Retrieval Functions Research Articles

Double-frequency grating shearing interferometer with built-in phase-shifting function for robust multi-level phase retrieval.

In this paper, we propose and demonstrate a novel interferometer and signal process to retrieve two-dimensional signals with multilevel phases. The interferometer is based on a shearing interferometry with double-frequency grating, and phase-shifting interferometry is derived as a built-in function of the lateral displacement of the grating. The interferometer not only retrieves the multilevel phase signals but also eliminates slow-varying phase errors wherever they occur. Owing to the common path algorithm, the new interferometer is more robust in dynamic circumstances for optical testing and data processing. We propose a pre-integral signal process for two-dimensional (2D) data processing to prevent post-phase-integral due to shearing interferometry. The simulation and experiment showed that the proposed interferometer with a pre-integral process has various advantages in signal processing for multilevel phase retrieval, and will be useful for higher data rates in optical data storage and free-space communication.

Fast, Searchable, Symmetric Encryption Scheme Supporting Ranked Search

Searchable encryption (SE) is one of the effective techniques for searching encrypted data without decrypting it. This technique can provide a secure indexing mechanism for encrypted data and utilize a secure trapdoor to search for the encrypted data directly, thus realizing a secure ciphertext retrieval function. Existing schemes usually build a secure index directly on the whole dataset and implement the retrieval of encrypted data by implementing a secure search algorithm on the index. However, this approach requires testing many non-relevant documents, which diminishes the query efficiency. In this paper, we adopt a clustering method to preclassify the dataset, which can filter out quite a portion of irrelevant documents, thus improving the query. Concretely, we first partition the dataset into multiple document clusters using the k-means clustering algorithm; then, we design index building and searching algorithms for these document clusters; finally, by using the asymmetric scalar-product-preserving encryption (ASPE) scheme to encrypt the indexes and queries, we propose a fast searchable symmetric encryption scheme that supports ranked search. Detailed security analysis demonstrates that the proposed scheme can guarantee the data and query security of the search process. In addition, theoretical and experimental analysis indicates that our scheme outperforms other similar schemes in terms of query efficiency.

Corpus Design of Chinese Medicine English Vocabulary Translation Teaching System Based on Python

The current corpus has the problem of imperfect span retrieval function, which leads to a large classification noise. This paper designs a Python-based corpus of Chinese medicine English vocabulary translation teaching system. Here, we select the script material of web crawler, extract topic tags in the form of tag window, calculate the amount of information carried by words, use Python to extract the characteristics of Chinese medicine English vocabulary, and according to the observation value of exploration strategy, use instant time difference learning algorithm to construct the translation mode of teaching system, limit the scope of key words, and design the cross-range retrieval function of corpus. Experimental results: the average classification noise of the designed corpus and the other two corpora is 25.007[Formula: see text]dB, 33.877[Formula: see text]dB and 32.166[Formula: see text]dB, which proves that the integrated Python corpus has higher comprehensive value.

Arthroscopic retrieval of knee foreign bodies in pediatric: a single-centre experience.

The present study was to investigate the clinical features of foreign body (FB) residues in children's knee joints, surgical retrieval, and postoperative function of knee joints. This retrospective study included a total of 13 children (8 boys and 5 girls; the mean age is 6.69years old, range from 2 to 16years old) who underwent retrieval surgery of knee FBs in our hospital. Related clinical factors were recorded and analyzed to find the influence factors of surgical methods and FBs' location change. The FBs in 11 cases were removed completely by arthroscopy, two cases by open surgery changed from arthroscopy. Besides glass pieces (n = 4, 31%) and wooden splinter (n = 1, 7%), sewing needle fragments were the most common type of knee foreign body (n = 8, 62%). There were two patients with FBs whom open surgery changed from arthroscopy was performed. The FBs of patients with open surgery changed from arthroscopy were more likely to locate in the posterior compartment (p = 0.04), and had a higher interval between injury and surgery than that in patients with arthroscopy (p = 0.01). The location of FBs (all were needle fragments) had changed intra-operatively in three patients, whose mean weight was lower than patients with fixed FBs (p = 0.04). The FB (small glass piece) of one patient was flushed out of the joint during arthroscopy. Arthroscopic retrieval surgery was an effective procedure to remove the FBs of the children's knee joint. The earlier it is detected, the easier it is to be treated. The location change of FBs should be cautioned in arthroscopic surgery and they are more likely to migrate into the posterior compartment of the knee joint.

