Document Retrieval Research Articles

A Universal Standard Archive File for Adsorption Data.

New advanced adsorbents are a crucial driver for the development of energy and environmental applications. Tremendous potential is provided by machine learning and data mining techniques, as these approaches can identify the most appropriate adsorbent for a particular application. However, the current scientific reporting of adsorption isotherms in graphs and figures is not adequate to reproduce original experimentally measured data. This report proposes the specification of a new standard adsorption information file (AIF) inspired by the ubiquitous crystallographic information file (CIF) and based on the self-defining text archive and retrieval (STAR) procedure, also used to represent biological nuclear magnetic resonance experiments (NMR-STAR). The AIF is a flexible and easily extended free-format archive file that is readily human and machine readable and is simple to edit using a basic text editor or parse for database curation. This format represents the first steps toward an open adsorption data format as a basis for a decentralized adsorption data library. An open format facilitates the electronic transmission of adsorption data between laboratories, journals, and larger databases, which is key in the effort to increase open science in the field of porous materials in the future.

Neural Concomitants of Remote Memory in a Comedian with Exceptional Verbal Memory

Most studies exploring how remote memory is represented in the brain are based on strong episodic self-related components. Because of methodological reasons, much less is known about how the information concerning the semantic part of autobiographical memory is retrieved, and whether the brain correlates differ according to the autobiographical moment of the memory formation. In the present study, we explored the neural concomitants of the retrieval of texts learnt at different periods of life, in a comedian with exceptional verbal memory skills. This 49-year-old comedian was instructed to recite aloud a total of 30 texts he learnt during three different epochs: before the age of 15 years (E1), between the age of 15 and 25 years (E2), and after the age of 25 years (E3). The most salient activation was observed for memory from the farthest period, with a preponderance of the medial rostral prefrontal cortex (PFC) and of the precuneus. There was no hippocampal activation during text retrieval by comparison to a control condition, whatever the learning period. This study supports the assumption that the recall of remote semantic memories can occur without hippocampal activation. We discussed the activation of the rostral PFC during retrieval of the oldest (and best consolidated) memories as the possible involvement of control meta-memory processes rather than memory processes per se.

Query expansion based on term distribution and DBpedia features

Query Expansion (QE) approaches that involve the reformulation of queries by adding new terms to the initial user query, are intended to ameliorate the vocabulary mismatch between the query keywords and the documents’ in Information Retrieval Systems (IRS). One big issue in QE is the selection of the right candidate terms for expansion. For this purpose Linked Data can be used, as a valuable resource, for providing additional expansion features such as the values of sub- and super classes of resources. The underlying research question is whether interlinked data and vocabulary items provide features which can be taken into account for query expansion. In this paper, we introduced a new QE approach that aimed at improving IRS by using the well-known distribution based method Bose-Einstein statistics (Bo1) as well as Linked Data from the knowledge base DBpedia using different numbers of expansion terms. We evaluated the effectiveness of each method individually as well as their combinations using two Text REtrieval Conference (TREC) test collections. Our approach has lead to significant improvement in terms of precision, recall, Mean Average Precision (MAP) at rank 10, and normalized Discounted Cumulative Gain (nDCG) at different ranks compared to Pseudo Relevance Feedback (PRF) that we used as a baseline. The results show that the inclusion of semantic annotations clearly improves the retrieval performance over the baseline method.

An improved BM25 algorithm for clinical decision support in Precision Medicine based on co-word analysis and Cuckoo Search

BackgroundRetrieving gene and disease information from a vast collection of biomedical abstracts to provide doctors with clinical decision support is one of the important research directions of Precision Medicine.MethodWe propose a novel article retrieval method based on expanded word and co-word analyses, also conducting Cuckoo Search to optimize parameters of the retrieval function. The main goal is to retrieve the abstracts of biomedical articles that refer to treatments. The methods mentioned in this manuscript adopt the BM25 algorithm to calculate the score of abstracts. We, however, propose an improved version of BM25 that computes the scores of expanded words and co-word leading to a composite retrieval function, which is then optimized using the Cuckoo Search. The proposed method aims to find both disease and gene information in the abstract of the same biomedical article. This is to achieve higher relevance and hence score of articles. Besides, we investigate the influence of different parameters on the retrieval algorithm and summarize how they meet various retrieval needs.ResultsThe data used in this manuscript is sourced from medical articles presented in Text Retrieval Conference (TREC): Clinical Decision Support (CDS) Tracks of 2017, 2018, and 2019 in Precision Medicine. A total of 120 topics are tested. Three indicators are employed for the comparison of utilized methods, which are selected among the ones based only on the BM25 algorithm and its improved version to conduct comparable experiments. The results showed that the proposed algorithm achieves better results.ConclusionThe proposed method, an improved version of the BM25 algorithm, utilizes both co-word implementation and Cuckoo Search, which has been verified achieving better results on a large number of experimental sets. Besides, a relatively simple query expansion method is implemented in this manuscript. Future research will focus on ontology and semantic networks to expand the query vocabulary.

Multimedia Retrieval and Analysis with Cottontail DB

Analysis and retrieval of media collections get more and more challenging the larger the collections become. Keeping everything in the main memory becomes less feasible, and more and more time and effort have to be spent to deal with the data management. However, traditional relational databases do not support primitives often used in multimedia workloads, such as the nearest-neighbour search on vectors. In this column, we introduce Cottontail DB, an open-source database management system for multimedia features. Cottontail DB supports traditional relational database operations and text retrieval based on Lucene and, most importantly, efficient vector-space retrieval operations for large datasets. Cottontail DB is the new data storage system powering the vitrivr multimedia retrieval stack, which was also previously featured in the SIGMM Records [7]. Just like the other components of vitrivr, Cottontail DB is released under the permissive MIT license. It is written in Kotlin, runs on all major operating systems, and comes with a flexible and easy-to-use gRPC API, which makes it usable in many applications, independent of the programming languages used. Cottontail DB's clean and modular architecture enables the easy extension of its functionalities and also makes it useful in an educational context. In the following, we will give a brief introduction on how Cottontail DB works, what we are using it for, and, most importantly, how it can help you manage your data. To learn more about Cottontail DB, including performance evaluations, we kindly refer readers to our Open Source Software Track Contribution at ACM MM 2020 [1], where Cottontail DB was honored with that year's Best Open Source Award.

A New Training Idea of Machine Learning in NLP

Natural language processing is an important direction in the field of computer science and artificial intelligence. It studies various theories and methods that enable effective communication between humans and computers in natural language. Generally speaking, NLP can be divided into the following areas, including text retrieval, sentiment analysis, semantic role labeling, machine translation and dialogue systems. Nowadays, although artificial intelligence has achieved a certain degree of achievements in the dialogue system, there is still room for improvement. This article proposes a way to deal with multiple interactive dialogues between man and machine, so that machine dialogue can take more about the content of the conversation before and after into account and the performance of machine is closer to the real human dialogue.

A Retrieval Method for Chinese EMR Based on Semantic Knowledge Map

Due to the diversity of natural language in Chinese electronic medical records, it is usually hard for traditional retrieval methods to provide ideal results. On this condition, this paper proposes a retrieval method for Chinese EMR based on semantic knowledge map. Through natural language processing and semantic analysis, we can build connections for medical knowledge, and organize all the entities into a visual knowledge map. After that, a novel retrieval method based on semantic knowledge map is proposed, which focuses on node connection of documents and terms. Through semantic extension and intention spread, the improved retrieval results are returned, and the results are reordered by correlation. Compared with general methods, this method can significantly improve the accuracy of Chinese EMR text retrieval and optimize the ranking strategy of retrieval results.

Frequency of pertinent MRI abnormalities of the sacroiliac joints of patients without spondyloarthropathies: a systematic review of the literature.

MRI criteria are central to the diagnosis of non-radiographic axial spondyloarthropathy (nr-axSpA). The cardinal feature of nr-axSpa is inflammatory low back pain, which may be difficult to distinguish from highly prevalent non-specific low back pain. This study aims to determine the frequency of relevant MRI findings in the sacroiliac joints (SIJ) of patients without Spondyloarthropathy (SpA), and therefore estimate the specificity of MRI scans for SpA. EMBASE and Medline were searched and limited to English. Titles were screened for relevance, with studies that included primary MRI findings in patients without SpA triggering retrieval. Retrieved papers were reviewed, data extracted by two authors and quality criteria (QUADAS 2) were applied. Findings were considered for asymptomatic and symptomatic individuals. The search recovered 2172 articles. Abstracts of 117 were reviewed for full text retrieval, 11 papers met eligibility criteria. These papers described MRI findings of 1180 asymptomatic patients and 1318 with low back symptoms but without SpA. In relevant populations, bone marrow oedema was found in 22% (95% CI 19-25) of asymptomatic and 20% (95% CI 18-22) of asymptomatic individuals. In all non-Spa patients, sclerosis was found in 13.4% and erosions in 6.5%. There is a significant frequency of diagnostically pertinent MRI abnormalities in the SIJ of patients without SpA. These are present in both asymptomatic and symptomatic individuals. Findings, such as oedema and sclerosis, lack specificity and should be interpreted with caution. Erosions are less frequent and are likely more specific for SpA.

Information retrieval interfaces in virtual reality-A scoping review focused on current generation technology.

The Information Retrieval user experience has remained largely unchanged since its inception for computers and mobile devices alike. However, recent developments in Virtual Reality hardware (pioneered by Oculus Rift in 2013) could introduce a new environment for Information Retrieval. This paper reports the results of a Scoping Literature Review (PRISMA-ScR) by rigorously examining the entire body of relevant literature with reproducible methods. The following research questions are answered: "What prototypes and concepts of Virtual Reality Information Retrieval systems with current generation hardware exist?", "How are user interaction and especially user input realised in these systems?", "What Retrieval features are used in these systems?", "How are search results displayed in these systems?" and "Can these VR IR systems compare to traditional (non-VR) IR systems?". After querying Google Scholar, Scopus and Web of Science, 1042 documents were reviewed in depth. Key features and attributes of the systems are summarised and discussed. Sketches of the user interfaces are included as well. The 30 documents that were relevant to the research questions include 16 distinct systems or theories. They discuss and utilise several user input technologies, ranging from controllers, voice input or hand tracking. Although conventional retrieval features are less common, systems enable retrieval of literature, 3D objects, images, books and texts and arrange them in a virtual space (e.g. as grids, arcs or maps). Finally, many of these systems were compared to conventional counterparts through user evaluation (n = 10). Most found user task times to be shorter or equal (n = 5, n = 3). In the seven papers that measured user performance (rate of correct solutions), three reported better performance (one equal). Notably, users always were more satisfied with the Virtual Reality systems compared to conventional ones. Possible limitations of these evaluations are demographic selection and the quality of baseline systems (control).

A quantum-like semantic model for text retrieval in Arabic

The subject of study. The paper focuses on the extraction of semantics from texts in Arabic. In particular, the applicability of the Bell test to word pairs is investigated as a measure of the semantic words relatedness in a context. The study applies the quantum formalism to the task of information retrieval in Arabic texts and presents the results of this work. The authors also examine the influence of the context width on the effectiveness of information retrieval. Method. The research is based on the vector representation of the context. It uses the well-known approach based on the HAL (Hyperspace Analogue to Language) matrix and Bell test. The HAL matrix allows taking into account both the frequency of the words occurrence in the context and the distance to the target word. Quantum theory operates with probability density matrices. Quantum theory allows describing probabilities in the vector space in a more natural way i.e., words can be represented as vectors. Main results. The results demonstrate that using the Bell’s test for texts in Arabic provides a better ranking of search results compared to the results of search services. Practical significance. The research results can be used in the development of the information retrieval systems, as well as for the further development of methods based on the distributive hypothesis.

An Automatic Synthesizer of Advising Tools for High Performance Computing

This article presents Egeria, the first automatic synthesizer of advising tools for High-Performance Computing (HPC). When one provides it with some HPC programming guides as inputs, Egeria automatically constructs a text retrieval tool that can advise on what to do to improve the performance of a given program. The advising tool provides a concise list of essential rules automatically extracted from the documents and can retrieve relevant optimization knowledge for optimization questions. Egeria is built based on a distinctive multi-layered design that leverages natural language processing (NLP) techniques and extends them with HPC-specific knowledge and considerations. This article presents the design, implementation, and both quantitative and qualitative evaluation results of Egeria.

A Strategic Approach in Handling Information Retrieval Course for Attaining Course Outcomes – A Case Study

Background: The major challenge in engineering education is to educate and train the student’s community with pedagogical practices in order to determine the notable skills to solve more complex and fragile structured problems. Innovative pedagogical practices make the students to adhere to the complex formulations of the corresponding domain and its applications. The realm of good understanding of knowledge and its deliverables can be ascertained through pedagogical practices with a focus on learner-centric activities in the classroom teaching. The actual target in teaching learning process is to make all the students to have a good exploration on the domain knowledge with success ratio. The learner success is considered to be the core metric with which we can judge the success of learner-centric activities with creativity and quality processes. Confirming the process in which the learners are engaged with the key ideas of the course to be taught makes the students to have practical implementations in Teaching-Learning process. Objective: This research work focus on the assessment and evaluation of learner-centric techniques for outcome based education upon statistical evaluation. Students of third year (VI Semester) of two consecutive batches have been analyzed for the course on Information Retrieval (14ITPS0). Two set of batches 2015-19 and 2016-20 have been considered for the assessment and analysis of Active Learning Strategies (ALS). The incorporation has been processed using the strategic approach based on daily, weekly and monthly assessments focusing on student learning criteria and their responding behaviors for 2016-20 batch. Each paradigm has been measured corresponding to the course outcomes at each level. Significant statistical analysis has been made for validating the process behind the teaching learning process. Real time case study: Considering the batch 2015-19 summary assignments, presentations has been used for the evaluation and assessment of interim assignments. The observed response from the students went well among the student’s community. All the students have been registered for the course and they too have completed the same. But, the time and success ratio in learning mechanism varied from student to student upon completion of the course. Considering the batch 2016-20 active learning strategies such as Quiz by Kahoot!, Flipped classroom activity and MOOCs course has been incorporated. Also, for MOOC’S online courses a set of two courses has been identified relevant to the subject of study such as Text Retrieval and Search engines & Text mining and Analytics. For the Research problem identification the students are allowed to choose the domain specific topics for the course on information retrieval. In this process the time and success ratio has also been observed it also varied from one student to another.

Current Technologies for Information Retrieval of Documents in Digital Libraries: A Survey

Son yıllarda, farklı konular için sunulan dijital bilgi kaynaklarının sayısı aşırı miktarda artmaktadır. Bu dijital bilgi kaynaklarına erişim desteği sunan sistemlerin birçoğu tarama, arama ve bilgi geri kazanımı araçlarına odaklanmıştır. Sayısal kütüphaneler, elektronik kitaplıklar ve Web sayfaları, bilgi erişimini iyileştirmek, belge koleksiyonlarını farklı anahtar kriterlere göre hiyerarşik olarak oluşturmak ve düzenlemek için yeni birçok açılım sunmaktadır. Farklı arama araçları, bilgi erişim teknikleri kullanılarak erişilebilen belgeleri düzenlemek, endekslemek ve özetlemek için yazılım tabanlı hizmetleri kullanarak daha kapsamlı bir doküman kapsamı sunulabilmektedir. Dijital kütüphanelerdeki arama mekanizmalarına uygulanan teknolojiler, doküman koleksiyonlarını yönetmek, anlamlı veri çıkarmak ve doküman ilişkilerinin belirlenmesi için farklı yöntem ve teknolojilerin kullanımını zorunlu kılmıştır. Özellikle belgeler arasındaki ilişki ne biçimleri ne de türleri ile açıkça tanımlanamamaktadır. Bu çalışma, sayısal kütüphaneler için belgelerin içeriğinden üst-veri çıkarımı, varlık isimlerinin elde edilmesi, anahtar kelimelerin elde erilmesi ve doküman benzerliklerinin oluşturulması için kullanılan yöntem ve teknikler için kapsamlı bir çalışma sunmaktadır.

