A Method for Aggregating Ranked Services for Personal Preference Based Selection

2021 ◽  
pp. 1373-1397
Author(s):  
Kenneth K Fletcher
Keyword(s):  
Real World ◽  
Rank Aggregation ◽  
Personal Preference ◽  
Functional Attributes ◽  
Optimal Service ◽  
Alternative Approaches ◽  
Incomplete Lists ◽  
Ranked List ◽  
Better Than

Typically, users' service requests, which are similar with varying preferences on non-functional attributes, may result in ranked lists of services that partially meet their needs due to conflicting non-functional attributes. The resultant multiple ranked lists of services that partially satisfies the user's request makes it challenging for the user to choose an optimal service, based on his/her preference. This work proposes a method that aggregates multiple ranked lists of services into a single aggregated ranked list, where top ranked services are selected for the user. Two algorithms are proposed; 1) Rank Aggregation for Complete Lists (RACoL), that aggregates complete ranked lists and 2) Rank Aggregation for Incomplete Lists (RAIL) to aggregate incomplete ranked lists. Examples using real-world airline services to evaluate both algorithms show that the results from both proposed algorithms closely represent the sets of ranked lists better than using alternative approaches. Experiments were also carried out to validate their performance.

A Method for Aggregating Ranked Services for Personal Preference Based Selection

2019 ◽  
Vol 16 (2) ◽  
pp. 1-23 ◽  
Author(s):  
Kenneth K Fletcher
Keyword(s):  
Real World ◽  
Rank Aggregation ◽  
Personal Preference ◽  
Functional Attributes ◽  
Optimal Service ◽  
Alternative Approaches ◽  
Incomplete Lists ◽  
Ranked List ◽  
Better Than

Typically, users' service requests, which are similar with varying preferences on non-functional attributes, may result in ranked lists of services that partially meet their needs due to conflicting non-functional attributes. The resultant multiple ranked lists of services that partially satisfies the user's request makes it challenging for the user to choose an optimal service, based on his/her preference. This work proposes a method that aggregates multiple ranked lists of services into a single aggregated ranked list, where top ranked services are selected for the user. Two algorithms are proposed; 1) Rank Aggregation for Complete Lists (RACoL), that aggregates complete ranked lists and 2) Rank Aggregation for Incomplete Lists (RAIL) to aggregate incomplete ranked lists. Examples using real-world airline services to evaluate both algorithms show that the results from both proposed algorithms closely represent the sets of ranked lists better than using alternative approaches. Experiments were also carried out to validate their performance.

scholarly journals Reviewer Recommendations Using Document Vector Embeddings and a Publisher Database: Implementation and Evaluation

2021 ◽  
Author(s):  
Yue Zhao ◽  
Ajay Anand ◽  
Gaurav Sharma
Keyword(s):  
Large Scale ◽  
Evaluation Methodology ◽  
Data Manipulation ◽  
Document Vector ◽  
Hyperparameter Selection ◽  
Alternative Approaches ◽  
Database Implementation ◽  
Indirect Comparisons ◽  
Ranked List ◽  
Better Than

<div>We develop and evaluate an automated data-driven framework for providing reviewer recommendations for submitted manuscripts. Given inputs comprising a set of manuscripts for review and a listing of a pool of prospective reviewers, our system uses a publisher database to extract papers authored by the reviewers from which a Paragraph Vector (doc2vec ) neural network model is learned and used to obtain vector space embeddings of documents. Similarities between embeddings of an individual reviewer’s papers and a manuscript are then used to compute manuscript-reviewer match scores and to generate a ranked list of recommended reviewers for each manuscript. Our mainline proposed system uses full text versions of the reviewers’ papers, which we demonstrate performs significantly better than models developed based on abstracts alone, which has been the predominant paradigm in prior work. Direct retrieval of reviewer’s manuscripts from a publisher database reduces reviewer burden, ensures up-to-date data, and eliminates the potential for misuse through data manipulation. We also propose a useful evaluation methodology that addresses hyperparameter selection and enables indirect comparisons with alternative approaches and on prior datasets. Finally, the work also contributes a large scale retrospective reviewer matching dataset and evaluation that we hope will be useful for further research in this field. Our system is quite effective; for the mainline approach, expert judges rated 38% of the recommendations as Very Relevant; 33% as Relevant; 24% as Slightly Relevant; and only 5% as Irrelevant.</div>

scholarly journals Reviewer Recommendations Using Document Vector Embeddings and a Publisher Database: Implementation and Evaluation