Construction and Optimization of Distributed Electronic Archives System Merging Control Messages

This paper analyzes the whole of the electronic archives management system, describes the basic features and functions of the system, and gives the flow of each functional module of the system. Techniques for implementing important functions in the system are discussed and compared. Using the object-oriented idea, this paper designs the electronic archives management system and gives the hybrid structure model of the system. According to the needs of the electronic office, an official document transmission module is established to realize the electronic transmission of official documents, and the specific implementation process is given. According to the characteristics of archives information management and utilization, SQL Server is used as the background database and the database design is carried out. This paper introduces the full-text retrieval technology that realizes the query and retrieval function of electronic archives and gives how to configure the full-text retrieval service in SQL Server. This paper proposes a K-means-based merging control message clustering algorithm to solve the problem of communication topology changes caused by large-scale, high-mobility merging control messages. Based on the idea of K-means clustering, the calculation method of distance in K-means is improved by combining the distribution of the initial electronic archives message, and the fitness function of the electronic archives is designed by using the information of the electronic archives in the cluster and the average distance to the neighboring electronic archives. According to the maximum fitness value, the cluster head is selected to establish clustering. The file management system is tested and analyzed in a simulated environment. Based on the functional positioning of the system and the business environment, the functional performance and performance of the analysis system are examined and analyzed, and the research and development results are inspected according to the system development goals to check whether the development results of the system have reached the advanced stage. At the functional level, it can automate the management of the core business of the distributed electronic archives management department and support the distributed storage management of massive archive data, which effectively improves the informatization level and development efficiency of archives management.

DAG blockchain-based lightweight authentication and authorization scheme for IoT devices

As the extension and expansion of the Internet, the Internet of things (IoT) connects things with the network through information sensing equipment to achieve intelligent identification, positioning, monitoring, management and other functions. With the increasing number of IoT users and devices, the demands for secure communication and data confidentiality are getting higher and higher, and the identity authentication and access control of IoT devices has become a huge challenge. In the IoT environment, the traditional device identity and access management methods based on public key infrastructure (PKI) and blockchain bring the problems of single point failure and scalability. The emergence of decentralized storage system and distributed ledger technology directed acyclic graph (DAG) provide new ideas to solve these problems. In the paper, we use distributed ledger technology IOTA with low energy demand to design a lightweight and scalable mechanism to manage the identity of IoT devices and access control of large-scale IoT data, so as to ensure source reliability and sharing security of IoT data. In the new scheme, IOTA technology is used to realize the functions of registration, update, revocation and retrieval of the identity of IoT devices, which solves the defects caused by centralized management. Inter planetary file system (IPFS) is used to store a large amount of data generated by devices in the IoT, which not only reduces the burden on devices and systems of IoT, but also solves the single point of failure, delay and other problems caused by traditional cloud storage. At the same time, the fog node is used to provide localized computing for a group of IoT devices to solve the problem of limited computing resources of IoT devices. In addition, in order to prevent data abuse caused by unauthorized devices accessing network resources, a trusted access control mechanism is designed to achieve fine-grained access control, access policy update and other functions, so as to better maintain the IoT environment and network security. Finally, the performance of the proposed scheme is evaluated through simulation experiments, and the security and functions of the scheme are analyzed, which proves that the scheme meets the security demands of the IoT environment.