Cross-modal multi-relationship aware reasoning for image-text matching

Cross-modal image-text matching has attracted considerable interest in both computer vision and natural language processing communities. The main issue of image-text matching is to learn the compact cross-modal representations and the correlation between image and text representations. However, the image-text matching task has two major challenges. First, the current image representation methods focus on the semantic information and disregard the spatial position relations between image regions. Second, most existing methods pay little attention to improving textual representation which plays a significant role in image-text matching. To address these issues, we designed a decipherable cross-modal multi-relationship aware reasoning network (CMRN) for image-text matching. In particular, a new method is proposed to extract multi-relationship and to learn the correlations between image regions, including two kinds of visual relations: the geometric position relation and semantic interaction. In addition, images are processed as graphs, and a novel spatial relation encoder is introduced to perform reasoning on the graphs by employing a graph convolutional network (GCN) with attention mechanism. Thereafter, a contextual text encoder based on Bidirectional Encoder Representations from Transformers is adopted to learn distinctive textual representations. To verify the effectiveness of the proposed model, extensive experiments were conducted on two public datasets, namely MSCOCO and Flickr30K. The experimental results show that CMRN achieved superior performance when compared with state-of-the-art methods. On Flickr30K, the proposed method outperforms state-of-the-art methods more than 7.4% in text retrieval from image query, and 5.0% relatively in image retrieval with text query (based on Recall@1). On MSCOCO, the performance reaches 73.9% for text retrieval and 60.4% for image retrieval (based on Recall@1).

The Study of Multiple Classes Boosting Classification Method Based on Local Similarity

Boosting of the ensemble learning model has made great progress, but most of the methods are Boosting the single mode. For this reason, based on the simple multiclass enhancement framework that uses local similarity as a weak learner, it is extended to multimodal multiclass enhancement Boosting. First, based on the local similarity as a weak learner, the loss function is used to find the basic loss, and the logarithmic data points are binarized. Then, we find the optimal local similarity and find the corresponding loss. Compared with the basic loss, the smaller one is the best so far. Second, the local similarity of the two points is calculated, and then the loss is calculated by the local similarity of the two points. Finally, the text and image are retrieved from each other, and the correct rate of text and image retrieval is obtained, respectively. The experimental results show that the multimodal multi-class enhancement framework with local similarity as the weak learner is evaluated on the standard data set and compared with other most advanced methods, showing the experience proficiency of this method.

Voice Keyword Retrieval Method Using Attention Mechanism and Multimodal Information Fusion

A cross-modal speech-text retrieval method using interactive learning convolution automatic encoder (CAE) is proposed. First, an interactive learning autoencoder structure is proposed, including two inputs of speech and text, as well as processing links such as encoding, hidden layer interaction, and decoding, to complete the modeling of cross-modal speech-text retrieval. Then, the original audio signal is preprocessed and the Mel frequency cepstrum coefficient (MFCC) feature is extracted. In addition, the word bag model is used to extract the text features, and then the attention mechanism is used to combine the text and speech features. Through interactive learning CAE, the shared features of speech and text modes are obtained and then sent to modal classifier to identify modal information, so as to realize cross-modal voice text retrieval. Finally, experiments show that the performance of the proposed algorithm is better than that of the contrast algorithm in terms of recall rate, accuracy rate, and false recognition rate.

