Domain-specific Approaches Research Articles

Algorithmic media use and algorithm literacy: An integrative literature review

Algorithms profoundly shape user experiences on digital platforms, raising concerns about their negative impacts and highlighting the importance of algorithm literacy. Research on individuals’ understanding of algorithms and their effects is expanding rapidly but lacks a cohesive framework. We conducted a systematic integrative literature review across social sciences and humanities (n = 169), addressing algorithm literacy in terms of its key conceptualizations and the endogenous, exogenous, and personal factors that influence it. We argue that existing research can be framed in terms of experiential learning cycles and outline how this approach can be beneficial for acquiring algorithm literacy. Finally, we propose a future research agenda that includes defining core competencies relevant to algorithm literacy, standardization of measures, integrating subjective and factual aspects of algorithm literacy, and task- and domain-specific approaches.

A SURVEY ON SUMMARIZATION OF LEGAL TEXTS

The task of summarizing legal texts is of paramount importance in facilitating access to legal knowledge. This survey paper critically reviews and analyzes diverse methodologies applied to the challenging task of summarizing legal texts. In the supervised realm, models such as GIST, SummaRuNNer, and BERTSUM have gained prominence, demonstrating prowess in extractive summarization techniques. Semi-supervised methods, leveraging innovative techniques such as TF-IDF Scores and Sentence Embeddings, have proven effective in handling the intricate structure of court judgments. Unsolicited approaches, CaseSummarizer and exemplified by USLT-LEGAL-BERT, address the challenges of summarization without the luxury of labeled data, showcasing their adaptability to diverse legal domains. Furthermore, the paper explores legal document retrieval strategies, incorporating document vector embeddings and deep learning techniques, underscoring the significance of domain-specific approaches. The study encompasses a variety of models, from Legal-BERT to hierarchical encoder decoder frameworks, showcasing their applicability in handling the complexity inherent in legal texts. Additionally, the survey delves into BERT-based techniques for legal classification tasks, emphasizing the crucial role of context and introducing innovative methods such as "Stride 64." In the realm of legal text simplification, the survey scrutinizes the KIS model and graph-based approaches, revealing nuanced trade- offs between readability, fluency, and meaning preservation. The research culminates with an exploration of the team's participation in LegalEval 2023 shared tasks, underscoring the prowess of deep learning models in tasks such as rhetorical role classification and legal named entity extraction.This survey paper provides a nuanced and comprehensive perspective on the evolving landscape of legal document summarization, offering valuable insights into current methodologies, challenges, and potential future directions for advancing the field. Keywords- Legal text Summarization,Supervised Learning, Extractive Summarization,Semi-supervised methods,Deep learning Techniques,Legal Document retrieval.

Clinical researchers’ insights on key data for eligibility screening in clinical studies

Abstract Introduction: Clinical research is critical for healthcare advancement, but participant recruitment remains challenging. Clinical research professionals (CRPs; e.g., clinical research coordinator, research assistant) perform eligibility prescreening, ensuring adherence to study criteria while upholding scientific and ethical standards. This study investigates the key information CRP prioritizes during eligibility prescreening, providing insights to optimize data standardization, and recruitment approaches. Methods: We conducted a freelisting survey targeting 150 CRPs from diverse domains (i.e., neurological disorders, rare diseases, and other diseases) where they listed essential information they look for from medical records, participant/caregiver inquiries, and discussions with principal investigators to determine a potential participant’s research eligibility. We calculated the salience scores of listed items using Anthropac, followed by a two-level analytic procedure to classify and thematically categorize the data. Results: The majority of participants were female (81%), identified as White (44%) and as non-Hispanic (64.5%). The first-level analysis universally emphasized age, medication list, and medical history across all domains. The second-level analysis illuminated domain-specific approaches in information retrieval: for instance, history of present illness was notably significant in neurological disorders during participant and principal investigator inquiries, while research participation was distinctly salient in potential participant inquiries within the rare disease domain. Conclusion: This study unveils the intricacies of eligibility prescreening, with both universal and domain-specific methods observed. Variations in data use across domains suggest the need for tailored prescreening in clinical research. Incorporating these insights into CRP training and refining prescreening tools, combined with an ethical, participant-focused approach, can advance eligibility prescreening practices.