2021 ◽  
Author(s):  
Yue Zhao ◽  
Ajay Anand ◽  
Gaurav Sharma
Keyword(s):  
Large Scale ◽  
Evaluation Methodology ◽  
Data Manipulation ◽  
Document Vector ◽  
Hyperparameter Selection ◽  
Alternative Approaches ◽  
Database Implementation ◽  
Indirect Comparisons ◽  
Ranked List ◽  
Better Than

<div>We develop and evaluate an automated data-driven framework for providing reviewer recommendations for submitted manuscripts. Given inputs comprising a set of manuscripts for review and a listing of a pool of prospective reviewers, our system uses a publisher database to extract papers authored by the reviewers from which a Paragraph Vector (doc2vec ) neural network model is learned and used to obtain vector space embeddings of documents. Similarities between embeddings of an individual reviewer’s papers and a manuscript are then used to compute manuscript-reviewer match scores and to generate a ranked list of recommended reviewers for each manuscript. Our mainline proposed system uses full text versions of the reviewers’ papers, which we demonstrate performs significantly better than models developed based on abstracts alone, which has been the predominant paradigm in prior work. Direct retrieval of reviewer’s manuscripts from a publisher database reduces reviewer burden, ensures up-to-date data, and eliminates the potential for misuse through data manipulation. We also propose a useful evaluation methodology that addresses hyperparameter selection and enables indirect comparisons with alternative approaches and on prior datasets. Finally, the work also contributes a large scale retrospective reviewer matching dataset and evaluation that we hope will be useful for further research in this field. Our system is quite effective; for the mainline approach, expert judges rated 38% of the recommendations as Very Relevant; 33% as Relevant; 24% as Slightly Relevant; and only 5% as Irrelevant.</div>

scholarly journals Network robustness improvement via long-range links

2019 ◽  
Vol 6 (1) ◽  
Author(s):  
Vincenza Carchiolo ◽  
Marco Grassia ◽  
Alessandro Longheu ◽  
Michele Malgeri ◽  
Giuseppe Mangioni
Keyword(s):  
Complex Networks ◽  
Long Range ◽  
Real World ◽  
Network Robustness ◽  
Scale Free ◽  
Area Of Interest ◽  
Before And After ◽  
Significant Area ◽  
Better Than

AbstractMany systems are today modelled as complex networks, since this representation has been proven being an effective approach for understanding and controlling many real-world phenomena. A significant area of interest and research is that of networks robustness, which aims to explore to what extent a network keeps working when failures occur in its structure and how disruptions can be avoided. In this paper, we introduce the idea of exploiting long-range links to improve the robustness of Scale-Free (SF) networks. Several experiments are carried out by attacking the networks before and after the addition of links between the farthest nodes, and the results show that this approach effectively improves the SF network correct functionalities better than other commonly used strategies.

scholarly journals Extremophilic Prokaryotic Endoxylanases: Diversity, Applicability, and Molecular Insights

2021 ◽  
Vol 12 ◽  
Author(s):  
Digvijay Verma
Keyword(s):  
Molecular Mechanisms ◽  
Good Choice ◽  
Industrial Processes ◽  
Low Molecular Weight ◽  
Short Life Span ◽  
Functional Attributes ◽  
Extreme Behavior ◽  
Harsh Conditions ◽  
Free Activity ◽  
Better Than

