Query Functions Research Articles

UNETR++: Delving Into Efficient and Accurate 3D Medical Image Segmentation.

Owing to the success of transformer models, recent works study their applicability in 3D medical segmentation tasks. Within the transformer models, the self-attention mechanism is one of the main building blocks that strives to capture long-range dependencies, compared to the local convolutional-based design. However, the self-attention operation has quadratic complexity which proves to be a computational bottleneck, especially in volumetric medical imaging, where the inputs are 3D with numerous slices. In this paper, we propose a 3D medical image segmentation approach, named UNETR++, that offers both high-quality segmentation masks as well as efficiency in terms of parameters, compute cost, and inference speed. The core of our design is the introduction of a novel efficient paired attention (EPA) block that efficiently learns spatial and channel-wise discriminative features using a pair of inter-dependent branches based on spatial and channel attention. Our spatial attention formulation is efficient and has linear complexity with respect to the input. To enable communication between spatial and channel-focused branches, we share the weights of query and key mapping functions that provide a complimentary benefit (paired attention), while also reducing the complexity. Our extensive evaluations on five benchmarks, Synapse, BTCV, ACDC, BraTS, and Decathlon-Lung, reveal the effectiveness of our contributions in terms of both efficiency and accuracy. On Synapse, our UNETR++ sets a new state-of-the-art with a Dice Score of 87.2%, while significantly reducing parameters and FLOPs by over 71%, compared to the best method in the literature. Our code and models are available at: https://tinyurl.com/2p87x5xn.

On the Connection Between the ABS Perturbation Methodology and Differential Privacy

This paper explores analytical connections between the perturbation methodology of the Australian Bureau of Statistics (ABS) and the differential privacy (DP) framework. We consider a single static counting query function and find the analytical form of the perturbation distribution with symmetric support for the ABS perturbation methodology. We then analytically measure the DP parameters, namely the (ε, δ) pair, for the ABS perturbation methodology under this setting. The results and insights obtained about the behaviour of (ε, δ) with respect to the perturbation support and variance are used to judiciously select the variance of the perturbation distribution give a good δ in the DP framework for a given desired ε and perturbation support. Finally, we propose a simple sampling scheme to implement the perturbation probability matrix in the ABS Cellkey method. The post sampling (ε, δ) pair is numerically analysed as a function of the Cellkey size. It is shown that the best results are obtained for a larger Cellkey size, because the (ε, δ) pair ost-sampling measures remain almost identical when we compare sampling and theoretical results.

Advanced Query Functionalities in Natural Logic Knowledge Bases

Natural logics are formal logics characterized by expressions that bear a resemblance to simplified natural language sentences. The inherent readability of natural logics renders them suitable also for large knowledge bases. Within the realm of natural logics, the rules of logical proof are applicable directly to the sentences constructed using this framework. This direct application ensures explainability of query inferences. In the paper we describe a specific natural logic named NaturaLog, suited for ontology-structured knowledge bases. We outline how NaturaLog can be effectively implemented into a database system to facilitate deductive querying. The primary focus of this paper lies in exploring various query functionalities and elucidating methods to achieve these capabilities, also when dealing with large-scale knowledge bases.

Text mining and portal development for gene-specific publications on Alzheimer’s disease and other neurodegenerative diseases

BackgroundTremendous research efforts have been made in the Alzheimer’s disease (AD) field to understand the disease etiology, progression and discover treatments for AD. Many mechanistic hypotheses, therapeutic targets and treatment strategies have been proposed in the last few decades. Reviewing previous work and staying current on this ever-growing body of AD publications is an essential yet difficult task for AD researchers.MethodsIn this study, we designed and implemented a natural language processing (NLP) pipeline to extract gene-specific neurodegenerative disease (ND) -focused information from the PubMed database. The collected publication information was filtered and cleaned to construct AD-related gene-specific publication profiles. Six categories of AD-related information are extracted from the processed publication data: publication trend by year, dementia type occurrence, brain region occurrence, mouse model information, keywords occurrence, and co-occurring genes. A user-friendly web portal is then developed using Django framework to provide gene query functions and data visualizations for the generalized and summarized publication information.ResultsBy implementing the NLP pipeline, we extracted gene-specific ND-related publication information from the abstracts of the publications in the PubMed database. The results are summarized and visualized through an interactive web query portal. Multiple visualization windows display the ND publication trends, mouse models used, dementia types, involved brain regions, keywords to major AD-related biological processes, and co-occurring genes. Direct links to PubMed sites are provided for all recorded publications on the query result page of the web portal.ConclusionThe resulting portal is a valuable tool and data source for quick querying and displaying AD publications tailored to users’ interested research areas and gene targets, which is especially convenient for users without informatic mining skills. Our study will not only keep AD field researchers updated with the progress of AD research, assist them in conducting preliminary examinations efficiently, but also offers additional support for hypothesis generation and validation which will contribute significantly to the communication, dissemination, and progress of AD research.

Research and Design of an Automated Security Event Analysis and Handling Framework Based on Threat Intelligence

In order to deeply explore and utilize the value of threat intelligence, strengthen research on attack organizations, and grasp the correlation between attack organizations, the author proposes the research and design of an automated security event analysis and handling framework based on threat intelligence. The author extracts the behavioral characteristics of the attack organization based on known APT attacks, and uses the machine learning framework Light GBM to establish a multi classification model to complete the analysis of unknown APT attack organizations. Through the study of multi-dimensional analysis of multi-source threat intelligence, attack organization correlation and judgment, an attack organization correlation and judgment system has been designed and implemented. The system includes six modules: threat intelligence collection module, threat intelligence multi-dimensional analysis module, attack organization fingerprint library module, attack organization correlation module, attack organization analysis module, and user module, providing attack organization correlation and judgment services for network security. The test results show that the intelligence reading and search query function can achieve the reading of various information of attack organizations, and achieve visual display of threat intelligence. The intelligence management function can achieve operations such as adding, deleting, and updating intelligence. The user management function of the system can achieve the management of administrator users and ordinary users. After testing, all functions of the system have been implemented and meet expectations.

An Efficient NoSQL-Based Storage Schema for Large-Scale Time Series Data

In IoT (internet of things), most data from the connected devices change with time and have sampling intervals, which are called time-series data. It is challenging to design a time series storage model that can write massive time-series data in a short time and can query and analyze the persistent time-series data for a long time. This paper constructs the RHTSDB (Redis-HBase Time Series Database) storage model based on Redis and HBase. RHTSDB uses the memory database Redis (Remote Dictionary Server) to cache massive time-series data, providing efficient data storage and query functions. HBase is used in RHTSDB for long-term storage of time-series data to realize their persistence. The paper designs a cold and hot separation mechanism for time-series data, where the infrequently accessed cold data are stored in HBase, and the frequently accessed and latest data are stored in Redis. Experiments verify that RHTSDB has apparent advantages over Apache IoTDB and HBase in data intake and query efficiency.

Insights from Overviewing Selective International Guidelines for Pediatric Asthma.

Asthma is a chronic atopic and inflammatory bronchial disease characterized by recurring symptoms and, episodic reversible bronchial obstruction and easily triggered bronchospasms. Asthma often begins in childhood. International guidelines are widely accepted and implemented; however, there are similarities and differences in the management approaches. There is no national guideline in many cities in Asia. This review aims to provide a practical perspective on current recommendations in the management of childhood asthma, specifically in the following aspects: diagnosis, classification of severity, treatment options, and asthma control, and to provide physicians with up-to-date information for the management of asthma. We used the PubMed function of Clinical Queries and searched keywords of "Asthma", "Pediatric," AND "Guidelines" as the search engine. "Clinical Prediction Guides", "Etiology", "Diagnosis", "Therapy," "Prognosis," and "Narrow" scope were used as filters. The search was conducted in November 2022. The information retrieved from this search was used in compiling the present article. Diagnosis is clinically based on symptom pattern, response to therapy with bronchodilators and inhaled corticosteroids, and spirometric pulmonary function testing (PFT). Asthma is classified in accordance with symptom frequency, peak expiratory flow rate (PEFR), forced expiratory volume in one second (FEV1), atopic versus nonatopic etiology, where atopy means a predisposition toward a type 1 hypersensitivity reaction. Asthma is also classified as intermittent or persistent (mild to severe). Unfortunately, there is no disease cure for asthma. However, symptoms can be prevented by trigger avoidance and suppressed with inhaled corticosteroids. Antileukotriene agents or long-acting beta-agonists (LABA) may be used together with inhaled corticosteroids if symptoms of asthma are not controlled. Rapidly worsening symptoms are usually treated with an inhaled short-acting beta-2 agonist (SABA, e.g., salbutamol) and oral corticosteroids. Intravenous corticosteroids and hospitalization are required in severe cases of asthma attacks. Some guidelines also provide recommendations on the use of biologics and immunotherapy. Asthma is diagnosed clinically, with supporting laboratory testing. Treatment is based on severity classification, from intermittent to persistent. Inhaled bronchodilator and steroid anti-inflammatory form the main stay of management.

Optimisation of the Query Function of Fenwick Tree With a Proportionately Less Opportunity Cost on the Sum Function Using Varying Orders of Fibonacci Bases

Optimisation of the Query Function of Fenwick Tree With a Proportionately Less Opportunity Cost on the Sum Function Using Varying Orders of Fibonacci Bases

Оптимизационный алгоритм управления водными ресурсами водохранилищ для обоснования диспетчерских графиков

The rules for the use of reservoir water resources need to be constantly updated, considering a significant number of factors affecting the effectiveness of management. Anthropogenic changes in morphometric functions, partial siltation of dead volume, and changes in basic hydrological characteristics have a significant impact on reservoir parameters and flow regulation regime. Justification of the regulatory regime remains an urgent and traditionally difficult engineering task of the IRP, discussed at professional meetings and in scientific publications. The article discusses a methodological approach based on the introduction of a given control algorithm into the process of assigning an interval request to the reservoir. In this case, the request is formulated as a two-parameter power function of the predicted resource for the estimated time interval (month). The degree indicator and the relative length of the range of uninterrupted water requirements coverage are the arguments of the query function. Nomograms illustrating the influence of the degree indicator on the outline of the dependence of requirements on the reservoir are constructed. In our opinion, this approach significantly reduces the Pareto set, excluding a significant part of the set of formal optima. The algorithm of the proposed model is focused on two-criteria optimization, where the relative values of the total annual water output and the sum of the relative interval ordinates of water consumption are considered as criteria. The unified functional is formed by linear convolution of criteria. The model was tested on the example of the resources of the Zhizdra River for a series of two low-water years. In the process of balance modeling and optimization, Excel tools are used, including “solution search” and VBA procedures. It is noted that it is necessary to use more powerful optimization algorithms for calculations based on multiyear hydrological series.

Global Combination and Clustering Based Differential Privacy Mixed Data Publishing

With the rapid advancement of information technology, a large amount of high-value data have been generated. To exploit the potential value of big data and at the same time to protect individuals' sensitive information, a global combination and clustering based differential privacy (DP) mixed data publishing method is proposed in this paper. The main idea of the proposed method is to improve the truthfulness of the published data as well as to enhance the utility by shifting the sensitivity of query function from a single record to a group of records using <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$k$</tex-math></inline-formula> -median clustering algorithm. Specifically, to improve the accuracy and utility of categorical attributes, a global combination method is proposed to take the correlation among categorical attributes into account. The proposed combination method takes all categorical attributes as a unit and then applies the exponential mechanism to improve the data utility. Then we combine it with the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$k$</tex-math></inline-formula> -median clustering with differential privacy to publish the mixed data. Theoretical analysis shows that the proposed method satisfies <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\varepsilon$</tex-math></inline-formula> -differential privacy. Experimental results on real datasets illustrate that the proposed method has a much lower information loss and time overhead than the state-of-the-art approach for the same parameters.

Introduction and analysis of bitcoin-based blockchain technology principle

Blockchain technology is a technology that inherently solves trust issues. It has the character-istics of decentralization, distributed storage, tamper resistance, security, and transparency. It ensures reliable communication between nodes that do not trust each other through consensus mechanisms, smart contracts, and other means. Blockchain stores each transaction data on each transaction node to make the data public and transparent, and generates the data into a blockchain.. As a relatively new distributed database system, blockchain has expanded to many other fields since the initial digital currency, but its development is seriously constrained by problems such as large storage overhead and low query efficiency. In order to find a suita-ble optimization method, select the representative blockchain system Bitcoin, analyze its data structure, data storage and data query processing mechanism, discuss the problems existing in the two functions of storage and query, summarize the existing relevant optimization methods, and look forward to the main research problems of the blockchain system represented by Bitcoin in the future.

Research on Business Data Analysis and Mining based on Deep Learning

At present, the database system can efficiently realize the functions of data entry, query and statistics, but it can't find the rules of relationships in the database, and can't predict the future development trend according to the existing data. Using data mining technology, we can effectively find the regularity behind a large number of data, provide the hidden knowledge and means in the data, and eliminate the phenomenon of data "explosion but lack of knowledge". Based on DL (Deep Learning), this paper studies the analysis and mining of commercial data. In order to better assist investors to evaluate and make decisions on financial data, it puts forward the need to build a reliable and effective financial data forecasting model. In the original data obtained, it is necessary to interpolate by certain methods in data cleaning, and some repeated data will be duplicated. This paper analyzes and makes full use of these massive commercial information through DL, finds out the potential laws of business and market operation, and finds some unknown and hidden useful information from it, which helps enterprises to grasp the development direction of the industry and sales market from a macro perspective and better serve the vast number of consumers.

MAPLE: A Metadata-Hiding Policy-Controllable Encrypted Search Platform with Minimal Trust

Commodity encrypted storage platforms (e.g., IceDrive, pCloud) permit data store and sharing across multiple users while preserving data confidentiality. However, end-to-end encryption may not be sufficient since it only offers confidentiality when the data is at rest or in transit. Meanwhile, sensitive information can be leaked from metadata representing activities during data operations (e.g., query, processing). Recent encrypted search platforms such as DORY (OSDI’20) or DURASIFT (WPES’19) permit multi-user data query functionalities, while protecting metadata privacy. However, they either incur a high processing overhead or offer limited security/functionality, and require strong trust assumptions. We propose MAPLE, a new metadata-hiding encrypted search platform that offers query functionalities (search, update) on the shared data across multiple users with complex policy controls. MAPLE protects metadata privacy all the time during query processing, while achieving significantly (asymptotically) lower processing overhead than state-of-the-art platforms. The core technique of MAPLE is the design of oblivious data structures for search index and access control coupled with secure computation techniques to enable efficient query processing with a minimal trust. We fully implemented MAPLE and evaluated its performance on commodity cloud (Amazon EC2) under real settings. Experimental results showed that MAPLE achieved a concrete performance comparable with its counterparts, while offering provably stronger security guarantees and more diverse functionalities.

Multimodal video retrieval with CLIP: a user study

Recent machine learning advances demonstrate the effectiveness of zero-shot models trained on large amounts of data collected from the internet. Among these, CLIP (Contrastive Language-Image Pre-training) has been introduced as a multimodal model with high accuracy on a number of different tasks and domains. However, the unconstrained nature of the model begs the question whether it can be deployed in open-domain real-word applications effectively in front of non-technical users. In this paper, we evaluate whether CLIP can be used for multimodal video retrieval in a real-world environment. For this purpose, we implemented impa, an efficient shot-based retrieval system powered by CLIP. We additionally implemented advanced query functionality in a unified graphical user interface to facilitate an intuitive and efficient usage of CLIP for video retrieval tasks. Finally, we empirically evaluated our retrieval system by performing a user study with video editing professionals and journalists working in the TV news media industry. After having the participants solve open-domain video retrieval tasks, we collected data via questionnaires, interviews, and UI interaction logs. Our evaluation focused on the perceived intuitiveness of retrieval using natural language, retrieval accuracy, and how users interacted with the system’s UI. We found that our advanced features yield higher task accuracy, user ratings, and more efficient queries. Overall, our results show the importance of designing intuitive and efficient user interfaces to be able to deploy large models such as CLIP effectively in real-world scenarios.

Geographical name culture sharing query service system for the "Belt and Road"

With the advancement of the "One Belt and One Road" initiative and the rapid development of the world economy, given the problems of scattered geographical name culture resources and incomplete information in the areas along the "Belt and Road", this paper aims at the massive geographic information data along the "Belt and Road" and proposes a method based on web crawlers and task crowd. Package shared service platform construction method, and develop the client based on MySQL database and WebGIS, provide real-time data update and resolution, cultural information query and other functions, and realize geographic information and place name cultural query function. Open up new ideas for the construction of the "Belt and Road" platform, perceive and obtain geographical information of geographical names from the public's perspective, and provide real-time and dynamic inquiry channels for the public.

A vehicle license plate data access model based on the jump hash consistency algorithm.

The massive amount of vehicle plate data generated by intelligent transportation systems is widely used in the field of urban transportation information system construction and has a high scientific research and application value. The adoption of big data platforms to properly preserve, process, and exploit these valuable data resources has become a hot research area in recent years. To address the problems of implementing complex multi-conditional comprehensive query functions and flexible data applications in the key-value database storage environment of a big data platform, this paper proposes a data access model based on the jump hash consistency algorithm. Algorithms such as data slice storage and multi-threaded sliding window parallel reading are used to realize evenly distributed storage and fast reading of massive time-series data on clustered data nodes. A comparative analysis of data distribution uniformity and retrieval efficiency shows that the model can effectively avoid generating the cluster hotspot problem, support comprehensive analysis queries with various complex conditions, and maintain high query efficiency by precisely positioning the data storage range and utilizing parallel scan reading.

