A Multi-armed Bandit Approach to Online Spatial Task Assignment

Spatial crowdsourcing uses workers for performing tasks that require travel to different locations in the physical world. This paper considers the online spatial task assignment problem. In this problem, spatial tasks arrive in an online manner and an appropriate worker must be assigned to each task. However, outcome of an assignment is stochastic since the worker can choose to accept or reject the task. Primary goal of the assignment algorithm is to maximize the number of successful assignments over all tasks. This presents an exploration-exploitation challenge; the algorithm must learn the task acceptance behavior of workers while selecting the best worker based on the previous learning. We address this challenge by defining a framework for online spatial task assignment based on the multi-armed bandit formalization of the problem. Furthermore, we adapt a contextual bandit algorithm to assign a worker based on the spatial features of tasks and workers. The algorithm simultaneously adapts the worker assignment strategy based on the observed task acceptance behavior of workers. Finally, we present an evaluation methodology based on a real world dataset, and evaluate the performance of the proposed algorithm against the baseline algorithms. The results demonstrate that the proposed algorithm performs better in terms of the number of successful assignments

A Capability Requirements Approach for Predicting Worker Performance in Crowdsourcing

Abstract—Assigning heterogeneous tasks to workers is an important challenge of crowdsourcing platforms. Current ap-proaches to task assignment have primarily focused on content-based approaches, qualifications, or work history. We propose an alternative and complementary approach that focuses on what capabilities workers employ to perform tasks. First, we model various tasks according to the human capabilities required to perform them. Second, we capture the capability traces of the crowd workers performance on existing tasks. Third, we predict performance of workers on new tasks to make task routing decisions, with the help of capability traces. We evaluate the ef-fectiveness of our approach on three different tasks including fact verification, image comparison, and information extraction. The results demonstrate that we can predict worker’s performance based on worker capabilities. We also highlight limitations and extensions of the proposed approach. Keywords—microtask, taxonomy, crowdsourcing, performance I

Flag-Verify-Fix: Adaptive Spatial Crowdsourcing leveraging Location-based Social Networks

This paper introduces the flag-verify-fix pattern that employs spatial crowdsourcing for city maintenance. The patterns motivates the need for appropriate assignment of dynamically arriving spatial tasks to a pool for workers on the ground. The assignment is aimed at maximizing the coverage of tasks spread over spatial locations; however, the coverage depends of willingness of workers to perform tasks assigned to them. We introduce the maximum coverage assignment problem that formulates two design issues of dynamic assignment. The quantity issue determines the number of worker required for a task and selection issue determines the set of workers. We propose an adaptive algorithm that uses location diversity based on a location-based social network to address the quantity issue and employs Thompson sampling for selecting the workers by learning their willingness. We evaluate the performance of the proposed algorithm in terms of coverage and number of assignments using real world datasets. The results show that our proposed algorithm achieves 30%-50% more coverage than the baseline algorithms, while requiring less workers per task

Efficient task assignment for spatial crowdsourcing: A combinatorial fractional optimization approach with semi-bandit learning

Spatial crowdsourcing has emerged as a new paradigm for solving problems in the physical world with the help of human workers. A major challenge in spatial crowdsourcing is to assign reliable workers to nearby tasks. The goal of such task assignment process is to maximize the task completion in the face of uncertainty. This process is further complicated when tasks arrivals are dynamic and worker reliability is unknown. Recent research proposals have tried to address the challenge of dynamic task assignment. Yet the majority of the proposals do not consider the dynamism of tasks and workers. They also make the unrealistic assumptions of known deterministic or probabilistic workers’ reliabilities. In this paper, we propose a novel approach for dynamic task assignment in spatial crowdsourcing. The proposed approach combines bi-objective optimization with combinatorial multi-armed bandits. We formulate an online optimization problem to maximize task reliability and minimize travel costs in spatial crowdsourcing. We propose the distance-reliability ratio (DRR) algorithm based on a combinatorial fractional programming approach. The DRR algorithm reduces travel costs by 80% while maximizing reliability when compared to existing algorithms. We extend the DRR algorithm for the scenario when worker reliabilities are unknown. We propose a novel algorithm (DRR-UCB) that uses an interval estimation heuristic to approximate worker reliabilities. Experimental results demonstrate that the DRR-UCB achieves high reliability in the face of uncertainty. The proposed approach is particularly suited for real-life dynamic spatial crowdsourcing scenarios. This approach is generalizable to the similar problems in other areas in expert systems. First, it encompasses online assignment problems when the objective function is a ratio of two linear functions. Second, it considers situations when intelligent and repeated assignment decisions are needed under uncertainty

