List Structure Research Articles

Methods for automated formation of a disassembled listing structure

The subject of the research is the method of splitting a disassembled code into logical blocks in automatic mode, searching for software vulnerabilities without using source code (using a binary file or its equivalent, obtained by reverse engineering).The object of the research is the existing code analyzers and features of their functionality.The aim of the study is to consider the possibility of splitting a disassembled code into logical blocks in automatic mode and some of the possible difficulties associated with this.Formulation of the problem. The complexity of analyzing large software products at the level of machine code necessitates the automation of this process. The research methodology is based on a combination of theoretical and empirical approaches using the methods of static and dynamic analysis, comparison, generalization, algorithmization, modeling, synthesis. Key findings. Splitting the code into blocks by sequential in line-by-line analysis of machine code in some cases can lead to misinterpretation. In addition, the analysis of the code according to the conclusions of the functions also does not guarantee the correctness of the determination of the boundaries of the functions. However, in general, the matrix method can be applied to analyze the dependencies of functions on the blocks of code thus selected.The scientific novelty is connected with the determination of promising vectors for the study of software code for vulnerability, the rationale for the approach (building the transition matrix from integer values), which may be the initial stage of preparation for the automated analysis of the disassembled code.

Efficient approach for incremental high utility pattern mining with indexed list structure

Since traditional frequent pattern mining approaches assume that all the items in binary databases have the same importance regardless of their own features, they have difficulty in satisfying requirements of real world applications such as finding patterns with high profits. High utility pattern mining was proposed to deal with such an issue, and various relevant works have been researched. There have been demands for efficient solutions to find interesting knowledge from specific environments in which data accumulates continuously with the passage of time such as social network service, wireless network sensor data, etc. Although several algorithms have been devised to mine high utility patterns from incremental databases, they still have performance limitations in the process of generating a large number of candidate patterns and identifying actually useful results from the found candidates. In order to solve the problems, we propose a new algorithm for mining high utility patterns from incremental databases. The newly proposed data structures in a list form and mining techniques allow our approach to extract high utility patterns without generating any candidates. In addition, we suggest restructuring and pruning techniques that can process incremental data more efficiently. Experimental results on various real and synthetic datasets demonstrate that the proposed algorithm outperforms state-of-the-art methods in terms of runtime, memory, and scalability.

Mining Top-k Regular High-Utility Itemsets in Transactional Databases

Mining high-utility itemsets is an important task in the area of data mining. It involves exponential mining space and returns a very large number of high-utility itemsets. In a real-time scenario, it is often sufficient to mine a small number of high-utility itemsets based on user-specified interestingness. Recently, the temporal regularity of an itemset is considered as an important interesting criterion for many applications. Methods for finding the regular high utility itemsets suffers from setting the threshold value. To address this problem, a novel algorithm called as TKRHU (Top k Regular High Utility Itemset) Miner is proposed to mine top-k high utility itemsets that appears regularly where k represents the desired number of regular high itemsets. A novel list structure RUL and efficient pruning techniques are developed to discover the top-k regular itemsets with high profit. Efficient pruning techniques are designed for reducing search space. Experimental results show that proposed algorithm using novel list structure achieves high efficiency in terms of runtime and space.

Comparing breast cancer treatments using automatically detected surrogate and clinically relevant outcomes entities from text

Population, intervention, comparison and outcome (PICO) facets of clinical studies are required both for physicians in a clinical setting and for reviewers as they compare the effectiveness of different treatment strategies. Automated methods developed for the first three of these facets identify entities, but outcome detection has been limited to identifying the entire sentence. We frame outcome detection as a noun phrase prediction task and use semi-supervised learning to detect new outcomes (aka endpoints) from the method section of 88 K MEDLINE abstracts. A manual analysis showed that 96.7% of all outcomes can be captured using a noun phrase representation. With respect to the machine learning classifiers, the Support Vector Machine produced higher precision, F1-score, and accuracy than the General Linear Model when evaluated with respect to the initial gold standard of survivorship seed terms and a manual gold standard that considered all outcomes. However, the best model does not employ machine learning, but rather leverages list structure and resulted in 90.14 precision, 60.69 recall, 75.41 F1-score, and 92.60 accuracy with respect to the manual gold standard of all outcomes. Finally we developed a silver standard with a precision of 89.28 and recall of 86.77 compared to the manual gold standard and used the silver standard to identify all outcomes reported for five breast cancer treatments. The increased precision afforded by this approach reveals that in contrast to chemotherapy and targeted therapy, the surrogate outcome disease free survival (DFS) is reported more frequently than the clinically relevant outcome overall survival (OS) for hormone therapies, which is consistent with findings that DFS translates into firm OS improvements in a hormone therapy setting.

Efficient and Robust Approximate Nearest Neighbor Search Using Hierarchical Navigable Small World Graphs

We present a new approach for the approximate K-nearest neighbor search based on navigable small world graphs with controllable hierarchy (Hierarchical NSW, HNSW). The proposed solution is fully graph-based, without any need for additional search structures (typically used at the coarse search stage of the most proximity graph techniques). Hierarchical NSW incrementally builds a multi-layer structure consisting of a hierarchical set of proximity graphs (layers) for nested subsets of the stored elements. The maximum layer in which an element is present is selected randomly with an exponentially decaying probability distribution. This allows producing graphs similar to the previously studied Navigable Small World (NSW) structures while additionally having the links separated by their characteristic distance scales. Starting the search from the upper layer together with utilizing the scale separation boosts the performance compared to NSW and allows a logarithmic complexity scaling. Additional employment of a heuristic for selecting proximity graph neighbors significantly increases performance at high recall and in case of highly clustered data. Performance evaluation has demonstrated that the proposed general metric space search index is able to strongly outperform previous opensource state-of-the-art vector-only approaches. Similarity of the algorithm to the skip list structure allows straightforward balanced distributed implementation.

On decidability of list structures

On decidability of list structures

Efficiently Updating the Discovered Multiple Fuzzy Frequent Itemsets with Transaction Insertion

Most pattern mining approaches such as association rule mining or frequent itemsets mining can only handle, however, the binary database in which each item or attribute is represented as 0 or 1. In real-life situations, data may be represented as more complicatedly. Moreover, it is more meaningful for human begins to understand if the discovered rules can be represented as the linguistic terms. Fuzzy-set theory was adopted to handle the quantitative database, which transforms the quantity value into the linguistic terms with their fuzzy values based on the pre-defined membership functions. Several algorithms were designed to handle this issue, but most of them focus on mining the fuzzy rules in the static database. When the database size is changed, the whole updated database should be rescanned again to obtain the up-to-date information. This progress ignores the already discovered knowledge with its computational cost, which is inefficient in dynamic applications. In this paper, we present an incremental multiple fuzzy frequent itemset mining with transaction INSertion (IMF-INS) algorithm to efficiently update the multiple fuzzy frequent itemsets from the quantitative dataset. The designed IMF-INS utilizes the fuzzy FUP concept to divide the transformed linguistic terms into four cases. Each case is performed by the designed approach to update the discovered information. Also, the fuzzy list structure is utilized to reduce the generation of candidates without multiple database scans. Experiments are conducted to show the performance of the proposed algorithm in terms of runtime, memory consumption, number of determined patterns and scalability. Results indicated that the designed IMF-INS outperforms the state-of-the-art batch mining progresses.

Maintenance algorithm for high average-utility itemsets with transaction deletion

High-utility itemset mining (HUIM) is an extension of traditional association-rule mining that can find profitable itemsets for decision-making. It faces, however, a limitation since the utility of an itemset increases along with the size of it. High-average utility itemset mining (HAUIM) provides a fair measure to find the average-utility of an itemset, which is more reasonable to design the sales strategies for making the efficient decision. Traditional algorithms of HAUIM mostly focus on mining high average-utility itemsets (HAUIs) from the static database. When the database size is changed, for example, transaction insertion/deletion, the discovered information is required to be updated, thus the updated database is necessary to be re-scanned for identifying the set of HAUIs in the batch manner. In this paper, we present an updating algorithm called FUP-HAUIMD to maintain the discovered HAUIs with transaction deletion. When some transactions in the database are deleted, the designed FUP-HAUIMD algorithm can easily update the discovered HAUIs without scanning the database all the time. The designed FUP-HAUIMD algorithm divides the itemsets into four cases based on the modified fast updated (MFUP) concept. The average-utility (AU)-list structure is further utilized to keep the necessary ramification for later mining progress. Experiments are then conducted to compare the designed FUP-HAUIMD algorithm with the state-of-the-art baseline algorithm running on the batch mode, and the developed approach shows better performance in terms of runtime, number of examined patterns, and scalability.