Exploring customer perception and satisfaction on the functions of parking application software

ABSTRACT The emergence of parking application software (parking APP), considerably decreases unnecessary vehicle circling and enhances parking resource utilization. Despite the abundant functions available, limited research has considered customer requirements and satisfaction regarding the functions of parking APPs. This research exactly examined the customer perception, as well as how satisfied customers were with the functions provided by the parking APPs. Initially, the attribute classification of functions was identified utilizing Kano model based on a questionnaire survey in Hunan Province, China. The integrated functions were then used as an antecedent to evaluate customer satisfaction by structural equation model (SEM). The data analysis suggests the perceived quality is positively influenced by the four main functions of query, navigation, evaluation, and account. The results reveal that customers have less demand for automatic space grabbing and reverse vehicle tracking in small and medium-sized cities.

Identifying Metal Binding Sites in Proteins Using Homologous Structures, the MADE Approach.

In order to identify the locations of metal ions in the binding sites of proteins, we have developed a method named the MADE (MAcromolecular DEnsity and Structure Analysis) approach. The MADE approach represents an evolution of our previous toolset, the ProBiS H2O (MD) methodology, for the identification of conserved water molecules. Our method uses experimental structures of proteins homologous to a query, which are subsequently superimposed upon it. Areas with a particular species present in a similar location among many homologous protein structures are identified using a clustering algorithm. Dense clusters likely represent positions containing species important to the query protein structure or function. We analyze well-characterized apo protein structures and show that the MADE approach can identify clusters corresponding to the expected positions of metal ions in their binding sites. The greatest advantage of our method lies in its generality. It can in principle be applied to any species found in protein records; it is not only limited to metal ions. We additionally demonstrate that the MADE approach can be successfully applied to predict the location of cofactors in computer-modeled structures, e.g., via AlphaFold. We also conduct a careful protein superposition method comparison and find our methodology robust and the results largely independent of the selected protein superposition algorithm. We postulate that with increasing structural data availability, additional applications of the MADE approach will be possible such as non-protein systems, water network identification, protein binding site elaboration, and analysis of binding events, all in a dynamic manner. We have implemented the MADE approach as a plugin for the PyMOL molecular visualization tool. The MADE plugin is available free of charge at https://gitlab.com/Jukic/made_software.

Knock-sideways by inducible ER retrieval enables a unique approach for studying Plasmodium-secreted proteins

Malaria parasites uniquely depend on protein secretion for their obligate intracellular lifestyle but approaches for dissecting Plasmodium-secreted protein functions are limited. We report knockER, a unique DiCre-mediated knock-sideways approach to sequester secreted proteins in the ER by inducible fusion with a KDEL ER-retrieval sequence. We show conditional ER sequestration of diverse proteins is not generally toxic, enabling loss-of-function studies. We employed knockER in multiple Plasmodium species to interrogate the trafficking, topology, and function of an assortment of proteins that traverse the secretory pathway to diverse compartments including the apicoplast (ClpB1), rhoptries (RON6), dense granules, and parasitophorous vacuole (EXP2, PTEX150, HSP101). Taking advantage of the unique ability to redistribute secreted proteins from their terminal destination to the ER, we reveal that vacuolar levels of the PTEX translocon component HSP101 but not PTEX150 are maintained in excess of what is required to sustain effector protein export into the erythrocyte. Intriguingly, vacuole depletion of HSP101 hypersensitized parasites to a destabilization tag that inhibits HSP101-PTEX complex formation but not to translational knockdown of the entire HSP101 pool, illustrating how redistribution of a target protein by knockER can be used to query function in a compartment-specific manner. Collectively, our results establish knockER as a unique tool for dissecting secreted protein function with subcompartmental resolution that should be widely amenable to genetically tractable eukaryotes.

Visual Analytics of Multidimensional Oral Health Surveys: Data Mining Study.

Oral health surveys largely facilitate the prevention and treatment of oral diseases as well as the awareness of population health status. As oral health is always surveyed from a variety of perspectives, it is a difficult and complicated task to gain insights from multidimensional oral health surveys. We aimed to develop a visualization framework for the visual analytics and deep mining of multidimensional oral health surveys. First, diseases and groups were embedded into data portraits based on their multidimensional attributes. Subsequently, group classification and correlation pattern extraction were conducted to explore the correlation features among diseases, behaviors, symptoms, and cognitions. On the basis of the feature mining of diseases, groups, behaviors, and their attributes, a knowledge graph was constructed to reveal semantic information, integrate the graph query function, and describe the features of intrigue to users. A visualization framework was implemented for the exploration of multidimensional oral health surveys. A series of user-friendly interactions were integrated to propose a visual analysis system that can help users further achieve the regulations of oral health conditions. A visualization framework is provided in this paper with a set of meaningful user interactions integrated, enabling users to intuitively understand the oral health situation and conduct in-depth data exploration and analysis. Case studies based on real-world data sets demonstrate the effectiveness of our system in the exploration of oral diseases.