Set Of Rules In Order Research Articles

EVaDe: Efficient and Lightweight Mirai Variants Detection via Approximate Largest Submatrix Search

The Mirai botnet, notorious for launching significant Distributed Denial of Service (DDoS) attacks and crippling portions of internet services in late 2016, has emerged as a significant threat. Its threat is magnified by the open-source nature of the original Mirai code, which enables a propagation and evolution rate that surpasses traditional malware and frequently defies common sense. As the primary targets of Mirai attacks, Internet of Things (IoT) devices must promptly adapt to the evolving variations of the Mirai threat scenario. In practice, however, IoT devices are frequently constrained by insufficient security detection resources. Therefore, there is an urgent need for a lightweight framework capable of handling Mirai variants and dynamically updating its rule set in order to effectively counter the threat. In response to these challenges, we present Efficient and lightweight Mirai Variants Detection (EVaDe), a novel, lightweight framework for detecting Mirai. EVaDe unleashes the power of sample function mining to efficiently automate the generation of detection rules, requiring limited hardware resources while maintaining effectiveness against Mirai and its numerous variants. In addition, to improve the efficacy of rule generation, we propose a sophisticated algorithm designed to optimize the maximum submatrix problem, thereby facilitating the efficient and rapid extraction of malicious rules from the sample group. We validated the experiments on actual IoT devices with significantly compressed performance overheads. An average sample detection time of 5 ms to make sure the system can be deployed in real production. According to the result, the approach has an average detection rate of 95% for Mirai and its variants, which beats every other well-known piece of commercial antivirus software on the market by 3% to 56%.

Collaborative Development: How Linguists and Aviation SMEs can Best Serve the Aviation Language Community

For the purposes of safety in aviation and in accordance with the ICAO recommendations, all pilots and controllers must follow a prescribed set of rules in order to communicate effectively within the worldwide aviation community. Therefore, they must be trained in their use of aviation language. It seems, after research conducted in this context, that the training provided solely by SMEs does not suffice as they do not draw attention to proper articulation and pronunciation, nor drill grammar and vocabulary sufficiently. On the other hand, language instructors deliver on such issues on a regular basis, though, not being experts in aviation themselves, may leave out important elements of aeronautical communication. Hence, the only way to deliver the training is the collaborative development of both specialists. We suggest here one model of such cooperation. The generation of sample language and conceptual examples to place the use of this structured communication in the proper context, should be initiated by the SME. However, language instructors should be available to participate in such exercise building sessions to continuously keep the linguistic goal of the exercise in clear focus, as they would be best suited to that role. Using such a model to develop outcome-based language exercises embodies several advantages over developing study material produced by only one half of this suggested collaborative team. This paper will seek to elaborate on the different models which may be used in such endeavours as well as narrow the focus to the model suggested above, with a more detailed explanation of this model, to include sample language lessons (or lesson plans) generated in this way.

MODULAR DISTANCE LEARNING IN TEACHING SPORTS TO THE STUDENTS' PERFORMANCE OF CAVINTI INTEGRATED NATIONAL HIGH SCHOOL

Physical activity, such as participating in sports on a regular basis, can improve overall health and fitness while also lessening the risk of a variety of medical illnesses. