Knowledge Graph Analysis of Internal Control Field in Colleges

Knowledge graph is a new method to describe concepts, instances and their relationships in the objective world. In recent years, it has attracted people\u27s wide attention. Knowledge graph can effectively expand the breadth of search results. At present, in the field of internal control of colleges and universities, the key word search technology is mainly used to retrieve relevant knowledge content. It is difficult to retrieve information by using the relation between objects. Therefore, this paper first proposes a knowledge graph construction method for internal control in universities, through which the knowledge graph of internal control policy in universities can be constructed. Then, this paper proposes a knowledge inference method based on inference rules, which USES the knowledge hidden in the knowledge graph of internal control policy to realize intelligent data retrieval. Finally, the CiteSpace software is used to realize the visualization display in the field of internal control in universities and realize the construction of internal control knowledge graph and visualization research. The system can effectively utilize the relationship between knowledge objects of internal control and give full play to the value of information resources of internal control

The Indonesian digital library network is born to struggle with the digital divide

IndonesiaDLN –The Indonesian Digital Library Network– is a distributed collection of digital library networks, digital library servers, full local contents, metadata, and people for the development of the Indonesian knowledge-based society. Beside the general issues of digital library such as publishing, quality control, authentication, networking, and information retrieval, we also face other issue –namely digital divide– in designing and implementing the Network. This paper describes basic design of the Network that able to handle the typical problems in developing digital library network in Indonesia as a developing country, such as internet accessibility, bandwidth capacity, and network delays. We also will describe our experiences in implementing the Network that currently has 14 successfully connected partners and more than 15 partners are in progress of developing their digital library servers

Congenial Web Search : A Conceptual Framework for Personalized, Collaborative, and Social Peer-to-Peer Retrieval

Traditional information retrieval methods fail to address the fact that information consumption and production are social activities. Most Web search engines do not consider the social-cultural environment of users' information needs and the collaboration between users. This dissertation addresses a new search paradigm for Web information retrieval denoted as Congenial Web Search. It emphasizes personalization, collaboration, and socialization methods in order to improve effectiveness. The client-server architecture of Web search engines only allows the consumption of information. A peer-to-peer system architecture has been developed in this research to improve information seeking. Each user is involved in an interactive process to produce meta-information. Based on a personalization strategy on each peer, the user is supported to give explicit feedback for relevant documents. His information need is expressed by a query that is stored in a Peer Search Memory. On one hand, query-document associations are incorporated in a personalized ranking method for repeated information needs. The performance is shown in a known-item retrieval setting. On the other hand, explicit feedback of each user is useful to discover collaborative information needs. A new method for a controlled grouping of query terms, links, and users was developed to maintain Virtual Knowledge Communities. The quality of this grouping represents the effectiveness of grouped terms and links. Both strategies, personalization and collaboration, tackle the problem of a missing socialization among searchers. Finally, a concept for integrated information seeking was developed. This incorporates an integrated representation to improve effectiveness of information retrieval and information filtering. An integrated information retrieval process explores a virtual search network of Peer Search Memories in order to accomplish a reputation-based ranking. In addition, the community structure is considered by an integrated information filtering process. Both concepts have been evaluated and shown to have a better performance than traditional techniques. The methods presented in this dissertation offer the potential towards more transparency, and control of Web search

Erosion Expert System for Environmental Impact Assessment

Soil Erosion is one of the most important natural resources management problems in the world. It is a primary source of sediment that pollutes streams and fills reservoirs. Development especially in hilly terrain will significantly increase this process and cause adverse impact to the local and regional environment. The main purpose of the Erosion Expert System (EES) being developed was to help EIA consultants in preparing the Erosion part of EIA reports besides providing relevant information on erosion. Computer programs developed can help in information retrieval and decision support when dealing with erosion control. The rule-bases of the system was developed using CLIPS version 6.04, an Expert System Shell which was designed by NASA (National Aeronautics and Space Administration). The Graphic User Interface (GUI) for the system was designed by using Visual Basic 5. Results show that Knowledge-based system would be useful especially if the domain knowledge is systematic. The advantage of Knowledge-based system is that it leads to a greater degree of unification in preparing EIA reports but it should be used only as an alternative source of knowledge since human specialist would be a preferred approach in environmental planning

Inhibitory control during selective retrieval may hinder subsequent analogical thinking