Extremophilic endoxylanases grabbed attention in recent years due to their applicability under harsh conditions of several industrial processes. Thermophilic, alkaliphilic, and acidophilic endoxylanases found their employability in bio-bleaching of paper pulp, bioconversion of lignocellulosic biomass into xylooligosaccharides, bioethanol production, and improving the nutritious value of bread and other bakery products. Xylanases obtained from extremophilic bacteria and archaea are considered better than fungal sources for several reasons. For example, enzymatic activity under broad pH and temperature range, low molecular weight, cellulase-free activity, and longer stability under extreme conditions of prokaryotic derived xylanases make them a good choice. In addition, a short life span, easy cultivation/harvesting methods, higher yield, and rapid DNA manipulations of bacterial and archaeal cells further reduces the overall cost of the product. This review focuses on the diversity of prokaryotic endoxylanases, their characteristics, and their functional attributes. Besides, the molecular mechanisms of their extreme behavior have also been presented here.

scholarly journals Nonpreemptive Goal Programing Method in Optimization Nurse Scheduling by Considering Education Level

2021 ◽  
Vol 22 (2) ◽  
pp. 85
Author(s):  
Fitriani Utina ◽  
Lailany Yahya ◽  
Nurwan Nurwan
Keyword(s):  
Goal Programming ◽  
Undergraduate Education ◽  
Optimal Solution ◽  
Education Level ◽  
Programming Method ◽  
Nurse Scheduling ◽  
Optimal Service ◽  
Schedule Design ◽  
Good Nurse ◽  
Better Than

Nurse scheduling is one of the problems that often arise in hospital management systems. Head of ICU room and nurse to cooperate in making good nurse scheduling for the creation of optimal service. In this paper, we study a hospital nurse schedule design by considering the level of nurse education and the provision of holidays. Nurses with undergraduate education (S1) Nurses become leaders on every shift and are accompanied by nurses with diploma education (D3). The scheduling model in this study using the nonpreemptive goal programming method and LINGO 11.0 software. The preparation of the schedule of nurses assigned to this method can optimize the need for efficient nurses per shift based on education level. The data in the research was obtained by collecting administrative data at Aloei Saboe Gorontalo hospital. The data used are the published schedule by the head of the ICU room. In making a nurse schedule, there are limitations to consider such ashospital regulation. The results of the study obtained an optimal solution in the form of meeting all the desired obstacles. Computational results shows that nurse scheduling using the nonpreemptive goal programming method and LINGO 11.0 software better than the schedule created manually. Every shift is a maximum of one leader with an undergraduate education (S1) background and accompanied by a nurse with a diploma education (D3) background. Keywords: scheduling, goal programming, nonpreemptive goal programming.

scholarly journals Where the action could be: Speakers look at graspable objects and meaningful scene regions when describing potential actions

2019 ◽  
Author(s):  
Gwendolyn L Rehrig ◽  
Candace Elise Peacock ◽  
Taylor Hayes ◽  
Fernanda Ferreira ◽  
John M. Henderson
Keyword(s):  
Visual Attention ◽  
Real World ◽  
Language Production ◽  
Native English Speakers ◽  
English Speakers ◽  
Linguistic Processing ◽  
General Meaning ◽  
New Evidence ◽  
Shed Light ◽  
Better Than

The world is visually complex, yet we can efficiently describe it by extracting the information that is most relevant to convey. How do the properties of real-world scenes help us decide where to look and what to say? Image salience has been the dominant explanation for what drives visual attention and production as we describe displays, but new evidence shows scene meaning predicts attention better than image salience. Here we investigated the relevance of one aspect of meaning, graspability (the grasping interactions objects in the scene afford), given that affordances have been implicated in both visual and linguistic processing. We quantified image salience, meaning, and graspability for real-world scenes. In three eyetracking experiments, native English speakers described possible actions that could be carried out in a scene. We hypothesized that graspability would preferentially guide attention due to its task-relevance. In two experiments using stimuli from a previous study, meaning explained visual attention better than graspability or salience did, and graspability explained attention better than salience. In a third experiment we quantified image salience, meaning, graspability, and reach-weighted graspability for scenes that depicted reachable spaces containing graspable objects. Graspability and meaning explained attention equally well in the third experiment, and both explained attention better than salience. We conclude that speakers use object graspability to allocate attention to plan descriptions when scenes depict graspable objects within reach, and otherwise rely more on general meaning. The results shed light on what aspects of meaning guide attention during scene viewing in language production tasks.

