Expert Finding Research Articles

Efficient and Effective Academic Expert Finding on Heterogeneous Graphs through ( k , 𝒫)-Core based Embedding

Expert finding is crucial for a wealth of applications in both academia and industry. Given a user query and trove of academic papers, expert finding aims at retrieving the most relevant experts for the query, from the academic papers. Existing studies focus on embedding-based solutions that consider academic papers’ textual semantic similarities to a query via document representation and extract the top- n experts from the most similar papers. Beyond implicit textual semantics, however, papers’ explicit relationships (e.g., co-authorship) in a heterogeneous graph (e.g., DBLP) are critical for expert finding, because they help improve the representation quality. Despite their importance, the explicit relationships of papers generally have been ignored in the literature. In this article, we study expert finding on heterogeneous graphs by considering both the explicit relationships and implicit textual semantics of papers in one model. Specifically, we define the cohesive ( k , 𝒫)-core community of papers w.r.t. a meta-path 𝒫 (i.e., relationship) and propose a ( k , 𝒫)-core based document embedding model to enhance the representation quality. Based on this, we design a proximity graph-based index (PG-Index) of papers and present a threshold algorithm (TA)-based method to efficiently extract top- n experts from papers returned by PG-Index. We further optimize our approach in two ways: (1) we boost effectiveness by considering the ( k , 𝒫)-core community of experts and the diversity of experts’ research interests, to achieve high-quality expert representation from paper representation; and (2) we streamline expert finding, going from “extract top- n experts from top- m ( m> n ) semantically similar papers” to “directly return top- n experts”. The process of returning a large number of top- m papers as intermediate data is avoided, thereby improving the efficiency. Extensive experiments using real-world datasets demonstrate our approach’s superiority.

Semi-Supervised Graph Pattern Matching and Rematching for Expert Community Location

Graph pattern matching (GPM) is widely used in social network analysis, such as expert finding, social group query, and social position detection. Technically, GPM is to find matched subgraphs that meet the requirements of pattern graphs in big social networks. In the application of expert community location, the nodes in the pattern graph and data graph represent expert entities, and the edges represent previous cooperations between them. However, the existing GPM methods focus on shortening the matching time and without considering the preference of the decision maker (DM), which makes it difficult for the DM to find ideal teams from numerous matches to complete the assigned task. In this article, as for the process of graph pattern matching and rematching, with a preferred expert set, i.e., the DM hopes that one or more experts in this set will appear in matched subgraphs, we propose a Dual Simulation-based Edge Sequencing-oriented Semi-Supervised GPM method (DsEs-ssGPM). In addition, considering a preferred expert set and a dispreferred expert set together, the DM hopes that experts in the dispreferred expert set will not appear in final matches, so we have the DsEs-ssGPM+ method. Technically, these DsEs-ssGPM methods conduct the matching process from the preferred expert set during dual simulation-based edge sequencing, and based on the edge sequence, these edges are searched recursively. Especially, as for the rematching process, when the preferred and/or the dispreferred expert sets change continuously, to process the GPM again is unnecessary and it is possible to revise the previous matched results partially with DsEs-ssGPM methods. Experiments on four large datasets demonstrate the effectiveness, efficiency and stability of our proposed DsEs-ssGPM methods, and the necessity of introducing an edge sequencing mechanism.

A Model to Identify Redundancy and Relevancy in Question-Answer Systems of Digital Scholarly Platforms

Many digital scholarly platforms allow researchers to conduct scientific discussions using their question-answer systems. Such discussions are helpful for emerging as well as established researchers in numerous prospects. In order to rely on such discussions, it is essential to effectively validate these questions and answers based on content as well as context. This will help in finding ‘quality’ content, improving user experience for researchers and acquiring efficient results in realistic applications such as influence analysis, topic modeling, expert finding and recommendation systems. In this research, one of the well-known scholarly platforms, ResearchGate (RG) is focused. Questions and answers of 14,000 researchers from RG are rendered. A qualitative analysis is performed to perceive various anomalies in the rendered data. To resolve these anomalies, we propose a Word2Vec based model that identifies redundancy and relevancy in questions and answers using content as well as context analysis. The proposed model is evaluated on the rendered data and the outcome suggests that the model accurately identifies redundant questions and irrelevant answers from scholarly platforms.

Problems and prospects of technocratic model of government

The paper deals with technocracy as one of the ways to organize the political life of society and variants of the state system. The paper analyzes the concept of technocracy and provides an overview of the main classical concepts of technocracy. It states that the concept of technocracy does not have an unambiguous definition, but has three basic features. Classical technocracy relies on the principle of exclusivity and the ideal of aristocratic rule laid down in Plato’s writings. The paper describes a new -deliberative model of technocracy, tailored on the principle of inclusiveness. Deliberative technocracy is a kind of technocracy by which political (and, in particular, state) decisions are taken based on expert opinion. Expert opinion is shaped following intellectual operations and is delineated in expert findings. Deliberative technocracy proclaims neither the power of abstract demos (mass men) nor the power of intellectual aristocracy. Deliberative technocracy brings a new force onto the political stage, otherwise referred to as experts. Thus, deliberative technocracy implies the majority rule, not the minority... but it is the rule of a qualified and competent majority.

A deep learning-based expert finding method to retrieve agile software teams from CQAs

Currently, many software companies are looking to assemble a team of experts who can collaboratively carry out an assigned project in an agile manner. The most ideal members for an agile team are T-shaped experts, who not only have expertise in one skill-area but also have general knowledge in a number of related skill-areas. Existing related methods have only used some heuristic non-machine learning models to form an agile team from candidates, while machine learning has been successful in similar tasks. In addition, they have only used the number of candidates’ documents in various skill-areas as a resource to estimate the candidates’ T-shaped knowledge to work in an agile team, while the content of their documents is also very important. To this end, we propose a multi-step method that rectifies the drawbacks mentioned. In this method, we first pick out the best possible candidates using a state-of-the-art model, then we re-estimate their relevant knowledge for working in the team with the help of a deep learning model, which uses the content of the candidates’ posts on StackOverflow. Finally, we select the best possible members for the given agile team from among these candidates using an integer linear programming model. We perform our experiments on two large datasets C# and Java, which comprise 2,217,366 and 2,320,883 posts from StackOverflow, respectively. On datasets C# and Java, our method selects, respectively, 68.6% and 55.2% of the agile team members from among T-shaped experts, while the best baseline method only selects, respectively, 49.1% and 40.2% of the agile team members from among T-shaped experts. In addition, the results show that our method outperforms the best baseline method by 8.1% and 11.4% in terms of F-measure on datasets C# and Java, respectively.

Topic-sensitive expert finding based solely on heterogeneous academic networks

Identifying experts in a specific research field is an essential and practical task in academia and industry. Although some efforts have been attempted for expert finding, they rely on abundant text mining to reveal the target research topic accurately. Furthermore, it is time-consuming to collect and process these text materials for each candidate, and it is difficult to prepare a precise description for a topic that can cover all sub-topics. To this end, in this study, we develop a new expert finding model called TOpic-Sensitive representative Author identification (TOSA), to find representative authors in a specific research topic from a heterogeneous topic-insensitive academic network involving various topics, with a few well-known experts collected previously who are professional in the target topic. Specifically, we first learn the high-order relationships about authors in the heterogeneous academic network, which contain enough topic-related information. Next, an embedding space is found, where the collected experts are close to each other; in other words, the area they gather indicates the features about the collected authoritative experts. Finally, a prototype is calculated based on the collected experts in the embedding space, and we can rank other unlabeled authors by measuring the closeness between them and the prototype. Along this line, in addition to the collected experts in advance, more authoritative experts in the target topic will be identified. Extensive experiments on DBLP and ACM datasets demonstrate that the proposed TOSA model can identify experts who are professional in the query topic directly from a heterogeneous topic-insensitive academic network without text data processing, and its performance is significantly superior to the baseline models.

