List Structure Research Articles

Method for Mining High-utility Patterns in Transaction Stream Data based on Linked List Structure

Mining valuable patterns in data streams presentsa significant challenge in the field of data mining. Thistask is crucial as it allows for the identification of highlyprofitable item sets within transaction databases. However, asnew transactions are continually added, new valuable patternsemerge, thus changing the usefulness of previously analyzeddata. It is essential to promptly update information regardingthese changes to enable effective business decision-making.Consequently, existing mining methods applied to transactionflow datasets require considerable time to identify newpatterns and update information related to new transactions.This article focuses on the research and proposal of a newtransaction stream data mining method called High-UtilityStream Linked-List Mining. The method utilizes a linkedlist structure known as the High-Utility Stream Linked List(HUSLL) to store information about patterns in the database.Mining and updating transaction information are directlyperformed on the HUSLL structure. Experimental resultsdemonstrate that this novel mining method exhibits moreefficient execution times compared to previous solutions.

Biophysical characterization of high-confidence, small human proteins

Significant efforts have been made to characterize the biophysical properties of proteins. Small proteins have received less attention because their annotation has historically been less reliable. However, recent improvements in sequencing, proteomics, and bioinformatics techniques have led to the high-confidence annotation of small open reading frames (smORFs) that encode for functional proteins, producing smORF-encoded proteins (SEPs). SEPs have been found to perform critical functions in several species, including humans. While significant efforts have been made to annotate SEPs, less attention has been given to the biophysical properties of these proteins. We characterized the distributions of predicted and curated biophysical properties, including sequence composition, structure, localization, function, and disease association of a conservative list of previously identified human SEPs. We found significant differences between SEPs and both larger proteins and control sets. In addition, we provide an example of how our characterization of biophysical properties can contribute to distinguishing protein-coding smORFs from noncoding ones in otherwise ambiguous cases.

A bitemporal RDF index based on skip list

The Resource Description Framework (RDF) is a framework for expressing information about resources in the form of triples (subject, predicate, object). The information represented by the standard RDF is static, i.e., that does not change over time. To better deal with a large amount of time-related information, temporal RDF is proposed. Consequently, how to explore index technology to efficiently query temporal information has become an important research issue, but the research on the index of temporal RDF is still short, especially the index of bitemporal RDF. BitemporalRDF can represent more complicated situations (e.g., RDF triples with both validtime and transactiontime). Indexes for bitemporal RDF can further expand the application scenarios and functions of temporal RDF. In this paper, we propose an efficient index for bitemporal RDF queries. The index innovatively introduces and re-designs skip list structure into the bitemporal RDF query. We also investigate how to cover almost all query patterns with as few indexes as possible. In addition, although the proposed index is conceived for temporal RDF, it also takes into account the performance of standard RDF queries when the time element is unknown. Finally, we run experiments with synthetic data sets of different sizes using the Lehigh University Benchmark (LUBM), and results prove that the proposed index is scalable and effective.

A Model of Forming a Dynamic Structure for Establishing the Source of Messages in the Receiver's Memory

The purpose of research is increasing the speed of procedures for determining the data source in the block coupling mode, due to the analysis of the dynamic list structure of messages formed in the receiver's memory as a result of intermediate calculations.Methods. The model of forming a dynamic list structure is based on the hardware implementation of the method of limiting the set of data blocks processed by the receiver. The data package includes a special service word, the contents of which are checked for falling into the range of values formed by the receiver when each data block is received. The described restriction reduces the number of typical comparison operations of service words performed when determining the source of messages, and also reduces the likelihood of errors in determining the data source.Results. Based on the model of the formation of a dynamic tree-like list structure, distributions of a priori probabilities of the number of nodes of a certain level are obtained in case of errors in determining the data source and without them. This allows us to obtain significant a posteriori error probabilities depending on the observed number of nodes of a certain level. The criteria for making a decision on the error of determining the source based on the calculation of the number of nodes before the complete completion of the formation of the tree structure and before the stage of its analysis are formulated. This reduces the computational complexity of the procedure for determining the data source in the block coupling mode and reduces the memory costs for storing intermediate results.Conclusion. In the course of the study, it was revealed that for sequences of messages with a length of more than 20, the detection of more than 8 extraneous branches of the dynamic list structure being formed allows us to state with a 90% probability that the procedure for determining the source ended in an error. The refusal to transmit subsequent sequence messages and to perform processing operations of the tree structure allows to increase the speed of the procedure for determining the source of messages and reduce its computational complexity.