In-IDE Code Generation from Natural Language: Promise and Challenges

A great part of software development involves conceptualizing or communicating the underlying procedures and logic that needs to be expressed in programs. One major difficulty of programming is turning concept into code , especially when dealing with the APIs of unfamiliar libraries. Recently, there has been a proliferation of machine learning methods for code generation and retrieval from natural language queries , but these have primarily been evaluated purely based on retrieval accuracy or overlap of generated code with developer-written code, and the actual effect of these methods on the developer workflow is surprisingly unattested. In this article, we perform the first comprehensive investigation of the promise and challenges of using such technology inside the PyCharm IDE, asking, “At the current state of technology does it improve developer productivity or accuracy, how does it affect the developer experience, and what are the remaining gaps and challenges?” To facilitate the study, we first develop a plugin for the PyCharm IDE that implements a hybrid of code generation and code retrieval functionality, and we orchestrate virtual environments to enable collection of many user events (e.g., web browsing, keystrokes, fine-grained code edits). We ask developers with various backgrounds to complete 7 varieties of 14 Python programming tasks ranging from basic file manipulation to machine learning or data visualization, with or without the help of the plugin. While qualitative surveys of developer experience are largely positive, quantitative results with regards to increased productivity, code quality, or program correctness are inconclusive. Further analysis identifies several pain points that could improve the effectiveness of future machine learning-based code generation/retrieval developer assistants and demonstrates when developers prefer code generation over code retrieval and vice versa. We release all data and software to pave the road for future empirical studies on this topic, as well as development of better code generation models.

A new design of multimedia big data retrieval enabled by deep feature learning and Adaptive Semantic Similarity Function.

Nowadays, multimedia big data have grown exponentially in diverse applications like social networks, transportation, health, and e-commerce, etc. Accessing preferred data in large-scale datasets needs efficient and sophisticated retrieval approaches. Multimedia big data consists of the most significant features with different types of data. Even though the multimedia supports various data formats with corresponding storage frameworks, similar semantic information is expressed by the multimedia. The overlap of semantic features is most efficient for theory and research related to semantic memory. Correspondingly, in recent years, deep multimodal hashing gets more attention owing to the efficient performance of huge-scale multimedia retrieval applications. On the other hand, the deep multimodal hashing has limited efforts for exploring the complex multilevel semantic structure. The main intention of this proposal is to develop enhanced deep multimedia big data retrieval with the Adaptive Semantic Similarity Function (A-SSF). The proposed model of this research covers several phases “(a) Data collection, (b) deep feature extraction, (c) semantic feature selection and (d) adaptive similarity function for retrieval. The two main processes of multimedia big data retrieval are training and testing. Once after collecting the dataset involved with video, text, images, and audio, the training phase starts. Here, the deep semantic feature extraction is performed by the Convolutional Neural Network (CNN), which is again subjected to the semantic feature selection process by the new hybrid algorithm termed Spider Monkey-Deer Hunting Optimization Algorithm (SM-DHOA). The final optimal semantic features are stored in the feature library. During testing, selected semantic features are added to the map-reduce framework in the Hadoop environment for handling the big data, thus ensuring the proper big data distribution. Here, the main contribution termed A-SSF is introduced to compute the correlation between the multimedia semantics of the testing data and training data, thus retrieving the data with minimum similarity. Extensive experiments on benchmark multimodal datasets demonstrate that the proposed method can outperform the state-of-the-art performance for all types of data.

Design of Fuzzy Aided Cognition System for International Economic and Trade Information under Supply Chain Management.

The existing fuzzy assistant cognition system has the problem of imperfect retrieval function, which leads to long execution time. This paper designs a fuzzy assistant cognition system of international economic and trade information under supply chain management. Hardware Part. Optimize wireless sensor and connect power interface and related accessories. In the software part, the dynamic characteristics of international economic and trade information elements are extracted, the flexible operation model of supply chain management is constructed, the demand-oriented organizational structure is established, the output set is determined by the product reasoning, and the retrieval function of fuzzy assistant cognitive system is designed by using the fuzzy association algorithm. Experimental Results. The average execution time of the fuzzy assistant cognitive system and the other two systems is 123.593 s, 165.733 s, and 169.506 s, which proves that the cognitive system integrated with supply chain management has a higher practical application value.

Efficient transformation capabilities of single database private block retrieval

Private information retrieval (PIR) is one of the promising techniques to preserve user privacy in the presence of trusted-but-curious servers. The information theoretically private query construction assures the highest user privacy over curious and unbounded computation servers. Therefore, the need for information-theoretic private retrieval was fulfilled by various schemes in a variety of PIR settings. But, there is a lack of efficient encryption switching scheme which supports efficient switching between information-theoretic to/from computationally bounded PIRs. We propose a combination of new bit connection methods called rail-shape and signal-shape and new quadratic residuosity assumption based family of trapdoor functions for generic single database private block retrieval (PBR). The main goal of this work is to show that the possibility of mapping from computationally bounded privacy to information theoretic privacy or vice-versa in a single database setting using newly constructed bit connection and trapdoor function combinations. Notably, the proposed schemes are single round, memoryless and plain database schemes (at their basic constructions).

Expanding the Galaxy's reference data.

SummaryProperly and effectively managing reference datasets is an important task for many bioinformatics analyses. Refgenie is a reference asset management system that allows users to easily organize, retrieve and share such datasets. Here, we describe the integration of refgenie into the Galaxy platform. Server administrators are able to configure Galaxy to make use of reference datasets made available on a refgenie instance. In addition, a Galaxy Data Manager tool has been developed to provide a graphical interface to refgenie’s remote reference retrieval functionality. A large collection of reference datasets has also been made available using the CVMFS (CernVM File System) repository from GalaxyProject.org, with mirrors across the USA, Canada, Europe and Australia, enabling easy use outside of Galaxy.Availability and implementationThe ability of Galaxy to use refgenie assets was added to the core Galaxy framework in version 22.01, which is available from https://github.com/galaxyproject/galaxy under the Academic Free License version 3.0. The refgenie Data Manager tool can be installed via the Galaxy ToolShed, with source code managed at https://github.com/BlankenbergLab/galaxy-tools-blankenberg/tree/main/data_managers/data_manager_refgenie_pull and released using an MIT license. Access to existing data is also available through CVMFS, with instructions at https://galaxyproject.org/admin/reference-data-repo/. No new data were generated or analyzed in support of this research.

New Ku-Band Geophysical Model Function and Wind Speed Retrieval Algorithm Developed for Tiangong-2 Interferometric Imaging Radar Altimeter

A Ku-band geophysical model function for wind speed retrieval named as KuLMOD-H is proposed based on the quasi-specular reflection model for the Chinese Tiangong-2 Interferometric Imaging Radar Altimeter (TG2-InIRA), which can be used to retrieve the wind speed with 2 km resolution. The model is derived by expanding the effective nadir reflection coefficient term and the mean square slope term in the quasi-specular reflection model using quadratic polynomials with wind speed as variable. The model coefficients are obtained by fitting the radar backscattering coefficient data from TG2-InIRA to the collocated sea surface wind speed data from European Center for Medium-Range Weather Forecasts (ECMWF). For solving the problem of potential ambiguous solutions when the incidence angles are relatively large, a regularization approach is further proposed. The retrieved wind speed results have a root-mean-square error (RMSE) of 1.42 m/s compared with the collocated ECMWF wind speed data and at the same time, they are highly consistent with the buoy data. Different from the previous works on TG2-InIRA wind speed retrieval, this article derives a semiphysical model suitable for incidence angles from 1 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$^\circ$</tex-math></inline-formula> to 8 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$^\circ$</tex-math></inline-formula> , by which better retrieval results are achieved. This work not only explores the wind speed retrieval capability of the instrument under low incidence-angle observation, but also provides high-resolution wind speed data for further correction of sea state bias for TG2-InIRA sea surface height measurements.

Detecting impaired language processing in patients with mild cognitive impairment using around-the-ear cEEgrid electrodes.

Mild cognitive impairment (MCI) is the term used to identify those individuals with subjective and objective cognitive decline but with preserved activities of daily living and an absence of dementia. Although MCI can impact functioning in different cognitive domains, most notably episodic memory, relatively little is known about the comprehension of language in MCI. In this study, we used around-the-ear electrodes (cEEGrids) to identify impairments during language comprehension in patients with MCI. In a group of 23 patients with MCI and 23 age-matched controls, language comprehension was tested in a two-word phrase paradigm. We examined the oscillatory changes following word onset as a function of lexico-semantic single-word retrieval (e.g., swrfeq vs. swift) and multiword binding processes (e.g., horse preceded by swift vs. preceded by swrfeq). Electrophysiological signatures (as measured by the cEEGrids) were significantly different between patients with MCI and controls. In controls, lexical retrieval was associated with a rebound in the alpha/beta range, and binding was associated with a post-word alpha/beta suppression. In contrast, both the single-word retrieval and multiword binding signatures were absent in the MCI group. The signatures observed using cEEGrids in controls were comparable with those signatures obtained with a full-cap EEG setup. Importantly, our findings suggest that patients with MCI have impaired electrophysiological signatures for comprehending single words and multiword phrases. Moreover, cEEGrid setups provide a noninvasive and sensitive clinical tool for detecting early impairments in language comprehension in MCI.