Interval-Valued Intuitionistic Fuzzy Decision With Graph Pattern in Big Graph

Graph pattern matching (GPM) in big graph has been widely used in decision making, such as expert finding, social group discovery, etc. However, these existing works consider neither the preference of the decision maker (DM), nor the subjectivity of constraints during the process of GPM. Therefore, this paper proposes an interval-valued intuitionistic fuzzy decision (IVIFD) with graph pattern in big graph. As traditionally IVIFD can maximally reduce the uncertainty of decision making and it is only valid for small datasets, which makes it impossible to be applied in big graph. In this paper, GPM is adopted to prune the searching space, which makes it possible to process IVIFD under the preference of the DM later. Technically, firstly, each DM selects the preferred vertices and/or edges in big graph, and the interval-valued intuitionistic fuzzy preference (IVIFP) is calculated and used to form the contextual constraints to conduct GPM. Secondly, a probability-certainty density function is introduced to capture the subjective probability of the contextual preference of subgraphs via the bijection from rating space of the context to preference space of the context, which leads to an interval-valued intuitionistic fuzzy set (IVIFS). In addition to the IVIFS, the IVIFD is made through interval-valued intuitionistic fuzzy cross entropy and grey relation degree. Moreover, the weight problem between different contexts is taken into account and handled respectively as three cases. Finally, numerical experiments and perturbation analysis validate the effectiveness and stability of our proposed method, and verify its necessity and efficiency through ablation experiments.

THE FORMATION OF A FOREIGN-LANGUAGE COMPETENCE IN READING BY MEANS OF GERMAN CLASSICS` AUTHENTIC TEXTS

The article deals with the methodology of developing foreign language competence in reading on the basis of the authentic text of the classic German literature author. Three Comrades, a novel by E.M. Remarque, is considered a classic of world literature, as it touches upon important topics universal for all generations - human relationships, friendship and love. We have processed 385 pages of the authentic novel with 28 chapters and a total number of 199430 units of word usage, and analyzed the findings of domestic and foreign experts on methods of forming foreign language competence in reading. To meet the objectives, the main stages of the incremental formation of communicative competence in reading were identified, interactive techniques were proposed together with a subsystem of exercises and tasks for each of the stages. When developing practical exercises and tasks for the work at the first stage, it was found that the use of such tools as the interactive method of clusters, direct replacement of words, mnemonic methods and didactic-methodological principle of personalization, has a positive effect on the removal of difficulties of linguistic and semantic nature and enables the beginning of successful work with the text. The article presents different types of reading and shows that introductory home reading is optimal for reading authentic literature. In practice, it has been found that the method of heuristic conversation is the main technique for the development of students' communication at the third stage; the auxiliary methods are the research method, analysis of stories and situations, mental map and analysis of the protagonists. It is proved that the most efficient methods for the development of foreign language competence in reading are dramatization of what has been read, and the method of express debate, which has a positive effect on the development of critical thinking, fluency and stating one's opinion. Validity of the obtained results is confirmed by statistically significant results by calculating the chi-square criterion and the mutual conjugation coefficient. The efficiency of the proposed methods and subsystem of exercises contributes to overcoming the language barrier, enriching the vocabulary, the ability to express one' own opinion and allows using textual information in various situations in life.

Towards comprehensive expert finding with a hierarchical matching network

In Community Question Answering (CQA) websites, expert finding aims to seek relevant experts for answering questions. The core of expert finding is to match candidate experts and target questions precisely. Most existing methods usually learn a single feature vector for the expert from the historically answered questions, and then match the target question, which would lose fine-grained and low-level semantic matching information. In this paper, instead of matching with a unified expert embedding, we propose an expert finding method with a multi-grained hierarchical matching framework, named EFHM. Specifically, we design a word-level and question-level match encoder to learn the fine-grained semantic matching between each historical answered question and target question, and then propose an expert-level match encoder to learn an overall expert feature for matching the target question. Through the hierarchical matching mechanism, our model has the potential to capture the comprehensive relevance between candidate experts and target questions. Experimental results on six real-world CQA datasets demonstrate that the proposed method could achieve better performance than existing state-of-the-art methods.