Analogical reasoning is a complex cognitive activity that involves access and retrieval of pre-existing knowledge in order to find a suitable solution. Prior work has shown that analogical transfer and reasoning can be influenced by unconscious activation of relevant information. Based on this idea, we report two experiments that examine whether reduced access to relevant information in memory may further disrupt analogical reasoning unwittingly. In both experiments, we use an adaptation of the retrieval practice paradigm [1] to modulate memory accessibility of potential solutions to a subsequent set of analogy problems of the type ‘A is to B as C is to ?’. Experiment 1 showed a retrieval-induced impairment in analogical problem solving. Experiment 2 replicated this finding and demonstrated that it cannot be due to the deliberative episodic retrieval of the solutions to the analogies. These findings, predictable from an inhibitory framework of memory control, provide a new focus for theories of analogical transfer and highlight the importance of unconscious memory processes that may modulate problem solving.The study was supported by the Spanish Ministry of Education and Science and Ministry of Economy, Industry and Competitiveness grants FPU014/07066 to TMV, PSI2015-65502-C2-1-P to TB, PSI2015-65502-C2-2-P to CJGA and PCIN- 2015-165-C02-01 to TMV, TB and CJG

modality independent encoding of individual concepts in the left parietal cortex

Abstract The organization of semantic information in the brain has been mainly explored through category-based models, on the assumption that categories broadly reflect the organization of conceptual knowledge. However, the analysis of concepts as individual entities, rather than as items belonging to distinct superordinate categories, may represent a significant advancement in the comprehension of how conceptual knowledge is encoded in the human brain. Here, we studied the individual representation of thirty concrete nouns from six different categories, across different sensory modalities (i.e., auditory and visual) and groups (i.e., sighted and congenitally blind individuals) in a core hub of the semantic network, the left angular gyrus, and in its neighboring regions within the lateral parietal cortex. Four models based on either perceptual or semantic features at different levels of complexity (i.e., low- or high-level) were used to predict fMRI brain activity using representational similarity encoding analysis. When controlling for the superordinate component, high-level models based on semantic and shape information led to significant encoding accuracies in the intraparietal sulcus only. This region is involved in feature binding and combination of concepts across multiple sensory modalities, suggesting its role in high-level representation of conceptual knowledge. Moreover, when the information regarding superordinate categories is retained, a large extent of parietal cortex is engaged. This result indicates the need to control for the coarse-level categorial organization when performing studies on higher-level processes related to the retrieval of semantic information

The sound of music: from increased personalization to therapeutic values

Music providers like Spotify leverage music recommendation systems to connect users with relevant music. Based on content-based and collaborative-filtering statistical methods, these machine learning algorithms quantify user-song probabilities and present the highest-ranked songs. However, most music providers do not fully address their users’ music seeking and retrieval needs. Likewise, the fields of Recommender Systems (RecSys), Music Recommendation Systems (MRS) and Music Information Retrieval (MIR) remain disconnected from real-world use cases of music seeking. In this conceptual paper, we review the literature of the RecSys, MRS, MIR and Music Therapy (MT) academic fields. We discuss trends towards greater user control and personalization in the MRS and MIR fields and the connections between MT and positive health outcomes such as reductions in stress, anxiety and heart rate.Analysis. We argue that greater control and visibility into the characteristics of songs and recommended items can generate positive downstream benefits. We recommend features that empower users to better seek, find, store, retrieve and learn from their musical catalogs. We suggest design enhancements that recognize music’s wider psychological and physiological benefits and create opportunities to build domain knowledge. Unlocking music’s myriad benefits through the enhancements proposed would catalyze positive outcomes for business stakeholders, users and society.Peer Reviewe

Ontological Principles of Disease Management from Public Health Perspective: a Tuberculosis Case Study

Formal ontological representation of clinical conditions and disease management is a key methodology ensuring that the complex knowledge of disease treatment, control and prevention can be represented, stored and accessed in the most appropriate way to help the medical professionals in their decision making. This is of particular importance for the public health domain where the concern is about the affect of the disease on populations rather than individuals.The existing evidence-based knowledge can best be used by professionals if incorporated into care pathways (formal or informal) which relate the sequence of actions necessary for accurate management of diseases to the progression of the illness and treatment. Therefore, there is a need for an ontological framework to be built around care pathways in order to allow the professionals to access the most relevant information at the time of making a decision. In this paper we will illustrate a Tuberculosis (TB) care pathway, as developed at City University, and show how a formal ontological representation can, in principle, serve the needs of information retrieval around this particular disease

The distinctive pattern of declarative memories in autism spectrum disorder: Further evidence of episodic memory constraints

This study examines declarative memory retrieval in ASD depending on the availability and access to stored conceptual knowledge. Fifteen autistic participants and a matched control group of 18 typically-developed (TD) volunteers completed a Remember-Know paradigm manipulated by encoding-type (categorical, perceptual) and item-typicality (high-typical, low-typical). The autistic group showed worse and slower recognition and less recollection but equivalent familiarity-based memories compared to TDs. Notably, low-typical items did not improve their memories as they did for TDs, likely due to difficulties in matching low-fit information to the stored schema. Results suggest that memory decline in ASD may derive from the episodic system and its dynamics with the semantic system. These findings may inform interventional strategies for enhancing learning abilities in ASD.info:eu-repo/semantics/acceptedVersio