Narrative imagery: Emotional modulation in the default mode network

The default mode network (DMN) is activated when constructing and imagining narrative events, with functional brain activity in the medial-prefrontal cortex hypothesized to be modulated during emotional processing by adding value (or pleasure) to the episodic representation. However, since enhanced reactivity during emotional, compared to neutral, content is a more frequent finding in both the brain and body in physiological, neural, and behavioral measures, the current study directly assesses the effects of pleasure and emotion during narrative imagery in the DMN by using a within-subject design to first identify the DMN during resting state and then assess activation during pleasant, neutral, or unpleasant imagery. Replicating previous findings, enhanced functional activity in the medial prefrontal cortex was found when imagining pleasant, compared to unpleasant, events. On the other hand, emotion-related activation was found when imagining either pleasant or unpleasant, compared to neutral, events in other nodes of the DMN including the posterior cingulate cortex (PCC), angular gyrus, anterior hippocampus, lateral temporal cortex, temporal pole, dorsomedial prefrontal cortex (dmPFC), and ventrolateral prefrontal cortex (vlPFC). Pervasive emotional modulation in the DMN is consistent with the view that a primary function of event retrieval and construction is to remember, recreate, and imagine motivationally relevant events important for planning adaptive behavior.

Model functions for retrieving sea surface wind speed with wide-swath imaging altimeter

Using satellite remote sensing technology to observe sea surface wind fields has become an important instrument. The successful launch of Tiangong-2, the future Surface Water and Ocean Topography (SWOT) mission, and demonstrating GaoFen Project provides the future understanding for the generation of new wide-swath imaging altimeter. The constructed data sets and validated data sets of the model function were obtained by combining the observable conditions of imaging altimeter with the theoretical model of sea surface backscattering coefficient. A model function for wind speed retrieval at incidence angles from 1 deg to 7 deg and from 7 deg to 11 deg was proposed, which is suitable for wide-swath imaging altimeter, model coefficients were calculated using the constructed data sets, model functions were validated using the validated data sets. The validated results of model function show that the root-mean-squared error (RMSE) ranged from 0.005 to 0.72 m / s. Wind speed was retrieved with China France Oceanography Satellite (CFOSAT) Surface Waves Investigation and Monitoring instrument (SWIM) L2 data for incidence angles from 3 deg to 10 deg with 0.5 interval, and wind speed was validated with European Centre for Medium-Range Weather Forecasts (ECMWF) data. The results of wind speed validation show that the average of RMSE is 1.6 m / s. Therefore, the wind speed retrieval was validated with the wind speed from ECMWF, which showed good coincidence.

Differential Associations of White Matter Brain Age With Language-Related Mechanisms in Word-Finding Ability Across the Adult Lifespan.

Research on cognitive aging has established that word-finding ability declines progressively in late adulthood, whereas semantic mechanism in the language system is relatively stable. The aim of the present study was to investigate the associations of word-finding ability and language-related components with brain aging status, which was quantified by using the brain age paradigm. A total of 616 healthy participants aged 18–88 years from the Cambridge Centre for Ageing and Neuroscience databank were recruited. The picture-naming task was used to test the participants’ language-related word retrieval ability through word-finding and word-generation processes. The naming response time (RT) and accuracy were measured under a baseline condition and two priming conditions, namely phonological and semantic priming. To estimate brain age, we established a brain age prediction model based on white matter (WM) features and estimated the modality-specific predicted age difference (PAD). Mass partial correlation analyses were performed to test the associations of WM-PAD with the cognitive performance measures under the baseline and two priming conditions. We observed that the domain-specific language WM-PAD and domain-general WM-PAD were significantly correlated with general word-finding ability. The phonological mechanism, not the semantic mechanism, in word-finding ability was significantly correlated with the domain-specific WM-PAD. In contrast, all behavioral measures of the conditions in the picture priming task were significantly associated with chronological age. The results suggest that chronological aging and WM aging have differential effects on language-related word retrieval functions, and support that cognitive alterations in word-finding functions involve not only the domain-specific processing within the frontotemporal language network but also the domain-general processing of executive functions in the fronto-parieto-occipital (or multi-demand) network. The findings further indicate that the phonological aspect of word retrieval ability declines as cerebral WM ages, whereas the semantic aspect is relatively resilient or unrelated to WM aging.