Improved adaptive-phase fuzzy high utility pattern mining algorithm based on tree-list structure for intelligent decision systems

With the rapid development of AI and big data mining technologies, computerized medical decision-making has become increasingly prominent. The aim of high-utility pattern mining (HUPM) is to discover meaningful patterns in medical databases that contribute to maximizing the utility from the perspective of diagnosis. However, HUPM pays less attention to the interpretability and explainability of these patterns in medical decision-making scenarios. This paper proposes a novel algorithm called the Improved fuzzy high-utility pattern mining (IF-HUPM) to address this problem. First, the paper applies a fuzzy preprocessing method to divide the fuzzy intervals of a medical quantitative data set, which enhances the fuzziness and interpretability of the data. Next, in the process of IF-HUPM, both fuzzy tree and list structures are employed to calculate fuzzy high-utility values. By combining the characteristics of the one-stage and two-stage algorithms of HUPM, an adaptive-phase Fuzzy HUPM hybrid frame is proposed. The experimental results demonstrate that the proposed IF-HUPM algorithm enhances both accuracy and efficiency and the mining process requires less time and space on average.

On the issue of parallel import of medical articles and component units to them in the Russian Federation in conditions of sanctions policy

Against the background of sanction policy and escalation of political conflict of Russia with unfriendly countries, trade relations with many foreign suppliers degraded. The current situation affected supply of medical goods, most of which were imported from countries of the European Union. At the state level, it was decided to legalize parallel import. As regards medical articles practical implementation of this mechanism met large number of exceptions that resulted in delays and disruptions of supplies. Indisputably, main problem remains inability to provide necessary amount of medical goods by national industry. The national market of medical articles and its components always significantly depended on import and found oneself completely unprepared for rupture of trade relations with unfriendly countries. It resulted in legalization of parallel import for particular categories of medical goods that seems to be possible solution of current problem. The article examines in detail basic principles of exhaustion of exclusive right to intellectual property objects. The analysis is presented concerning classification groupings of medical articles for customs purposes under the Customs Code of the EAEU. Also is analyzed structure of lists of products that are subject to legalization of parallel import in retrospective view. The main problematic aspects in analyzed issue were identified and recommendations for their elimination were formulated.

Efficient approach for mining high-utility patterns on incremental databases with dynamic profits

High-utility itemset mining (HUIM) is one of the heavily studied fields of data mining, which is due to its high compatibility with real-world applications. HUIM is a process of extracting a complete set of interesting patterns by considering the importance and the quantity of each item. Incremental high-utility itemset mining (IHUIM) further increases the compatibility of HUIM by mining from a data stream instead of a static database. It does this by mining interesting patterns in a single database scan and storing prior knowledge in a compact data structure. However, there is a critical limitation, because the importance of each item has to be guaranteed to remain unchanged throughout the data stream. This limitation hinders the compatibility of IHUIM to the real-world applications, because the importance of an item changes from time to time in the real world. Conventional IHUIM approaches consequently have to use outdated information when mining interesting patterns. This paper proposes a novel problem of mining high-utility itemsets in an incremental and dynamic profit environment in order to account for the fluctuation of the item's importance. Furthermore, a novel approach to mining patterns in this type of environment is introduced using an efficient list structure and tight upper bounds. Experiments on real and synthetic datasets show that the proposed approach performs well compared to the state-of-the-art approaches in terms of the runtime and memory usage, and it scales better than the other approaches.

Design of buffered double linked list for LEO satellite beam‐hopping forwarding

SummaryThe beam‐hopping technology can solve the problems of uneven user distribution and uneven service demand and improve the efficiency of resource utilization. It is one of the important technologies of future satellite–ground integrated communication. However, the mobility of LEO satellites and the on‐demand scheduling of beams make each satellite beam correspond to multiple ground wave positions, which brings great difficulties to data processing. In this paper, after analyzing the difficulties of the data forwarding buffer of the beam‐hopping system, a double‐linked list structure of user state chain and data buffer chain is proposed, which solves the problem of data forwarding between the satellite and users in the dynamic state and provides an efficient solution for the data exchange of the beam‐hopping LEO satellite.

Spaced Retrieval Practice: Can Restudying Trump Retrieval?

We investigated spaced retrieval and restudying in 3 preregistered, online experiments. In all experiments, participants studied 40 Swahili–English word pair translations during an initial study phase, restudied intact pairs or attempted to retrieve the English words to Swahili cues twice in three spaced practice sessions, and then completed a final cued-recall test. All 5 sessions were separated by 2 days. In Experiment 1, we manipulated the response format during retrieval (covert vs. overt) and the test list structure (blocked vs. intermixed covert/overt retrieval trials). A memory rating was required on all trials (retrieval: “Was your answer correct?”; restudy: “Would you have remembered the correct translation?”). Response format had no effect on recall, but surprisingly, final test performance for restudied items exceeded both the overt and covert retrieval conditions. In Experiment 2, we manipulated the requirement to make a memory rating. If a memory rating was required, final test restudy performance exceeded retrieval performance, replicating Experiment 1. However, the pattern was descriptively reversed if no rating was required. In Experiment 3, the memory rating was removed altogether, and we examined recall performance for items restudied versus retrieved once, twice, or thrice. Performance improved with practice, and retrieval performance exceeded restudy performance in all conditions. The reversal of the typical retrieval practice effect observed in Experiments 1 and 2 is discussed in terms of theories of reactivity of memory judgments.

A tabu search algorithm for the unrelated parallel machine scheduling problem with varied carbon emission constraints in different time intervals

The carbon emissions generated during the rapid development of the manufacturing industry have caused severe environmental pollution. Research on parallel machine scheduling problems considering carbon emission constraints has started to emerge in recent years to promote sustainable development and achieve low carbon emissions in production. In this paper, we consider an unrelated parallel machine scheduling problem with various carbon emission constraints in different time intervals. A mixed integer linear programming model with the objective of minimising the makespan is formulated to describe the problem accurately. Since the high complexity of our problem, we propose an efficient tabu search algorithm with a dedicated linked list structure to address this problem. Firstly, we generate four initial sequences according to the proposed heuristic rules and apply a greedy insertion decoding method to obtain the scheduling scheme. Then, we employ the 2-swap neighbourhood search strategy to exploit the promising solution space. The proposed model and algorithm are tested on extensive instances generated randomly. Computational results validate the correctness of the model and the effectiveness of the tabu search algorithm.

Implementation of Web Scraping on Google Search Engine for Text Collection Into Structured 2D List

Purpose: This research proposes the implementation of web scraping on Google Search Engine to collect text into a structured 2D list.Design/methodology/approach: Implementing two important stages in the process of collecting data through web scraping, namely the HTML parsing process to extract links (URL) on Google Search Engine pages, and HTML parsing process to extract the body text from website pages on each link that has been collected.Findings/result: The inputted query is adjusted to the latest issues and news in Indonesia, for example the President's important figures, the month of Ramadan and Idul Fitri, riots tragedy (stadium) and natural disasters, rising prices of basic commodities, oil and gold, as well as other news. The least number of links obtained was 56 links and the most was 151 links, while the processing time to obtain links for each of the fastest queries was 1 minute 6.3 seconds and the longest was 2 minutes 49.1 seconds. The results of scraping links from these queries were obtained from Wikipedia, Detik, Kompas, the Election Supervisory Body (Bawaslu), CNN Indonesia, the General Election Commission (KPU), Pikiran Rakyat, and others.Originality/value/state of the art: Based on previous research, this study provides an alternative to produce optimal collection of links and text from web scraping results in the form of a 2D list structure. Lists in the Python programming language can store character sequences in the form of strings and can be accessed using index keys, and manipulate text efficiently.

Pre-large based high utility pattern mining for transaction insertions in incremental database

High utility pattern mining has been actively researched and applied to diverse applications because it can process the database by considering the quantity and importance of items. However, traditional high utility pattern mining methods aim to handle static databases, so they cannot meet the requirements of users who want to process the dynamic environments. Although methods to process incremental databases have been proposed, they have limitations that they perform the mining process on the entire database, including already processed data, whenever data are newly inserted. The pre-large concept is one of the techniques to process the dynamic database. Utilizing the pre-large technique, we can efficiently handle the transaction insertion using the extracted patterns of the previous mining process. In this paper, we propose a novel pre-large-based approach to discover high utility patterns from incremental databases. A list structure is proposed to store the utility information of patterns, so candidate patterns are not generated, and an additional database scan is not required. Performance evaluation performed on various real and synthetic datasets shows that the proposed algorithm is more efficient and effective than the latest approaches in a dynamic environment.

Evaluating the conceptual strategy change account of test-potentiated new learning in list recall

Prior testing potentiates new learning, an effect known as test-potentiated new learning (TPNL). Research using lists of related words has established that testing, by free recall, also increases semantic clustering of later recall output. It has been suggested that this is evidence that testing induces a strategy change in encoding and retrieval towards greater conceptual organisation. The current research evaluated whether this conceptual strategy change explains TPNL in three experiments. We found a) that a retrieval task that did not increase semantic clustering (list discrimination) consistently produced TPNL, and b) that factors (word-relatedness and list structure) that influenced the amount of semantic clustering had no effect on the magnitude of TPNL. These results suggest that conceptual strategy change is neither necessary nor sufficient for TPNL and is more likely to be an effect of testing, rather than a cause of TPNL.

CASBERT: BERT-based retrieval for compositely annotated biosimulation model entities.

Maximising FAIRness of biosimulation models requires a comprehensive description of model entities such as reactions, variables, and components. The COmputational Modeling in BIology NEtwork (COMBINE) community encourages the use of Resource Description Framework with composite annotations that semantically involve ontologies to ensure completeness and accuracy. These annotations facilitate scientists to find models or detailed information to inform further reuse, such as model composition, reproduction, and curation. SPARQL has been recommended as a key standard to access semantic annotation with RDF, which helps get entities precisely. However, SPARQL is unsuitable for most repository users who explore biosimulation models freely without adequate knowledge of ontologies, RDF structure, and SPARQL syntax. We propose here a text-based information retrieval approach, CASBERT, that is easy to use and can present candidates of relevant entities from models across a repository's contents. CASBERT adapts Bidirectional Encoder Representations from Transformers (BERT), where each composite annotation about an entity is converted into an entity embedding for subsequent storage in a list of entity embeddings. For entity lookup, a query is transformed to a query embedding and compared to the entity embeddings, and then the entities are displayed in order based on their similarity. The list structure makes it possible to implement CASBERT as an efficient search engine product, with inexpensive addition, modification, and insertion of entity embedding. To demonstrate and test CASBERT, we created a dataset for testing from the Physiome Model Repository and a static export of the BioModels database consisting of query-entities pairs. Measured using Mean Average Precision and Mean Reciprocal Rank, we found that our approach can perform better than the traditional bag-of-words method.

MRCIF: A Memory-Reverse-Based Code Injection Forensics Algorithm

The new DLL injection method and its variants can prevent the injected process from calling the common system API to load the injected DLL module so that the malicious module is invisible to the LDR linked list of the process. Traditional injection detection methods have low accuracy in forensic detection of new injection attacks. To solve this problem, this paper proposes a code injection covert memory page detection and forensic detection forensic algorithm based on a memory structure reverse analysis named MRCIF. First, the physical memory pages containing DLL features from the memory image are located, and a sub-algorithm is designed for mapping physical memory space and virtual memory space, thus realizing the reverse reconstruction of the physical page subset corresponding to the DLL code module. Then, in the virtual memory space, the LDR linked list structure of the process is reversely reconstructed, and a reverse reconstruction algorithm of the DLL virtual page subset is developed to reconstruct its virtual space. Finally, a DLL injection covert page detection sub-algorithm is designed based on the physical memory page subset and virtual space page subset. The experimental results indicate that MRCIF achieves an accuracy of 88.89%, which is much higher than that of the traditional DLL module injection detection method, and only MRCIF can accurately detect the Virtual Address Descriptor (VAD) remapping attack.