A Novel Fitness Computation Framework for Nature Inspired Classification Algorithms

Nature inspired algorithms have become popular for discovering classification rules due to their ability to effectively handle large and complex search spaces. However, nature inspired algorithms have to compute the fitness of individuals (candidate classification rules) over successive generations repeatedly. Each fitness computation requires scanning the training data. Since the database scan is computationally expensive operation, the execution time for nature inspired algorithms for discovering classification rules grows unreasonably faster for bulky datasets. This paper proposes a novel fitness computation framework for nature inspire algorithms by using a list structure. The indices of instances, covered by every possible attribute-value pair with respect to each class in the training data, are stored in the suggested list structure. The list is prepared only once in advance and stores all information to compute the fitness of any rule that may come up in the life time of the nature inspired algorithm. The existence of the pre-maintained list eliminates the need of scanning training data again and again for fitness computation. We have conducted experiments on 12 datasets from UCI machine learning repository. The results show that the suggested fitness computational framework brings significant speed gain without compromising predictive accuracy. Although, a Genetic Algorithm is used for classification rule discovery as the nature inspired algorithm in this paper, the fitness computation framework is general and can be used with any other nature inspired algorithm.

High Utility Itemset Mining Using Partition Utility List Structure

High Utility Itemset Mining Using Partition Utility List Structure

MEMU: More Efficient Algorithm to Mine High Average-Utility Patterns With Multiple Minimum Average-Utility Thresholds

High average-utility itemsets mining (HAUIM) is an emerging topic in data mining. Compared to traditional high utility itemset mining, HAUIM more fairly measures the utility of itemsets by considering their lengths (number of items). Many previous studies have presented algorithms to efficiently mine high average-utility itemsets (HAUIs). Most of these algorithms, however, only mine HAUIs using a single minimum high average-utility threshold, which limits their usefulness to analyze real data. This is a problem because different items are not equally important to the user. The importance of an item can be expressed for example in terms of weights, interestingness or unit profit. In the past, a baseline algorithm called HAUIM-MMAU was presented to mine HAUIs using multiple minimum high average-utility thresholds. However, it uses a generate-and-test approach to mine HAUIs using a level-wise approach, which is time consuming. In this paper, we propose an efficient algorithm to discover HAUIs based on the average-utility list structure. A tighter upper-bound model is used to reduce the search space instead of the one used in traditional HAUIM, which is called the auub model. Three pruning strategies are also respectively developed to increase the performance HAUIs. Experiments show that the proposed algorithm outperforms the state-of-the-art HAUIM-MMAU algorithm in terms of runtime, memory usage, number of candidates, and scalability.

EVALUATION OF WORKERS ‘EMPLOYMENT IN RUSSIAN ENTERPRISES IN DIFFERENT SPHERES OF ACTIVITY

The article examines the fluidity of human resources at Russian enterprises by types of sectoral activity in the period from 2014-2016. The estimation of the number of accepted employees of the list structure in the economy as a whole is given, and the number of retired employees of the list structure as a whole for the economy in the Russian Federation is analyzed. In the conditions of the transition economy, one of the topical and acute problems is the problem of personnel turnover at the enterprise, the solution of which predetermines the preservation of the stability and development of organizations, as well as the provision of jobs for the population of Russia. To identify the changes, the number of personnel taken and left at Russian enterprises was calculated by types of economic activities from 2014 to 2016. (in thousands of people). It was revealed that many companies engaged in “production" need to revise their policy with regard to frequent staff changes. Frequent dismissal of employees can lead to significant economic losses. In addition, turnover creates human and technological difficulties, adversely affects other employees, their motivation and loyalty to the company. Also, constant changes in the team are very poorly reflected in the working relationships that ensure coherent work. The reasons for the problem of the inconstancy of workers at Russian enterprises by types of economic activ ity arecons idered, and later the waysof solutio n a re proposed.