Critical thinking for transformative praxis in teacher education: Music, media and information literacy, and social studies in the United States

The notion and practice of critical thinking (CT) has moved from its speculative formation by John Dewey to a standard element in teacher education curricula and standards. In the process, CT has narrowed its focus to the analysis and articulation of logical thought, and lost transformative value. In this paper, we examine the conception and implementation of CT in three teacher education domains primarily in the United States–music, media and information literacy, and social studies–asking how CT has deformed education in those domains, and how domain-specific approaches could reinvigorate CT. We further suggest refocusing the purpose of CT in teacher education on accomplishing transformative education for equity in school and society, by implementing a critically reflective, transformative praxis based on the insights of domain-specific approaches to CT.

Heuristic Search for Physics-Based Problems: Angry Birds in PDDL+

This paper studies how a domain-independent planner and combinatorial search can be employed to play AngryBirds, a well established AI challenge problem. To model the game, we use PDDL+, a planning language for mixed discrete/continuous domains that supports durative processes and exogenous events. The paper describes the PDDL+ model and identifies key design decisions that reduce the problem complexity. In addition, we propose several domain-specific enhancements including heuristics and a search technique similar to preferred operators. Together, they alleviate the complexity of combinatorial search. We evaluate our approach by comparing its performance with dedicated domain-specific solvers on a range of Angry Birds levels. The results show that our performance is on par with these domain-specific approaches in most levels, even without using our domain-specific search enhancements.

Optimality Certificates for Classical Planning

Algorithms are usually shown to be correct on paper, but bugs in their implementations can still lead to incorrect results. In the case of classical planning, it is fortunately straightforward to check whether a computed plan is correct. For optimal planning however, plans are additionally required to have minimal cost, which is significantly more difficult to verify. While some domain-specific approaches exists, we lack a general tool to verify optimality for arbitrary problems. We bridge this gap and introduce two approaches based on the principle of certifying algorithms, which provide a computer-verifiable certificate of correctness alongside their answer. We show that both approaches are sound and complete, analyze whether they can be generated and verified efficiently, and show how to apply them to concrete planning algorithms. The experimental evaluation shows that verifying optimality comes with a cost but is still practically feasible. Furthermore it confirms that the tested planner configurations provide optimal plans on the given instances, as all certificates were verified successfully.

A Network Intrusion Detection System for Building Automation and Control Systems

Building Automation and Control Systems (BACS) are traditionally based on specialized communications protocols, such as KNX or BACnet, and dedicated sensing and actuating devices. Despite the increased awareness about the security risks associated with BACS, there is a lack of security tools for protecting this special breed of cyber-physical systems. This is further aggravated by the fact that general-purpose security tools are typically not able to cope with the specific requirements and technologies associated with BACS, making it necessary to devise domain-specific approaches – as shown, for instance, by the KNX Secure initiative led by the KNX Association. Nevertheless, despite the advances brought by KNX Secure and similar initiatives, there is still a considerable gap between the security needs of BACS and the solutions available. In this paper, we address this gap by proposing a Network Intrusion Detection System (NIDS) specifically designed for BACS. This NIDS is protocol-agnostic and can potentially support different BACS protocols and technologies, such as KNX, BACnet, Modbus or mixed ecosystems, without loss of generality. We also present a specific proof-of-concept implementation of this NIDS concept for KNX – one of the more widespread BACS protocols. To this purpose, a real-world KNX deployment was used to showcase and evaluate the proposed approach.

Expert survey and classification of tools for modeling and simulating hybrid energy networks

Sector coupling is expected to play a key role in the decarbonization of the energy system by enabling the integration of decentralized renewable energy sources and unlocking hitherto unused synergies between generation, storage and consumption. Within this context, a transition towards hybrid energy networks (HENs), which couple power, heating/cooling and gas grids, is a necessary requirement to implement sector coupling on a large scale. However, this transition poses practical challenges, because the traditional domain-specific approaches struggle to cover all aspects of HENs. Methods and tools for conceptualization, system planning and design as well as system operation support exist for all involved domains, but their adaption or extension beyond the domain they were originally intended for is still a matter of research and development. Therefore, this work presents innovative tools for modeling and simulating HENs. A categorization of these tools is performed based on a clustering of their most relevant features. It is shown that this categorization has a strong correlation with the results of an independently carried out expert review of potential application areas. This good agreement is a strong indicator that the proposed classification categories can successfully capture and characterize the most important features of tools for HENs. Furthermore, it allows to provide a guideline for early adopters to understand which tools and methods best fit the requirements of their specific applications.

Deepdive: a learning-based approach for virtual camera in immersive contents

A 360° video stream provide users a choice of viewing one's own point of interest inside the immersive contents. Performing head or hand manipulations to view the interesting scene in a 360° video is very tedious and the user may view the interested frame during his head/hand movement or even lose it. While automatically extracting user's point of interest (UPI) in a 360° video is very challenging because of subjectivity and difference of comforts. To handle these challenges and provide user's the best and visually pleasant view, we propose an automatic approach by utilizing two CNN models: object detector and aesthetic score of the scene. The proposed framework is three folded: pre-processing, Deepdive architecture, and view selection pipeline. In first fold, an input 360° video-frame is divided into three subframes, each one with 120° view. In second fold, each sub-frame is passed through CNN models to extract visual features in the sub-frames and calculate aesthetic score. Finally, decision pipeline selects the subframe with salient object based on the detected object and calculated aesthetic score. As compared to other state-of-the-art techniques which are domain specific approaches i.e., support sports 360° video, our system support most of the 360° videos genre. Performance evaluation of proposed framework on our own collected data from various websites indicate performance for different categories of 360° videos.

Examining the Predictive Validity of the Grit Scale-Short (Grit-S) Using Domain-General and Domain-Specific Approaches With Student-Athletes.

This paper contributes to the debate as to whether grit is best conceptualized and measured as a domain-specific or domain-general construct. In the field of sport psychology, grit has traditionally been conceptualized and measured as a domain-general construct, with the majority of studies using the Grit Scale-Short (Grit-S: Duckworth and Quinn, 2009) to assess grit and its relationships with an array of personality-, performance-, and health-related outcomes. To date, no studies have compared the predictive validity of domain-general and domain-specific versions of the Grit-S with athletes who operate in different achievement settings. In a sample of United Kingdom student-athletes (N = 326, 214 males, 112 females; Mage = 19.55 years, SD = 1.48 years), we examined the degree to which a domain-general version and two domain-specific versions of the Grit-S accounted for variance in two criterion variables that were either situated in an academic context (i.e., emotional exhaustion) or a sport context (i.e., competitive level). Results obtained from a series of hierarchical multiple regression analyses indicated that an academic-version of the Grit-S explained unique variance in academic emotional exhaustion beyond the variance explained by the domain-general version of the scale, and a sport-version of the Grit-S explained unique variance in competitive level beyond the variance explained by the domain-general version. Results support the adoption of domain-specific approaches to measure grit in specific achievement contexts. Our findings highlight the need for researchers to carefully consider the measurement approaches they adopt when studying grit in individuals who operate across different achievement settings.

Interdisciplinary Data-integrated Approaches

Rapid urbanization with the associated land cover and land use change, as well as resource depletion, contribute to the degradation of ecosystems and biodiversity and have a negative impact on human health and well-being. Societal calls for responses and results pose a significant challenge for research and education in the various fields concerned with the environment. Alongside the current environmental crisis there is a pressing need for developing ‘green solutions’ for the built environment with the help of datadriven methods, workflows and tools.
 In view these developments, a shift from narrow disciplinary and domain-specific approaches towards broader interdisciplinary, multi-domain and multi-scalar strategies is required. This includes dataacquisition, data-sharing and data-integration, as well as data-driven modelling to enable the complexity of sustainability problems arising from rapid urbanization to be tackled. While there have been efforts to address the challenges of multi-domain approaches, for instance in the fields of sustainability, the urban and architectural sciences, as well as the interoperability of methods and tools, the actual problem goes deeper, requiring interdisciplinary knowledge exchange to develop adequate shared paradigms, concepts, methods and tools.
 Cyber-physical Architecture (CpA) issue 5 addresses these challenges by engaging with experts from a range of disciplines involved in environmental concerns while utilizing data-acquisition, data-sharing and integration, and data-driven modelling in a discourse that identifies modalities for a broader interdisciplinary, multi-domain and multi-scalar approach.

A novel computer-aided diagnosis framework for EEG-based identification of neural diseases

Recent advances in electroencephalogram (EEG) signal classification have primarily focused on domain-specific approaches, which impede algorithm cross-discipline capability. This study introduces a new computer-aided diagnosis (CAD) system for the classification of two distinct EEG domains under a unified sequential framework. The key motivation to consider two neural diseases by one framework is to develop a unified algorithm for EEG classification. The main contributions of this study are five-fold. First, EEG signals are decomposed into 10 intrinsic mode functions (IMFs) with the help of empirical wavelet transform. Second, a novel two-dimensional (2D) modeling of IMFs is plotted to visualize the complexity of EEG signals. Third, several new geometrical features are extracted to analyze the dynamic and chaotic essence. Fourth, significant features are selected by binary particle swarm optimization algorithm (B–PSO). Fifth, selected features are fed to the k-nearest neighbor classifier for EEG signal classification purposes. All the experiments are executed on one depression and two epileptic EEG datasets in a leave one out cross-validation strategy. The proposed CAD system provides an average classification accuracy of 93.35% in depression detection, 99.33% for regular against ictal, and 97.33% for interictal versus ictal respectively. The overall empirical analysis authenticates that the proposed CAD outperforms the existing domain-specific methods in terms of classification accuracies and multirole adaptability, thus, can be endorsed as an effective automated neural rehabilitation system.

Embedding and learning with signatures

Sequential and temporal data arise in many fields of research, such as quantitative finance, medicine, or computer vision. A novel approach for sequential learning, called the signature method and rooted in rough path theory, is considered. Its basic principle is to represent multidimensional paths by a graded feature set of their iterated integrals, called the signature. This approach relies critically on an embedding principle, which consists in representing discretely sampled data as paths, i.e., functions from [0,1] to Rd. After a survey of machine learning methodologies for signatures, the influence of embeddings on prediction accuracy is investigated with an in-depth study of three recent and challenging datasets. It is shown that a specific embedding, called lead–lag, is systematically the strongest performer across all datasets and algorithms considered. Moreover, an empirical study reveals that computing signatures over the whole path domain does not lead to a loss of local information. It is concluded that, with a good embedding, combining signatures with other simple algorithms achieves results competitive with state-of-the-art, domain-specific approaches.

Breaking the ice: Use of music improvisation to facilitate interprofessional communication

Abstract Uni-professional socialization is an expected outcome of professional training; however, this development can pose a barrier to interprofessional education (IPE) and interprofessional practice (IPP). As students progress in their educational and clinical training, they become acculturated in their professional ‘language’ and in domain-specific approaches to problem solving. An unintended consequence is that specialized terminology and narrow perspectives shaped by a single discipline can interfere with effective interprofessional collaboration and lower the quality of healthcare services. We introduced the Universal Language Room (ULR), an original IPE icebreaker designed to facilitate communication at its most fundamental level. The activity served as a metaphor for initiating effective cross-disciplinary interactions and preventing deformation professionelle. Immediate debriefing and written feedback prompted students to reflect upon the activity, why it was used, and what it achieved. Qualitative analysis of responses indicated that the experience helped students understand and appreciate key IPE objectives. Overall, results suggest that the ULR helped ‘break the ice’ during a pilot course in IPE and can be a useful model for promoting effective communication across disciplines.

Impact of individual actions on the collective response of social systems

In a social system individual actions have the potential to trigger spontaneous collective reactions. The way and extent to which the activity (number of actions—A) of an individual causes or is connected to the response (number of reactions—R) of the system is still an open question. We measure the relationship between activity and response with the distribution of efficiency, a metric defined as eta =R/A. Generalizing previous results, we show that the efficiency distribution presents a universal structure in three systems of different nature: Twitter, Wikipedia and the scientific citations network. To understand this phenomenon, we develop a theoretical framework composed of three minimal statistical models that contemplate different levels of dependence between A and R. The models not only are able to reproduce the empirical activity-response data but also can serve as baselines or null models for more elaborated and domain-specific approaches.

Çok Dilli Metin Analizinde Alan Bağımlı Değerlendirme Verisinin Oluşturulması

Web enables to retrieve concise information about specific entities including people, organizations, movies and their features. Additionally, large amount of Web resources generally lies on a unstructured form and it tackles to find critical information for specific entities. Text analysis approaches such as Named Entity Recognizer and Entity Linking aim to identify entities and link them to relevant entities in the given knowledge base. To evaluate these approaches, there are a vast amount of general purpose benchmark datasets. However, it is difficult to evaluate domain-specific approaches due to lack of evaluation datasets for specific domains. This study presents WeDGeM that is a multilingual evaluation set generator for specific domains exploiting Wikipedia category pages and DBpedia hierarchy. Also, Wikipedia disambiguation pages are used to adjust the ambiguity level of the generated texts. Based on this generated test data, a use case for well-known Entity Linking systems supporting Turkish texts are evaluated in the movie domain.

Applied Veterinary Informatics: Development of a Semantic and Domain-Specific Method to Construct a Canine Data Repository

Animals are used to study the pathogenesis of various human diseases, but typically as animal models with induced disease. However, companion animals develop disease spontaneously in a way that mirrors disease development in humans. The purpose of this study is to develop a semantic and domain-specific method to enable construction of a data repository from a veterinary hospital that would be useful for future studies. We developed a two-phase method that combines semantic and domain-specific approaches to construct a canine data repository of clinical data collected during routine care at the Matthew J Ryan Veterinary Hospital of the University of Pennsylvania (PennVet). Our framework consists of two phases: (1) a semantic data-cleaning phase and (2) a domain-specific data-cleaning phase. We validated our data repository using a gold standard of known breed predispositions for certain diseases (i.e., mitral valve disease, atrial fibrillation and osteosarcoma). Our two-phase method allowed us to maximize data retention (99.8% of data retained), while ensuring the quality of our result. Our final population contained 84,405 dogs treated between 2000 and 2017 from 194 distinct dog breeds. We observed the expected breed associations with mitral valve disease, atrial fibrillation, and osteosarcoma (P < 0.05) after adjusting for multiple comparisons. Precision ranged from 60.0 to 83.3 for the three diseases (avg. 74.2) and recall ranged from 31.6 to 83.3 (avg. 53.3). Our study describes a two-phase method to construct a clinical data repository using canine data obtained during routine clinical care at a veterinary hospital.

A Pattern-Based Development Approach for Interaction Flow Modeling Language

Development and deployment technologies for data-intensive web applications have considerably evolved in the last years. Domain-specific frameworks or model-driven web engineering approaches are examples of these technologies. They have made possible to face implicit problems of these systems such as quick evolving business rules or severe time-to-market requirements. Both approaches propose the automation of redundant development tasks as the key factor for their success. The implementation of CRUD operations is a clear example of repetitive and recurrent task that may be automated. However, although web application frameworks have provided mechanisms to automate the implementation of CRUD operations, model-driven web engineering approaches have generally ignored them, so automation has not been properly faced yet. This paper presents a pattern-based development approach for the Interaction Flow Modeling Language as a way to finally automate repetitive specification tasks. Our approach is illustrated by defining and applying IFML patterns for CRUD operations. Additionally, a supporting tool, which enables automation, is shown. The suitability of our approach and the utility of its tool have been evaluated by its application into several real projects developed by a software company specialized in model-driven web application development. The results obtained present evidence of a significant productivity improvement obtained by the automation of the IFML specification of CRUD operations.

Numerically accurate code synthesis for Gauss pivoting method to solve linear systems coming from mechanics

In numerical analysis of mechanical problems, we usually have to solve huge linear systems, which may be non-symmetric or ill-conditioned. For these reasons, it is necessary to develop original and domain specific approaches to treat these family of systems. In this work, we introduce a new methodology to synthesize numerically accurate programs for the Gauss pivoting method. The synthesis is based on program transformation techniques and it is guided in its estimation of accuracy by interval arithmetic that computes the propagation of roundoff errors. We apply our code synthesis to the resolution of systems coming from finite element method arising from problems of Mechanics. We test our synthesizer on two problems concerning the flexion of a beam and the sliding contact of a viscoelastic body on a rigid foundation. Our experimental results show that the specialized synthesized code to solve the families of systems given in input is far more accurate and faster than the standard implementation of the Gauss method.

GAMS – An infrastructure for the long-term preservation and publication of research data from the Humanities

Research data repositories and data centres are becoming more and more important as infrastructures in academic research. The article introduces the Humanities’ research data repository GAMS, starting with the system architecture to preservation policy and content policy. Challenges of data centres and repositories and the general and domain-specific approaches and solutions are outlined. Special emphasis lies on the sustainability and long-term perspective of such infrastructures, not only on the technical but above all on the organisational and financial level.