Research on Application Value Analysis of Real Estate Registration Based on Big Data Mining

With the rapid development and improvement of big data, data mining, cloud computing and other technologies, real estate registration has introduced many emerging technologies. As an advanced computer multimedia information processing technology, image recognition can automatically recognize pictures in the real estate registration system using features learning, pattern recognition, and machine learning, extract text, patterns and other information on the pictures, and save these information content In the database, it realizes the functions of retrieval, query and data pre-examination of real estate registration, and improves the intelligence, information and sharing of the real estate registration system, which has important functions and significance.

An atlas-guided automatic planning approach for rectal cancer intensity-modulated radiotherapy

We try to develop an atlas-guided automatic planning (AGAP) approach and evaluate its feasibility and performance in rectal cancer intensity-modulated radiotherapy. The developed AGAP approach consisted of four independent modules: patient atlas, similar patient retrieval, beam morphing (BM), and plan fine-tuning (PFT) modules. The atlas was setup using anatomy and plan data from Pinnacle auto-planning (P-auto) plans. Given a new patient, the retrieval function searched the top similar patient by a generic Fourier descriptor algorithm and retrieved its plan information. The BM function generated an initial plan for the new patient by morphing the beam aperture from the top similar patient plan. The beam aperture and calculated dose of the initial plan were used to guide the new plan optimization in the PFT function. The AGAP approach was tested on 96 patients by the leave-one-out validation and plan quality was compared with the P-auto plans. The AGAP and P-auto plans had no statistical difference for target coverage and dose homogeneity in terms of V 100% (p = 0.76) and homogeneity index (p = 0.073), respectively. The CI index showed they had a statistically significant difference. But the ΔCI was both 0.02 compared to the perfect CI index of 1. The AGAP approach reduced the bladder mean dose by 152.1 cGy (p < 0.05) and V 50 by 0.9% (p < 0.05), and slightly increased the left and right femoral head mean dose by 70.1 cGy (p < 0.05) and 69.7 cGy (p < 0.05), respectively. This work developed an efficient and automatic approach that could fully automate the IMRT planning process in rectal cancer radiotherapy. It reduced the plan quality dependence on the planner experience and maintained the comparable plan quality with P-auto plans.

A Learning to rank framework based on cross-lingual loss function for cross-lingual information retrieval

A Learning to rank framework based on cross-lingual loss function for cross-lingual information retrieval

Ordinal synchronization mark sequence and its steganography for a multi-link network covert channel.

A multi-link network covert channel (MLCC) such as Cloak exhibits a high capacity and robustness and can achieve lossless modulation of the protocol data units. However, the mechanism of Cloak involving an arrangement of packets over the links (APL) is limited by its passive synchronization schemes, which results in intermittent obstructions in transmitting APL packets and anomalous link switching patterns. In this work, we propose a novel ordinal synchronization mark sequence (OSMS) for a Cloak framework based MLCC to ensure that the marked APL packets are orderly distinguishable. Specifically, a unidirectional function is used to generate the OSMS randomly before realizing covert modulation. Subsequently, we formulate the generation relation of the marks according to their order and embed each mark into the APL packets by using a one-way hash function such that the mark cannot be cracked during the transmission of the APL packet. Finally, we set up a retrieval function of the finite set at the covert receiver to extract the marks and determine their orders, and the APL packets are reorganized to realize covert demodulation. The results of experiments performed on real traffic indicated that the MLCC embedded with OSMS could avoid the passive synchronization schemes and exhibited superior performance in terms of reliability, throughput, and undetectability compared with the renowned Cloak method, especially under a malicious network interference scenario. Furthermore, our approach could effectively resist the inter-link correlation test, which are highly effective in testing the Cloak framework.