ExpFinder: A hybrid model for expert finding from text-based expertise data

Finding an expert plays a crucial role in driving successful collaborations and speeding up high-quality research development and innovations. However, the rapid growth of scientific publications and digital data makes identifying the right experts a challenging problem. Existing approaches for finding experts given a topic can be categorised into information retrieval techniques such as vector space models, document language models, and graph-based models. In this paper, we propose ExpFinder , a new hybrid model for expert finding, that integrates a novel N-gram vector space model, denoted as nVSM, and a graph-based model, denoted as μCO-HITS, that is a proposed variation of the CO-HITS algorithm. The key of nVSM is to exploit recent inverse document frequency weighting method for N-gram words, and ExpFinder incorporates nVSM into μCO-HITS to achieve expert finding. We comprehensively evaluate ExpFinder on four different datasets from the academic domains in comparison with six different expert finding models. The evaluation results show that ExpFinder is an highly effective model for expert finding, substantially outperforming all the compared models in 19% to 160.2%.

From Visual Art to Music: Sonification Can Adapt to Painting Styles and Augment User Experience

Advances in the fields of data processing and sonification have been applied to transcribe a variety of visual experiences into an auditory format. Although image sonification examples exist, the application of these principles to visual art has not been examined thoroughly. We sought to develop and evaluate a set of guidelines for the sonification of visual artworks. Through conducting expert interviews (N = 11), we created an initial sonification algorithm that accounts for art style, lightness, and color diversity to modulate the sonified output in terms of tempo and pitch. This algorithm was evaluated through user evaluations (N = 22). User study responses supported expert interview findings, the notion that sonification can be designed to match the experience of viewing an artwork, and showed interesting interaction effects among art styles, visual components, and musical parameters. We suggest the proposed guidelines can augment visitor experiences at art exhibits and provide the basis for further experimentation.

CODE: Contrastive Pre-training with Adversarial Fine-Tuning for Zero-Shot Expert Linking

Expert finding, a popular service provided by many online websites such as Expertise Finder, LinkedIn, and AMiner, is beneficial to seeking candidate qualifications, consultants, and collaborators. However, its quality is suffered from lack of ample sources of expert information. This paper employs AMiner as the basis with an aim at linking any external experts to the counterparts on AMiner. As it is infeasible to acquire sufficient linkages from arbitrary external sources, we explore the problem of zero-shot expert linking. In this paper, we propose CODE, which first pre-trains an expert linking model by contrastive learning on AMiner such that it can capture the representation and matching patterns of experts without supervised signals, then it is fine-tuned between AMinerand external sources to enhance the model’s transferability in an adversarial manner. For evaluation, we first design two intrinsic tasks, author identification and paper clustering, to validate the representation and matching capability endowed by contrastive learning. Then the final external expert linking performance on two genres of external sources also implies the superiority of adversarial fine-tuning method. Additionally, we show the online deployment of CODE, and continuously improve its online performance via active learning.