The diversity of larger fungi in the vicinities of Khanty-Mansiysk (middle taiga of West Siberia)

Публикация посвящена таксономическому анализу сообщества макромицетов в окрестностях г. Ханты-Мансийска (пос. Шапша, бореальная зона Западной Сибири). Площадки постоянного мониторинга были заложены в темнохвойных смешанных лесах и их послерубочных производных в начале лета 2015 года. Суммарная площадь наблюдения на площадках составила 1000 м2. Кроме того, в тех же типах растительности были проложены маршруты для выявления редких видов. Площадки посетили 6 раз в течение сезона с мая по сентябрь 2015 года.
 В настоящей публикации проведен таксономический анализ выявленной микобиоты. Мы определили 460 видов из 6 классов, 14 порядков, 55 семейств и 130 родов. Микобиота окрестностей пос. Шапша богаче описанной ранее в районе Ханты-Мансийска на стационаре Мухрино по числу видов, и существенно отличается по составу (около половины видов каждого списка – уникальны). Выявленный список видов в значительной степени расширяет список микобиоты ХМАО: 224 вида указывается впервые для территории. Восемь видов, зарегистрированных в окр. Шапши, занесены в Красную книгу ХМАО.
 Обобщенная таксономическая структура выявленной микобиоты приводится в таблице. Полные сведения о находках содержатся в электронном приложении к публикации.

L* Algorithm\u2014A Linear Computational Complexity Graph Searching Algorithm for Path Planning

The state-of-the-art graph searching algorithm applied to the optimal global path planning problem for mobile robots is the A* algorithm with the heap structured open list. In this paper, we present a novel algorithm, called the L* algorithm, which can be applied to global path planning and is faster than the A* algorithm. The structure of the open list with the use of bidirectional sublists (buckets) ensures the linear computational complexity of the L* algorithm because the nodes in the current bucket can be processed in any sequence and it is not necessary to sort the bucket. Our approach can maintain the optimality and linear computational complexity with the use of the cost expressed by floating-point numbers. The paper presents the requirements of the L* algorithm use and the proof of the admissibility of this algorithm. The experiments confirmed that the L* algorithm is faster than the A* algorithm in various path planning scenarios. We also introduced a method of estimating the execution time of the A* and the L* algorithm. The method was compared with the experimental results.

Efficient algorithms for mining top-rank-k erasable patterns using pruning strategies and the subsume concept

Mining erasable patterns (EPs) is one of the emerging tasks in data mining which helps factory managers to establish plans for the development of new systems of products. However, systems usually face the problem of many EPs. Therefore, the problem of mining top-rank-k EPs, and an algorithm for mining these using the PID_List structure named VM, were proposed in 2013. In this paper, we propose two efficient methods, named the TEP (Top-rank-k Erasable Pattern mining) and TEPUS (Top-rank-k Erasable Pattern mining Using the Subsume concept) algorithms, for mining top-rank-k EPs. The TEP algorithm uses the dPidset structure to reduce the memory usage and a dynamic threshold pruning strategy to accelerate the mining process. The TEPUS algorithm is the extension of the TEP algorithm using the subsume concept and the index strategy to further speed up the mining time and reduce the memory usage. Finally, we conduct an experiment to compare the mining time, memory usage and scalability of TEP, TEPUS and two state-of-the-art algorithms (VM and dVM) for mining top-rank-k EPs. Our performance studies show that TEPUS outperforms TEP, VM and dVM.

A Note on Correctly Gathering Results from JR's Concurrent Invocation Statement

JRs concurrent invocation statement (CoStmt, for short) provides a form of collective communication. The thread executing a CoStmt can gather results from each of many invocations. This paper presents a problem that arises in using Java List structures in gathering such results. It presents incorrect and correct attempts at solutions and discusses the tradeoffs, including performance, among the correct solutions.