FCHM-stream: fast closed high utility itemsets mining over data streams

The high-speed, continuous and endless characteristics of data streams make it a challenging task to quickly mine high utility itemsets in limited memory space. The sliding window model, which focuses only on the most recent data, has received extensive research and attention as it can effectively adapt to the data stream environment. However, the presence of many communal batches in adjacent sliding windows causes the algorithm to repeatedly generate a large number of identical itemsets, which reduces the spatiotemporal performance of the algorithm. In order to solve these problems and provide users with a concise and lossless resultset, a new closed high utility pattern mining algorithm over data stream is proposed, named FCHM-Stream. A new utility list structure based on batch division and a resultset maintenance strategy based on skip-list structure are designed to effectively reduce identical itemsets repeatedly generated and thus reduce the running time of the algorithm. Extensive experimental results show that the proposed algorithm has a large improvement in runtime compared to the state-of-the-art algorithms.

Recommendations on the use of item libraries for patient-reported outcome measurement in oncology trials: findings from an international, multidisciplinary working group

The use of item libraries for patient-reported outcome (PRO) measurement in oncology allows for the customisation of PRO assessment to measure key health-related quality of life concepts of relevance to the target population and intervention. However, no high-level recommendations exist to guide users on the design and implementation of these customised PRO measures (item lists) across different PRO measurement systems. To address this issue, a working group was set up, including international stakeholders (academic, independent, industry, health technology assessment, regulatory, and patient advocacy), with the goal of creating recommendations for the use of item libraries in oncology trials. A scoping review was carried out to identify relevant publications and highlight any gaps. Stakeholders commented on the available guidance for each research question, proposed recommendations on how to address gaps in the literature, and came to an agreement using discussion-based methods. Nine primary research questions were identified that formed the scope and structure of the recommendations on how to select items and implement item lists created from item libraries. These recommendations address methods to drive item selection, plan the structure and analysis of item lists, and facilitate their use in conjunction with other measures. The findings resulted in high-level, instrument-agnostic recommendations on the use of item-library-derived item lists in oncology trials.

Mentions of Groups of Noble Captives in Intrastate Conflicts in Pre-Mongolian Rus

The article examines chronicle evidences about capture of noble captives during intrastate cionflicts of knyazs in pre-Mongolian Rus. Evidences given in chronological order and related to the circumstances of the respective military conflicts. The analysis of the texts of the chronicle evidences has been done. Note the restrained nature of the texts, the short reference to captives, and hierarchical structure of list of captives. It is concluded that the capture of noble captives is connected with the political ambitions of the knyazs.

Ефективний чисельний метод длярозв’язання рiвняння Рiчардса-Клюта звiдстеженням зони повного насичення

The article presents modifications for numerical methods for modeling of mass transfer process in porous medium with full saturated zone tracking. The goal of the article is to increase computational efficiency of finding an approximate solution process using division of the area into the two non-intersecting parts: unsaturated zone and zone with full saturation. Numerical methods for solving the one-dimensional Richards-Klute equation with tracking of the full saturated zone have been developed. The cases of monotonic solution and solution with general properties of Richards-Klute equation were considered. A modification of the full saturated zone tracking process using a doubly connected edge list structure have been developed for two-dimensional case. Efficiency increase estimation is proven for one- and two-dimensional cases using probability distibution for a measure of the full saturated zone. A comparative analysis of the proposed modifications was carried out. The results of numerical experiments coincide with the estimates predicted by theory.

Optimizing Storage for Energy Conservation in Tracking Wireless Sensor Network Objects

The amount of needed control messages in wireless sensor networks (WSN) is affected by the storage strategy of detected events. Because broadcasting superfluous control messages consumes excess energy, the network lifespan can be extended if the quantity of control messages is decreased. In this study, an optimized storage technique having low control overhead for tracking the objects in WSN is introduced. The basic concept is to retain observed events in internal memory and preserve the relationship between sensed information and sensor nodes using a novel inexpensive data structure entitled Ordered Binary Linked List (OBLL). Whenever an object passes over the sensor area, the recognizing sensor can immediately produce an OBLL along the object’s route. To retrieve the entire information, the OBLL can be traversed with logarithmic complexity which is much less than the traversing complexity of existing linked list structures. Performance evaluation and simulations were carried out to ensure that the suggested technique minimizes the number of messages and thus saving energy and extending the network life.