EXPERT ADVISOR IN CRIMINAL PROCEEDINGS

The new provisions of the criminal procedure legislation of the RS have brought certain changes and novelties about the role of psychiatrists in criminal proceedings. Namely, in addition to appearing in the status of a court expert, the current legal solutions also enable the direct involvement of psychiatrists in criminal proceedings in the capacity of an expert advisor. In this way, preconditions have been created so that experts in the field of expertise can critically examine the expert's findings, which created preconditions for better professional control of expertise, confrontation of expert opinions, with the basic goal of providing the court with the most objective psychiatric truth in decision-making. judgments. The aim of this paper is to present the legal and practical position of psychiatrists as professional advisors in criminal proceedings, advantages and disadvantages, both in the legal solution and in practical work in individual court proceedings. The paper will also present some examples that will illustrate certain obstacles facing psychiatric counselors, both in providing professional assistance to the party that hired them, and in the most objective view of existing psychiatric circumstances in specific court proceedings. The paper contains the personal view of the author on the current codes in this area, as well as experiences from engaging in specific criminal proceedings, with the basic goal of creating better conditions for those who make legal decisions to better assess the facts in the field of psychiatry and pass objective and expertly reasoned judgments.

Participative or Directive Leadership Behaviors for Decision-Making in Crisis Management Teams?

We assess the relative value of participative and directive leadership for improving the accuracy and speed of decision-making in crisis management teams, contingent on whether teams face an emergency that is familiar or unfamiliar to them. Testing our theory, using randomized experiments, with 72 teams tasked with managing simulated crises, we found that participative leadership improves decision accuracy in unfamiliar emergencies, whereas directive leadership improves accuracy in familiar crises; directive leadership produces speedier decisions than participative leadership when the team is familiar with the crisis. We discuss implications of our findings for leaders and crisis management experts.

High-quality domain expert finding method in CQA based on multi-granularity semantic analysis and interest drift

Expert finding is an important research field in community question answering (CQA). Traditional expert finding methods mainly exploit topic analysis and authority calculation methods to identify high-quality experts in certain fields. To avoid recommending questions to those experts who do not display the willingness or ability to provide high-quality answers, user interest drift and user quality should be considered. This study proposes a novel method named high-quality domain expert finding in CQA based on multi-granularity semantic analysis and interest drift (HQExpert). Firstly, HQExpert considers different semantic granularities by employing two models, a coarse-grained topic model LC-LDA and a fine-grained model (BERT), to capture the domain information of questions and users more accurately. Secondly, to address the diverse interests of the users, a user interest drift model in HQExpert is developed to dynamically represent the changes in the interests of the users at different periods. In addition, a user quality model is developed to further optimize the professional level of the user, finding experts who can provide high-quality answers and are interested in the current question. Finally, extensive experiments on two datasets from different domains demonstrate that the proposed HQExpert model can significantly improve the accuracy of finding high-quality experts.

Expert participation in criminal proceedings

The scientific article is devoted to determining the legal status of an expert in criminal proceedings and problematic issues related to it. The development of becoming an expert began in ancient times and is covered by various legal acts.
 This article considers various scientific formulations of definition of expert. These formulations make it possible to identify the main features that inherent in the person. It is concluded that the most accurate and complete can be considered the legislative definition of the expert, which is contained in Art. 69 of the Criminal Procedure Code of Ukraine. Emphasis is placed on the lack of pre-defined force of the expert's finding.
 To properly understand the concept of expert, the discussion issue related to the category of "special knowledge" is analyzed, the interpretation of which is missing in the regulations of Ukraine. The peculiarities of special knowledge and the importance of ways to acquire them are indicated.
 In addition, considerable attention is paid to the expert’s legal personality, as it helps to determine his criminal status. Therefore, it is considered the legal capacity of the expert as a participant in criminal proceedings and his legal capacity and tort capability.
 Despite the possibility of using the expert's finding as a procedural source of evidence, there are a number of problematic issues, that are reflected in the lack of implementation the expert's right to appeal the actions of the person that leads the case and violates the expert's rights; the complexity of the procedure for gathering proofs by the defense by conducting certain investigative actions, that includes the involvement of an expert; lack of a list of grounds and procedures for involving an expert; inability to engage a legal expert.
 The results of the article emphasize the need to improve the criminal procedure legislation in the appropriate direction, which will make the participation of the expert in criminal proceedings more effective.