WikiServe: Using Wikipedia to Match IoT based Services for Situation Response

2021 ◽  
Author(s):  
Sazid Zaman Khan ◽  
Alan Colman ◽  
Iqbal H. Sarker
Keyword(s):  
Internet Of Things ◽  
Natural Disaster ◽  
Smart Devices ◽  
Baseline Method ◽  
Service Matching ◽  
Ontological Model ◽  
Ranked List ◽  
F Measure ◽  
The Web ◽  
Better Than

A large number of smart devices (things) are being deployed with the swift development of Internet of Things (IOT). These devices, owned by different organizations, have a wide variety of services to offer over the web. During a natural disaster or emergency (i.e., a situation), for example, relevant IOT services can be found and put to use. However, appropriate service matching methods are required to find the relevant services. Organizations that manage situation responses and organizations that provide IOT services are likely to be independent of each other, and therefore it is difficult for them to adopt a common ontological model to facilitate the service matching. Moreover, there exists a large conceptual gap between the domain of discourse for situations and the domain of discourse for services, which cannot be adequately bridged by existing techniques. In this paper, we address these issues and propose a new method, WikiServe, to identify IOT services that are functionally relevant to a given situation. Using concepts (terms) from situation and service descriptions, WikiServe employs Wikipedia as a knowledge source to bridge the conceptual gap between situation and service descriptions and match functionally relevant IOT services for a situation. It uses situation terms to retrieve situation related articles from Wikipedia. Then it creates a ranked list of services for the situation using the weighted occurrences of service terms in weighted situation articles. WikiServe performs better than a commonly used baseline method in terms of Precision, Recall and F measure for service matching.

The Role of the Computer in Team Problem-Solving: Critiquing or Partial Automation?

1993 ◽  
Vol 37 (15) ◽  
pp. 1029-1029
Author(s):  
Stephanie A. E. Guerlain ◽  
Philip J. Smith
Keyword(s):  
Problem Solving ◽  
Computer System ◽  
Real World ◽  
Automated System ◽  
Computer Interface ◽  
Rule Base ◽  
Heuristic Strategy ◽  
System Designs ◽  
Better Than

A testbed was developed for studying the effects of different computer system designs on human-computer team problem-solving, using the real-world task of antibody identification. The computer interface was designed so that practitioners could solve antibody identification cases using the computer as they normally would using paper and pencil. A rule-base was then encoded into the computer such that it had knowledge for applying a heuristic strategy that is often helpful for solving cases. With this testbed, studies have been run comparing different computer system designs. A critiquing system was found to be better than a partially automated system on cases where the computer's knowledge is incompetent.

scholarly journals Common Worship

2018 ◽  
Vol 35 (3) ◽  
pp. 299-325 ◽  
Author(s):  
Joshua Cockayne ◽  
David Efird ◽  
Keyword(s):  
Recent Work ◽  
Christian Tradition ◽  
Personal Preference ◽  
Corporate Worship ◽  
To Come ◽  
Better Than

People of faith, particularly in the Judeo-Christian tradition, worship corporately at least as often, if not more so, than they do individually. Why do they do this? There are, of course, many reasons, some having to do with personal preference and others having to do with the theology of worship. But, in this paper, we explore one reason, a philosophical reason, which, despite recent work on the philosophy of liturgy, has gone underappreciated. In particular, we argue that corporate worship enables a person to come to know God better than they would otherwise know him in individual worship.

Sign in / Sign up

Export Citation Format

Share Document