Unsupervised approaches for measuring textual similarity between legal court case reports

In the domain of legal information retrieval, an important challenge is to compute similarity between two legal documents. Precedents (statements from prior cases) play an important role in The Common Law system, where lawyers need to frequently refer to relevant prior cases. Measuring document similarity is one of the most crucial aspects of any document retrieval system which decides the speed, scalability and accuracy of the system. Text-based and network-based methods for computing similarity among case reports have already been proposed in prior works but not without a few pitfalls. Since legal citation networks are generally highly disconnected, network based metrics are not suited for them. Till date, only a few text-based and predominant embedding based methods have been employed, for instance, TF-IDF based approaches, Word2Vec (Mikolov et al. 2013) and Doc2Vec (Le and Mikolov 2014) based approaches. We investigate the performance of 56 different methodologies for computing textual similarity across court case statements when applied on a dataset of Indian Supreme Court Cases. Among the 56 different methods, thirty are adaptations of existing methods and twenty-six are our proposed methods. The methods studied include models such as BERT (Devlin et al. 2018) and Law2Vec (Ilias 2019). It is observed that the more traditional methods (such as the TF-IDF and LDA) that rely on a bag-of-words representation performs better than the more advanced context-aware methods (like BERT and Law2Vec) for computing document-level similarity. Finally we nominate, via empirical validation, five of our best performing methods as appropriate for measuring similarity between case reports. Among these five, two are adaptations of existing methods and the other three are our proposed methods.

Ultrasound-Guided Transversus Abdominis Plane Block in laparoscopic surgeries: A scoping review

ABSTRACT Background Pain following laparoscopic surgeries is a major concern that has been managed by several modalities with varying results. Ultrasound-Guided Transversus Abdominis Plane Block (TAPB) is considered a modality that may help decrease postoperative pain and opioid consumption and its related side effects which may contribute to reduction of hospital stay and improvement in functional recovery. Objective The aim of this scoping review was to demonstrate the efficacy of ultrasound guided TAPB for pain relief following laparoscopic surgeries and its effect on minimizing opioid consumption and incidence of side effects Method This scoping review was reported following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) extension for scoping review guidelines. A search of PubMed, Scopus, and Web of Science databases was conducted. The titles and abstracts of potentially relevant studies were screened before the retrieval of the full texts, which were then examined to determine which studies met the eligibility criteria (adult patients, receiving TAPB and undergoing laparoscopic surgeries, English language, full text articles related to TAPB and laparoscopic surgeries) Results One hundred and seventeen potentially eligible studies were identified. 98 records were excluded as they were either duplicates or did not meet the eligibility criteria leaving 19 articles. 13 articles were found discussing the efficacy of TAPB on pain intensity, most of which concluded that TAPB provided superior pain relief when compared with other methods. Most of the reviewed articles found no significant difference in the incidence of postoperative nausea and vomiting (PONV) or the quality of functional recovery among patients who received TAPB. Conclusion This scoping review suggests that TAPB is an effective technique for analgesia following laparoscopic surgeries. Further studies are required to better elucidate the effects of TAPB on the incidence of side effects and on patients’ functional recovery due to the limited number of reports discussing these outcomes.

Методы визуального графоаналитического представления и поиска научно-технических текстов

Методы визуального графоаналитического представления и поиска научно-технических текстов

Improved key of RSA Algorithm Generated from The image based on SHA256 Algorithm

At the present time, information security has become very necessary and is considered a very difficult problem in the event that it is exposed by the attackers and becomes easy and available to them. Therefore, data protection and non-disclosure to unauthorized persons are required to do so. The solution is to encrypt it and there are a large number of encryption algorithms in use. In this paper, both the RSA generic encryption algorithm and the hash algorithm known as SHA256 were used, in which the RSA algorithm was improved by generating the algorithm keys from an image generated with the SHA256 hashing algorithm. Where two keys are formed, one of which is public for encryption and is available and the other is a private key that is not disclosed only to the person who has access to the message, and effective encryption of the data was produced in addition to the retrieval of the original text. The proposed method has been programmed in VisualBaic.Net 2012 language.