Attention-based skill translation models for expert finding

The growing popularity of community question answering websites can be seen by the growing number of users. Many methods are proposed to identify talented users in these communities, but many of them suffer from vocabulary mismatches. The solution to this problem can be found in translation approaches. The present paper proposes two translation methods for extracting more relevant translations. The proposed methods rely on the attention mechanism. The methods use multi-label classifiers that take each question as input and predict the skills related to the question. Using the attention mechanism, the model is able to focus on specific parts of the given input and predict the correct labels. The ultimate goal of these networks is to predict skills related to questions. Using word attention scores, we can find out how relevant a single word is to a particular skill. As a result of these attention scores, we obtain more relevant translations for each skill. We then use these translations to bridge the lexical gap and improve expert retrieval results. Extensive experiments on two large sub-collections of the StackOverflow dataset demonstrate that the proposed methods outperform the best baseline method by up to 14.11/% MAP improvement.

Rilonacept for the treatment of recurrent pericarditis

ABSTRACT Introduction Recurrent pericarditis (RP) is a debilitating disease that has an underlying autoinflammatory pathophysiology mediated by cytokine interleukin (IL)-1. Rilonacept, a recombinant dimeric fusion protein that blocks IL-1α and IL-1β signaling has emerged as a valuable therapeutic option of RP. Rilonacept has been evaluated in Phase 2 and 3 clinical trials and was recently approved for RP treatment. Areas covered This article reviews available clinical trials assessing the efficacy and safety of rilonacept for the treatment of RP. Expert opinion Findings from the Rhapsody study) trial suggest that rilonacept represents a promising new therapy for those patients with colchicine resistant or glucocorticoid-dependent disease. Treatment leads to rapid clinical response, with a median resolution of symptoms in 5 days, normalization of C-reactive protein (CRP) in a median of 7 days, and successful weaning from glucocorticoids. This novel therapy also reduces recurrence rates compared with placebo. Rilonacept has also demonstrated a good safety profile, with the most common adverse events including injection-site reactions and upper respiratory tract infections. This anti-IL 1 agent has emerged as an efficacious treatment for RP, with potential use for glucocorticoid-free regimens and as monotherapy. Future trials are needed to explore these treatment options and to clarify the appropriate therapy duration.

Different Tools Used for Treatment of Chronic Hepatitis B

Currently different vaccines are available for cure and medicines are also available since 1988 but still 250 million people are infected with hepatitis B virus. The viruses of hepatitis B and hepatitis C causes liver cancer and also tuberculosis and malaria. The well developed and under developing countries both are very poor in care that can reduce the risk of hepatitis B. If we talk about United States only one third of those people who are infected with this virus are aware of their infection. The main reason for this un awareness is that the asymptomatic nature of hepatitis B virus as the symptoms are observed when it is at the chromic stage. The patients and care providers both lack knowledge regarding this disease. More attention has been focused from the previous 5 years in order to improve care linked to this disease, its proper diagnosis and proper care. There are also some novel compounds in trial phases to functionally cure this disease. There are also some professional organizations that provide knowledge and guidelines regarding this disease. These steps help those areas in which there is lack of knowledge regarding this disease and also they don’t have proper cure of this disease. The findings of global experts are used to fulfill the gap that is occurring in this regard.

Reconstruction and validation of head trauma accidents based on legal medicine data

The autopsy is performed when a case is suspected, and the autopsy physician makes an educated guess as to the circumstances of the accident, which is the decisive factor in determining the case. Conversely, in head trauma cases, the injury situation can be inferred by simulation from the mechanical point of view. In this study, we estimated the behavior of the victim by whole-multi-body analysis, calculate the mechanical responses in the brain at the time of collision by head finite element analysis, and compared the calculated results with the location of the victim’s injury to verify the validity of accident reconstruction. In a hit-and-run case, we estimated the violations traffic regulations, the vehicle type, the vehicle speed, and the relative position of the victim and the vehicle. The results demonstrated the possibility of quantitative evaluation by adding mechanical information to the opinion findings of forensic experts.