Retrieval Of Relevant Information Research Articles

Explicit Knowledge Graph Reasoning for Conversational Recommendation

Traditional recommender systems estimate user preference on items purely based on historical interaction records, thus failing to capture fine-grained yet dynamic user interests and letting users receive recommendation only passively. Recent conversational recommender systems (CRSs) tackle those limitations by enabling recommender systems to interact with the user to obtain her/his current preference through a sequence of clarifying questions. Recently, there has been a rise of using knowledge graphs (KGs) for CRSs, where the core motivation is to incorporate the abundant side information carried by a KG into both the recommendation and conversation processes. However, existing KG-based CRSs are subject to two defects: (1) there is a semantic gap between the learned representations of utterances and KG entities, hindering the retrieval of relevant KG information; (2) the reasoning over KG is mostly performed with the implicitly learned user interests, overlooking the explicit signals from the entities actually mentioned in the conversation. To address these drawbacks, we propose a new CRS framework, namely the K nowledge E nhanced C onversational R easoning (KECR) model. As a user can reflect her/his preferences via both attribute- and item-level expressions, KECR jointly embeds the structured knowledge from two levels in the KG. A mutual information maximization constraint is further proposed for semantic alignment between the embedding spaces of utterances and KG entities. Meanwhile, KECR utilizes the connectivity within the KG to conduct explicit reasoning of the user demand, making the model less dependent on the user’s feedback to clarifying questions. As such, the semantic alignment and explicit KG reasoning can jointly facilitate accurate recommendation and quality dialogue generation. By comparing with strong baselines on two real-world datasets, we demonstrate that KECR obtains state-of-the-art recommendation effectiveness, as well as competitive dialogue generation performance.

Retrieval dynamics in resolving relational inconsistency during verbal analogical reasoning.

Reasoning about verbal analogies requires selective retrieval of relevant relational information. A consequence of this may be that inhibitory processes in memory cause reduced recall of information associated with analogy-irrelevant relations. The current experiments apply the retrieval-induced forgetting framework to investigate the potential role of such inhibitory processes in analogical reasoning. Participants studied verbal analogies in A-B :: C-D form. Then, half of the A-B pairs from the study phase appeared in verbal analogy problems but with a new C term (A-B :: C-?), and half the items did not appear in verbal analogy problems. A final recall test was then administered for all the original analogies. When the new C term in the analogical reasoning phase conveyed a new relation that was inconsistent with the original relation, reduced recall for items appearing in analogies was observed (Experiment 1). However, when the new C term conveyed a relation that was consistent with the original relation, no forgetting effect was observed (Experiment 1). This forgetting effect occurred even when a hint of the original relation was provided at final recall (Experiment 2). These results indicate that reasoning about analogies may involve inhibitory processes that help reduce competition among competing relations. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

P and S wave detection for the Nevados de Chillan Volcanic Complex (Chile), based on spectro-temporal features

The localization of seismic events is a relevant step for volcano monitoring as it provides information of physical parameters that support the forecasting of volcanic activity. Automating this step is necessary to process the enormous amount of signals received by the observatories, improving relevant and opportune information retrieval at the early stages of monitoring. Localisation of seismic events is based on identifying the arrival time of the P and S waves in the raw signals, in a procedure called P and S picking. This is mainly performed manually or semi-automatically, usually applying the STA/LTA algorithm. The literature shows that the automation research on this process has been focused mainly on tectonic seismicity since the P and S waves are more difficult to detect in volcanic seismicity. This work aims to automate this process, proposing a novel and simple technique for volcano seismic events, using spectro-temporal energy features extracted from the spectrograms. The energy accumulated by an event over time, its second derivative, as well as a modification of the STA/LTA method were applied to detect the P and S waves for volcano tectonic (VT) events and only the P wave for long period (LP) events. The proposed method was adjusted and tested with the multi-station events recorded at the active Nevados de Chillan Volcanic Complex (Chile) and the results were compared with the reference manual picking provided by the observatory and with the traditional STA/LTA approach. The spectro-temporal approach presented lower errors and standard deviations than STA/LTA, in particular for the P detection for LP and the S detection, which are more difficult to detect. The median time error for the P wave detection was 0.0430 ± 0.21s for VT events and 0.0870 ± 0.35s for LP. For the S wave detection the error was 0.05 ± 0.49s. 80% of the P wave detection absolute errors were below 0.13[s] for VT and below 0.48[s] for LP. In turn, for the S wave detection, 80% of the errors were below 0.47[s]. In addition to the time errors, the distance errors were also evaluated, thus the localization was calculated for each picking method. The results presented a longitudinal error of 0.3° for both methods and a latitude error of 0.01° for the spectro-temporal method and 0.03° for STA/LTA method, once again demonstrating the good performance of the proposal. These results are promising despite the complexity of the P and S wave detection in volcanic events and the simplicity of the method compared to the methods proposed in the literature.

Transparency and the future of semantic searching in academic libraries

“Semantic search” is a fairly broad term without a fixed goal amongst developers. Most agree that semantic searching should reach beyond simple keyword or text/string matching in order to provide more robust taxonomies and relevant information retrieval systems. Many novice users and first-year students struggle to retrieve the scholarly sources they desire because they lack the specialized vocabulary attained through advanced years of study. Most students are used to the dominant semantic search discovery system, Google and Google Scholar, but few students understand how these systems work. Query parsing, fuzzy matching, and understanding how semantic searching utilizes taxonomies for more accurate tagging is not usually a consideration for a desperate sophomore looking for last-minute sources to finish a paper that is due the next day. So, there is a danger that meeting a student’s perceived need for a Google-like discovery system is more important than creating a transparent system. However, information literacy and library instruction must give careful consideration to these issues and be able to help emerging scholars/students understand both the ethical and practical horizons of semantic search tools.

You Only Demanufacture Once (YODO): WEEE retrieval using unsupervised learning

Recent developments in robotic demanufacturing raise the potential to increase the cost-efficiency of recycling and recovering resources from Waste of Electrical and Electronic Equipment (WEEE). However, the industrial adoption of robotic demanufacturing for mixed WEEE streams requires tailored instructions for every product model. Considering the large variation in product models, it is not expected to be feasible in the coming decade to rely only on computer vision technologies to define the tailored instructions required for robust and time-efficient robotic demanufacturing. Therefore, the presented research developed a generic retrieval system named You Only Demanufacture Once (YODO) based on content-based image retrieval (CBIR) to identify the product model and retrieve product model-specific demanufacturing instructions. The system compares the visual features represented on a color image of the WEEE with a database of known descriptions representing previously imaged WEEE to find a match or to figure out whether the analyzed product model is new to the system. The performance of YODO is evaluated with a case study for laptop model identification, where a large dataset is created including 4089 images of a representative laptop waste stream. The results demonstrate a top-1 retrieval mean average precision (mAP) of 93.75%. After running YODO on 3600 laptops, the system learned 1079 unique product models, and the presented results show an 85% chance that the next laptop presented to the system is already registered in the database, allowing the retrieval of relevant information for robotic demanufacturing. This corroborates that a fast learning rate can be achieved, allowing a YODO system to support the robotic demanufacturing by making prior product-specific learnings available.

Web Personalized Recommendation Model Using Temporal Fuzzy Association Rule Mining

Abstract: A web access log file contains timely sequenced log entries which include essential fields to indicate user activities. Analysis of these patterns provides valuable information for web designer to quickly respond to their individual needs. Many industries are struggling to retain regular interested customers for the improvement of customer relationship. Retrieval of relevant information automatically from these log files for interested group of users is a difficult process, since acquiring interested user profiles which evolves continuously with respect to time are not so easy. The paper presents a novel Web Personalized Recommendation Model (WPRM) using temporal fuzzy association rule mining technique. Temporal Fuzzy Association Rule Mining (FTARM) technique is proposed and applied on a focused set of interested users to provide intelligent recommendations. The proposed model results in less execution time and reduced memory utilization with high accuracy.

Multi-obstacle aware smart navigation system for visually impaired people in fog connected IoT-cloud environment.

Design of smart navigation for visually impaired/blind people is a hindering task. Existing researchers analyzed it in either indoor or outdoor environment and also it's failed to focus on optimum route selection, latency minimization and multi-obstacle presence. In order to overcome these challenges and to provide precise assistance to visually impaired people, this paper proposes smart navigation system for visually impaired people based on both image and sensor outputs of the smart wearable. The proposed approach involves the upcoming processes: (i) the input query of the visually impaired people (users) is improved by the query processor in order to achieve accurate assistance. (ii) The safest route from source to destination is provided by implementing Environment aware Bald Eagle Search Optimization algorithm in which multiple routes are identified and classified into three different classes from which the safest route is suggested to the users. (iii) The concept of fog computing is leveraged and the optimal fog node is selected in order to minimize the latency. The fog node selection is executed by using Nearest Grey Absolute Decision Making Algorithm based on multiple parameters. (iv) The retrieval of relevant information is performed by means of computing Euclidean distance between the reference and database information. (v) The multi-obstacle detection is carried out by YOLOv3 Tiny in which both the static and dynamic obstacles are classified into small, medium and large obstacles. (vi) The decision upon navigation is provided by implementing Adaptive Asynchronous Advantage Actor-Critic (A3C) algorithm based on fusion of both image and sensor outputs. (vii) Management of heterogeneous is carried out by predicting and pruning the fault data in the sensor output by minimum distance based extended kalman filter for better accuracy and clustering the similar information by implementing Spatial-Temporal Optics Clustering Algorithm to reduce complexity. The proposed model is implemented in NS 3.26 and the results proved that it outperforms other existing works in terms of obstacle detection and task completion time.

Learning to Rank Articles for Molecular Queries

The cost of developing new drugs is estimated at billions of dollars per year. Identification of new molecules for drugs involves scanning existing bio-medical literature for relevant information. As the potential drug molecule is novel, retrieval of relevant information using a simple direct search is less likely to be productive. Identifying relevant papers is therefore a more complex and challenging task, which requires searching for information on molecules with similar characteristics to the novel drug. In this paper, we present the novel task of ranking documents based on novel molecule queries. Given a chemical molecular structure, we wish to rank medical papers that will contribute to a researcher's understanding of the novel molecule drug potential. We present a set of ranking algorithms and molecular embeddings to address the task. An extensive evaluation of the algorithms is performed over the molecular embeddings, studying their performance on a benchmark retrieval corpus, which we share with the community. Additionally, we introduce a heterogeneous edge-labeled graph embedding approach to address the molecule ranking task. Our evaluation shows that the proposed embedding model can significantly improve molecule ranking methods. The system is currently deployed in a targeted drug delivery and personalized medicine research laboratory.

Multiple functions of the angular gyrus at high temporal resolution.

Here, the functions of the angular gyrus (AG) are evaluated in the light of current evidence from transcranial magnetic/electric stimulation (TMS/TES) and EEG/MEG studies. 65 TMS/TES and 52 EEG/MEG studies were examined in this review. TMS/TES literature points to a causal role in semantic processing, word and number processing, attention and visual search, self-guided movement, memory, and self-processing. EEG/MEG studies reported AG effects at latencies varying between 32 and 800ms in a wide range of domains, with a high probability to detect an effect at 300-350ms post-stimulus onset. A three-phase unifying model revolving around the process of sensemaking is then suggested: (1) early AG involvement in defining the current context, within the first 200ms, with a bias toward the right hemisphere; (2) attention re-orientation and retrieval of relevant information within 200-500ms; and (3) cross-modal integration at late latencies with a bias toward the left hemisphere. This sensemaking process can favour accuracy (e.g. for word and number processing) or plausibility (e.g. for comprehension and social cognition). Such functions of the AG depend on the status of other connected regions. The much-debated semantic role is also discussed as follows: (1) there is a strong TMS/TES evidence for a causal semantic role, (2) current EEG/MEG evidence is however weak, but (3) the existing arguments against a semantic role for the AG are not strong. Some outstanding questions for future research are proposed. This review recognizes that cracking the role(s) of the AG in cognition is possible only when its exact contributions within the default mode network are teased apart.

Crawling and Mining the Dark Web: A Survey on Existing and New Approaches

The last two decades have seen a marked increase in the illegal activities on the Dark Web. Prompt evolvement and use of sophisticated protocols make it difficult for security agencies to identify and investigate these activities by conventional methods. Moreover, tracing criminals and terrorists poses a great challenge keeping in mind that cybercrimes are no less serious than real life crimes. At the same time, computer security societies and law enforcement pay a great deal of attention on detecting and monitoring illegal sites on the Dark Web. Retrieval of relevant information is not an easy task because of vastness and ever-changing nature of the Dark Web; as a result, web crawlers play a vital role in achieving this task. Thereafter, data mining techniques are applied to extract useful patterns that would help security agencies to limit and get rid of cybercrimes. The aim of this paper is to present a survey for those researchers who are interested in this topic. We started by discussing the internet layers and the properties of the Deep Web, followed by explaining the technical characters of The Onion Routing (TOR) network, and finally describing the approaches of accessing, extracting and processing Dark Web data. Understanding the Dark Web, its properties and its threats is vital for internet servers; we do hope this paper be of help in that goal.

Semantic Conceptual Framework for Environmental Monitoring and Surveillance—A Case Study on Forest Fire Video Monitoring and Surveillance

This paper presents a semantic conceptual framework and definition of environmental monitoring and surveillance and demonstrates an ontology implementation of the framework. This framework is defined in a mathematical formulation and is built upon and focused on the notation of observation systems. This formulation is utilized in the analysis of the observation system. Three taxonomies are presented, namely, the taxonomy of (1) the sampling method, (2) the value format and (3) the functionality. The definition of concepts and their relationships in the conceptual framework clarifies the task of querying for information related to the state of the environment or conditions related to specific events. This framework aims to make the observation system more queryable and therefore more interactive for users or other systems. Using the proposed semantic conceptual framework, we derive definitions of the distinguished tasks of monitoring and surveillance. Monitoring is focused on the continuous assessment of an environment state and surveillance is focused on the collection of all data relevant for specific events. The proposed mathematical formulation is implemented in the format of the computer readable ontology. The presented ontology provides a general framework for the semantic retrieval of relevant environmental information. For the implementation of the proposed framework, we present a description of the Intelligent Forest Fire Video Monitoring and Surveillance system in Croatia. We present the implementation of the tasks of monitoring and surveillance in the application domain of forest fire management.

Status of socio-demographic and behavioral profile of younger and older HIV high risk groups in Chhattisgarh

ABSTRACTBackground:The prevention of new infections in high-risk groups is a major thrust in National AIDS Control Program. There is enough evidence that many epidemiological and behavioral determinants which make High Risk Group vulnerable to HIV transmission. The most effective means of controlling the spread of HIV is through the implementation of Targeted Interventions (TIs) efforts by which services are catered to them. Furthermore, stigma and marginalization are often linked to this population, which may lead to difficulties while accessing social and health services (SHS) due to behavioral, cultural and language barriers or lack of knowledge of the system. So, finding the sociodemographic & behavioral profile can give a breakthrough in improving the quality of life of HIV high risk groups.Material and Methods:A cross sectional study was conducted during September to December 2019 in two districts (i.e., Raipur & Durg) among HRGs of Chhattisgarh. Training cum sensitization of survey team e.g., peer educators, outreach workers, counselors, project managers prior to the survey was done for data collection.Results:A Total of 3963 HRGs were registered with TI NGOs, 3418 (86.2%) were screened. The mean age of study participants was 27.69 ± 6.1. Compliance of participation was 86.2%. HRGs were observed to have malnutrition (BMI <18.5 &> 25.0). 7 cases of Pulmonary TB were found among IDUs. Prevalence of diabetes and Hypertension was 1.2% and 1.1% respectively. Substance abuse (i.e., for Alcohol and Tobacco) was significantly higher among IDUs and FSWs.Conclusion:This study reinforces the fact that for accessing High Risk Groups and retrieval of relevant information can best be obtained by their care givers i.e., TI NGOs personnel. Also understanding the sociodemographic and behavioral profiles are central to designing targeted HIV prevention interventions for them.

Optimal machine-driven acquisition of future cosmological data

We present a set of maps classifying regions of the sky according to their information gain potential as quantified by Fisher information. These maps can guide the optimal retrieval of relevant physical information with targeted cosmological searches. Specifically, we calculated the response of observed cosmic structures to perturbative changes in the cosmological model and we charted their respective contributions to Fisher information. Our physical forward-modeling machinery transcends the limitations of contemporary analyses based on statistical summaries to yield detailed characterizations of individual 3D structures. We demonstrate this advantage using galaxy counts data and we showcase the potential of our approach by studying the information gain of the Coma cluster. We find that regions in the vicinity of the filaments and cluster core, where mass accretion ensues from gravitational infall, are the most informative with regard to our physical model of structure formation in the Universe. Hence, collecting data in those regions would be most optimal for testing our model predictions. The results presented in this work are the first of their kind to elucidate the inhomogeneous distribution of cosmological information in the Universe. This study paves a new way forward for the performance of efficient targeted searches for the fundamental physics of the Universe, where search strategies are progressively refined with new cosmological data sets within an active learning framework.

Different approaches to collaborative problem solving between successful versus less successful problem solvers: Tracking changes of knowledge structure

STEM Problem solving necessitates a substantial amount of specialized domain knowledge. An important element of problem-solving within the domain includes how knowledge is structured and organized in memory to facilitate efficient retrieval of relevant information and future problem solving. In previous studies, however, problem solving and knowledge structure have been studied in relative isolation, resulting in viewing them as a separate two-way process. To address this gap, this study aimed to track how individuals developed their knowledge structures before, during, and after collaborative problem solving in the contexts of STEM (physics, astronomy, and biology), with particular attention to understanding the different mechanism between success versus less successful problem-solvers. For that, we employed a relatively new and promising network approach to representing learners’ knowledge structures as network graphs for analysis and comparison. Results visually demonstrated that successful problem-solvers tend to share a solution-focused knowledge and establish their group knowledge-oriented knowledge structure, whereas the less successful problem-solvers tend to share problem-focused knowledge and then establish their prior knowledge-oriented knowledge structure. Implications and discussion for the findings are provided.

DES-Tcell is a knowledgebase for exploring immunology-related literature

T-cells are a subtype of white blood cells circulating throughout the body, searching for infected and abnormal cells. They have multifaceted functions that include scanning for and directly killing cells infected with intracellular pathogens, eradicating abnormal cells, orchestrating immune response by activating and helping other immune cells, memorizing encountered pathogens, and providing long-lasting protection upon recurrent infections. However, T-cells are also involved in immune responses that result in organ transplant rejection, autoimmune diseases, and some allergic diseases. To support T-cell research, we developed the DES-Tcell knowledgebase (KB). This KB incorporates text- and data-mined information that can expedite retrieval and exploration of T-cell relevant information from the large volume of published T-cell-related research. This KB enables exploration of data through concepts from 15 topic-specific dictionaries, including immunology-related genes, mutations, pathogens, and pathways. We developed three case studies using DES-Tcell, one of which validates effective retrieval of known associations by DES-Tcell. The second and third case studies focuses on concepts that are common to Grave’s disease (GD) and Hashimoto’s thyroiditis (HT). Several reports have shown that up to 20% of GD patients treated with antithyroid medication develop HT, thus suggesting a possible conversion or shift from GD to HT disease. DES-Tcell found miR-4442 links to both GD and HT, and that miR-4442 possibly targets the autoimmune disease risk factor CD6, which provides potential new knowledge derived through the use of DES-Tcell. According to our understanding, DES-Tcell is the first KB dedicated to exploring T-cell-relevant information via literature-mining, data-mining, and topic-specific dictionaries.

Question Answering Survey: Directions, Challenges, Datasets, Evaluation Matrices

The usage and amount of information available on the internet increase over the past decade. This digitization leads to the need for automated answering system to extract fruitful information from redundant and transitional knowledge sources. Such systems are designed to cater the most prominent answer from this giant knowledge source to the user query using natural language understanding (NLU) and thus eminently depends on the Question-answering(QA) field. Question answering involves but not limited to the steps like mapping of user question to pertinent query, retrieval of relevant information, finding the best suitable answer from the retrieved information etc. The current improvement of deep learning models evince compelling performance improvement in all these tasks. In this review work, the research directions of QA field are analyzed based on the type of question, answer type, source of evidence-answer, and modeling approach. This detailing followed by open challenges of the field like automatic question generation, similarity detection and, low resource availability for a language. In the end, a survey of available datasets and evaluation measures is presented.

BioMaster: An Integrated Database and Analytic Platform to Provide Comprehensive Information About BioBrick Parts.

Synthetic biology seeks to create new biological parts, devices, and systems, and to reconfigure existing natural biological systems for custom-designed purposes. The standardized BioBrick parts are the foundation of synthetic biology. The incomplete and flawed metadata of BioBrick parts, however, are a major obstacle for designing genetic circuit easily, quickly, and accurately. Here, a database termed BioMaster http://www.biomaster-uestc.cn was developed to extensively complement information about BioBrick parts, which includes 47,934 items of BioBrick parts from the international Genetically Engineered Machine (iGEM) Registry with more comprehensive information integrated from 10 databases, providing corresponding information about functions, activities, interactions, and related literature. Moreover, BioMaster is also a user-friendly platform for retrieval and analyses of relevant information on BioBrick parts.

BioLitMine: Advanced Mining of Biomedical and Biological Literature About Human Genes and Genes from Major Model Organisms

The accumulation of biological and biomedical literature outpaces the ability of most researchers and clinicians to stay abreast of their own immediate fields, let alone a broader range of topics. Although available search tools support identification of relevant literature, finding relevant and key publications is not always straightforward. For example, important publications might be missed in searches with an official gene name due to gene synonyms. Moreover, ambiguity of gene names can result in retrieval of a large number of irrelevant publications. To address these issues and help researchers and physicians quickly identify relevant publications, we developed BioLitMine, an advanced literature mining tool that takes advantage of the medical subject heading (MeSH) index and gene-to-publication annotations already available for PubMed literature. Using BioLitMine, a user can identify what MeSH terms are represented in the set of publications associated with a given gene of the interest, or start with a term and identify relevant publications. Users can also use the tool to find co-cited genes and a build a literature co-citation network. In addition, BioLitMine can help users build a gene list relevant to a MeSH term, such as a list of genes relevant to “stem cells” or “breast neoplasms.” Users can also start with a gene or pathway of interest and identify authors associated with that gene or pathway, a feature that makes it easier to identify experts who might serve as collaborators or reviewers. Altogether, BioLitMine extends the value of PubMed-indexed literature and its existing expert curation by providing a robust and gene-centric approach to retrieval of relevant information.

An Integrated Search Framework for Leveraging the Knowledge-Based Web Ecosystem

The explosion of information constrains the judgement of search terms associated with Knowledge-Based Web Ecosystem (KBWE), making the retrieval of relevant information and its knowledge management challenging. The existing information retrieval (IR) tools and their fusion in a framework need attention, in which search results can effectively be managed. In this article, we demonstrate the effective use of information retrieval services by a variety of users and agents in various KBWE scenarios. An innovative Integrated Search Framework (ISF) is proposed, which utilises crawling strategies, web search technologies and traditional database search methods. Besides, ISF offers comprehensive, dynamic, personalized, and organization-oriented information retrieval services, ranging from the Internet, extranet, intranet, to personal desktop. In this empirical research, experiments are carried out demonstrating the improvements in the search process, as discerned in the conceptual ISF. The experimental results show improved precision compared with other popular search engines.

Teaching AND reaching our students from a distance

Teaching AND reaching our students from a distance