The Role of Carbapenems in the Management of Diabetic Wounds

Abstract:  To contemplate the role  of Carbapenems in the Administration of Diabetic wounds. Prospective Study Place and Duration of Study:This examination was led at the Idris Teaching Hospital, Sialkot from the month January 2014 till Nov 2017.Materials and Methods: To consider the function of Carbapenems in the Management of Diabetic Wound. Around hundreds of cases  of diabetic wounds were incorporated into this imminent investigation amid January 2014-Nov 2017 at Idris Teaching College Sialkot. Patients were given approximately One gram of Carbapenems two times daily. The Performa was intended to track the record of  age, sexual orientation, span of treatment, evaluations of the injuries and territory of the body associated with diabetic wounds. Educated assent of the considerable number of patients was considered before treatment and authorization of moral board of trustees of the organization was additionally acquired.Results:In 63 (63%) of females, diabetic wounds were more likely to found as compared to the 37 (37%) of the males. The regular age run was 50-60 years 35 (35%) cases in female and 20 (20%) cases in male as appeared in table no.1. The rate of diabetic wounds of foot, legs, back, and hands was 86%, 3%, 5% and 6% individually as demonstrated table no.2. It demonstrated that the foot was most normal region engaged with diabetic wounds 86 cases (86%). The frequency was in various evaluations of the injuries (I-V), 20 (20%), 30 (30%), 20 ( 20%), 15 (15%) and 15 (15%) individually as appeared in table no. 03. It demonstrated that the occurrence of review II wounds was greatest 30 (30%). In evaluations of wounds from I-V, the term was 3-7 days, 8-15 days, 16-30 days, 31-45 days, 46-60 days separately as appeared in table no.03.Conclusion: It demonstrated that injuries of the review 2 have most extreme frequency 30 (30%) when contrasted with other review of the injuries.Keywords : Carbapenems, Diabetic Wounds, Managemen

Practice of standard Cross Infection Protocol in Private Dental Clinics of Khyber Pakhtunkhwa, Pakistan: A cross-sectional study

Background: Infection control, which is considered to be the backbone of dentistry, has become a particularly essential piece of dental training because both the dentists and patients are at an expanded danger of cross-contamination. Dental clinical settings represent a high organic hazard of spreading a wide scope of microorganisms. The objective of this study is to gather information of different dentists regarding their practice of standard cross infection protocols and how can they improve the same in their private practices. Materials and Methods: This cross-sectional study was conducted in dental clinics of Khyber Pakhtunkhwa, KPK from January 2020 to July 2020 by distributing a questionnaire among dentists. It was a pre-designed questionnaire that was circulated in Google forms through Whatsapp and emails. The questionnaire was divided into 2 sections. Data was compiled and statistical tests were applied using the Statistical Package for Social Sciences SPSS® ver.23.0 Results: Regarding cross infection control measures, maximum dentists seem to have knowledge of cross infection control techniques. A significant difference was found (p= 0.05) between male & female dentists in disposing dental waste from clinical set up properly. A significant difference was also found between male & female dentists about rubber dam isolation (p=0.02). Conclusion: The result of this study showed that practice of dentists in KPK is not up to standard protocols of cross infection control. In this way, the need of great importance is to authorize and execute better proportions of infection control to improve dental practice in KPK. Key words: Infection control, Dental practice, Sterilizatio

Effects of Expertise Assessment on the Quality of Task Routing in Human Computation

Human computation systems are characterized by the use of human workers to solve computationally difficult problems. Expertise profiling involves assessment and representation of a worker’s expertise, in order to route human computation tasks to appropriate workers. This paper studies the relationship between the assessment workload on workers and the quality of task routing. Three expertise assessment approaches were compared with the help of a user study, using two different groups of human workers. The first approach requests workers to provide self-assessment of their knowledge. The second approach measures the knowledge of workers through their performance against tasks with known responses. We propose a third approach based on a combination of self-assessment and task-assessment. The results suggest that the self-assessment approach requires minimum assessment workload from workers during expertise profiling. By comparison, the task-assessment approach achieved the highest response rate and accuracy. The proposed approach requires less assessment workload, while achieving the response rate and accuracy similar to the task-assessment approach

Slua: Towards semantic linking of users with actions in crowdsourcing

Recent advances in web technologies allow people to help solve complex problems by performing online tasks in return for money, learning, or fun. At present, human contribution is limited to the tasks defined on individual crowdsourcing platforms. Furthermore, there is a lack of tools and technologies that support matching of tasks with appropriate users, across multiple systems. A more explicit capture of the semantics of crowdsourcing tasks could enable the design and development of matchmaking services between users and tasks. The paper presents the SLUA ontology that aims to model users and tasks in crowdsourcing systems in terms of the relevant actions, capabilities, and rewards. This model describes different types of human tasks that help in solving complex problems using crowds. The paper provides examples of describing users and tasks in some real world systems, with SLUA ontology

AN ENTITY-CENTRIC APPROACH TO GREEN INFORMATION SYSTEMS

The integration of sustainable thinking and performance within day-to-day business activities has become an important business need. Sustainable business requires information on the use, flows and destinies of energy, water, and materials including waste, along with monetary information on environment-related costs, earnings, and savings. Creating this holistic view of economic, social and environmental information is not a straightforward mission from an IT perspective, and implies tackling several challenges such as information granularity and overload, the different projections of the same factual information, and the heterogeneity of information systems. In this paper, we propose an entity-centric approach to Green Information Systems to assist organisations in forming a cohesive representation of the environmental impact of their business operations at both micro- and macrolevels. Initial results from a Small Medium-size Enterprise case study are discussed along with future research directions