Efficient High Utility Pattern Mining for Establishing Manufacturing Plans With Sliding Window Control

In industrial areas, understanding the preference of customers is one of the important considerations for establishing profitable product manufacturing plans. As one of the approaches in pattern mining, high utility pattern mining has been employed to find a set of products creating high profits by considering the purchase quantity and price of each product. In this regard, high utility pattern mining can be useful to establish profitable product manufacturing plans that allow a corporation to maximize its revenue. For establishing manufacturing plans, we also need to understand the recent preference of customers from stream data, which are continually generated without limitations. In this paper, we propose a novel algorithm and list structure for finding high utility patterns over data streams on the basis of a sliding window mode. Unlike existing algorithms, the proposed algorithm does not consume huge computational resources for verifying candidate patterns because it can avoid the generation of candidate patterns. Therefore, the algorithm efficiently works in complex dynamic systems. Experimental results obtained from various tests using real-world dataset show that the proposed algorithm outperforms state-of-the-art methods in terms of runtime, memory usage, and scalability.

Tina, an Interpreter for Interpreting Compilation Theory

The traditional C, C ++, Java compiler system are large and integrate a large number of libraries and frameworks, allowing developers to use conveniently, are not conducive to understand the compiler theory and technology. In this paper, aiming at the above problems, we research a simple structure of code compiler technology, developed a simple compiler system with common sense, making the reader better learn compiler theory and technology and increasing the interest of this course. Firstly, the main features and applications what Tina interpreter were discussed, and the analysis and discussion of the implementation process. The actual situation in the field of researching, proposed Tina compiler which is the design method of this solution. Secondly, from code structure and format of Tina, it analyzes the common lexical, grammatical problems. Research on the compiler technology, control structures and procedures, parsing judgment for detailed analysis. Compilation process is divided into lexical analysis, syntax analysis and code generation three processing stages,which let the Windows system as the development platform and let C language as development tools and use recursive technique and the data structure of lists and queue and stacks implementing the three modules of the specific design.

Student reading lists: evaluating the student experience at the University of Huddersfield

PurposeThe purpose of this paper is to evaluate the bespoke resource list management system (MyReading) at the University of Huddersfield.Design/methodology/approachAn online survey was designed to assess student use of MyReading and their views on potential improvements. The survey used closed questions designed to obtain quantitative data. Thematic analysis was used to analyse qualitative data obtained from open questions.FindingsThe paper supports findings of another recent study which found that reading lists are perceived as more important by students than by lecturers. A variety of positive and negative themes emerged which pointed to this conclusion. Positive themes were: the perceived helpfulness of reading lists, students’ view of MyReading as a starting point for independent further reading, ease of use of MyReading and the time saving afforded to students and the value students place on their reading lists as being “quality assured” by lecturers. Negative themes were: inconsistency in the length and structure of lists; concerns that some lists are not regularly updated; lack of awareness of functionality, revealing training needs for students and lecturers; and suggestions for future enhancements to MyReading. Another finding from the quantitative data is the clear link between low use of reading lists by students in certain schools and low use of other library resources.Practical implicationsThe research provides guidance to universities regarding future development of resource list management systems and promotional and training needs.Originality/valueThe study adds rich information to the existing body of qualitative research on students’ perceptions of their reading lists.

Mining of high average-utility itemsets using novel list structure and pruning strategy

A novel algorithm for efficiently mining high average-utility itemsets is presented in this paper. The algorithm utilizes list structures, which compactly capture all information needed to calculate the actual average-utilities of itemsets so as to mine all high average-utility itemsets without the generation of candidate itemsets. The algorithm thus does not require any scanning of a given transactional database after initial two scans for constructing list structures of itemsets with 1-lengths. The algorithm can generate all high average-utility itemsets through its depth-first search based mining process, which is conducted by recursively constructing list structures of itemsets with (k+1)-lengths from list structures of itemsets with k-lengths. In order to avoid the expansion of unpromising itemsets that cannot be expanded to high average-utility itemsets, a novel pruning technique using tight upper-bounds of itemsets’ average-utilities is designed and applied to the algorithm. Therefore, the runtime and memory efficiencies of the algorithm are able to be enhanced significantly because the search space of its mining process can be considerably reduced. Various experiments were performed by using four real datasets and two groups of synthetic datasets. Experimental results support that the proposed algorithm has runtime, memory, and scalability performances superior to those of existing algorithm.