. It has become essential to get financial cover to mitigate the expenses due to a security incident. Organizations want to purchase adequate cyber insurance to safeguard against the third-party services they use. However, cyber insurance policies describe their coverages and exclusions using legal jargon that can be difficult to comprehend. Parsing these policy documents and extracting the rules embedded in them is currently a very manual time-consuming process. We have developed a novel framework that automatically extracts the coverage and exclusion key terms and rules embedded in a cyber policy. We have built our framework using Information Retrieval and Artificial Intelligence techniques, specifically Semantic Web and Modal Logic. We have also developed a web interface where users can find the best matching cyber insurance policy based on particular coverage criteria. To validate our approach, we used industry standards proposed by the Federal Trade Commission document (FTC) and have applied them against publicly available policies of seven insurance providers. Our system will allow cyber insurance seekers to explore various policy documents and compare the paradigms mentioned in those documents while selecting the best relevant policy documents.

The enrollment process of an educational institution becomes very complex when the waiting time is prolonged and crowds of people are created. Technological tools are the most effective way to deal with this problem. Therefore, this research seeks to optimize the processes that make up the enrollment management of an educational institution through the implementation of a digital enrollment system. The development of the digital enrollment system was divided into four phases. Planning: Where the system requirements were described. Design: A simple model was chosen according to the established requirements. Coding: Programming languages were used that helped in the structure, customization and operation of the system. Test: The system was verified for errors. In this sense, the digital enrollment process was optimized, such as student enrollment, registration and reports, allowing the reduction of crowds, time and human effort. It allowed collaborative work at a distance between the institution and the students, being a technological and innovative aid that reduces the time of the enrollment processes, in an equitable and transparent way, guaranteeing freedom of choice and equal access opportunities to remote education in times of pandemic.

Small and medium enterprises (SMEs) in the industrial district have their own advantages because they can share information, knowledge, and facilities in a communal way. However, the implementation of cleaner production in SMEs requires a process of innovation that is environmentally friendly. Open innovation enables organizations like SMEs to innovate better by adopting knowledge from outside of the organization. Cleaner production is a major factor to attain sustainability because it is part of the steps for sustainability process. Innovation is required in the adoption process of cleaner production to reach sustainability; not to mention it has a role in cleaner production activity. This research model consists of two second-order variables, i.e., open innovation climate, and open innovation. Based on hypothesis testing on modern SMEs, open innovation has a positive influence on cleaner production; and open innovation climate has a positive influence on open innovation modern SMEs; environmental dynamism and environmental competitiveness do not have a positive effect in moderating open innovation climate and open innovation. On the other hand, all hypotheses on traditional SMEs are confirmed to have a positive influence or/and positive effect; open innovation has a positive influence on cleaner production; open innovation climate also has a positive influence on open innovation; environmental dynamism and environmental competitiveness have positive effects in moderating open innovation climate and open innovation.

In present scenario, each and every activity in basic lifestyle of people is becoming smarter with the day-to-day flourishing development of technology such as Internet of Things, Artificial Intelligence, Machine Learning and Blockchain. Among the smart improvements, smart city holds the highest rank due to the initiatives provided by the administration. Smart city mission has influenced the citizen’s life with respect to the automation on fundamental needs. Energy resources are considered as one of the important requirements for the survival of people. With the advent of Internet of Things, smart grid systems are designed in order to meet the sustainable demands. It also provides information about the energy resources consumed and alert the consumers about the demand prevails on the resources. Technological improvement is more essential for a country, only if it can be considered and adaptable for sustainable development. Therefore these papers outline the Internet of Things based smart grid which helps in the energy conservation process to meet the future demands. Energy management processes has to be well-versed among the citizens as it helps in understanding the possible ways on storage of energy resources. However, when energy storage is concerned, security issues are the counterpart which has to be focused at higher level. Therefore, blockchain techniques applied for energy storage systems are also outlined along with the applications and future directions of smart grid.

Cyber security is guarding computer systems, data, network and other resources from unauthorized access and malicious users. There are no direct methods of measuring strength of cyber security. As they say, “You can’t manage what you can’t measure”. One can easily track the efforts taken for security through cyber security metrics. Being a quantifiable measure, metric can be utilized in tracking the status of a specific process and assess its outcomes along with its strength. This work aims to provide taxonomy of cyber security metrics with five basic metrics, along with the tools under Multi Criteria Decision making approach can be used in evaluation of cyber security strength.

The wide availability of embedded sensor platforms and lowcost cameras—together with the developments in wireless communication—make it now possible the conception of pervasive intelligent systems based on vision. Such systems may be understood as distributed and collaborative sensor networks, able to produce, aggregate and process images in order to understand the observed scene and communicate the relevant information found about it. In this paper, we investigate the peculiarities of visual sensor networks with respect to standard vision systems and we identify possible strategies to accomplish image processing and analysis tasks over them. Although the rather strong constraints in computational and transmission power of embedded platforms that may prevent the use of state of the art computer vision and pattern recognition methods, we argue that multinode processing methods may be envisaged to decompose a complex task into a hierarchy of computationally simpler problems to be solved over the nodes of the network. These ideas are illustrated by describing an application of visual sensor network to infomobility. In particular, we consider an experimental setting in which several views of a parking lot are acquired by the sensor nodes in the network. By integrating the various views, the network is capable to provide a description of the scene in terms of the available spaces in the parking lot.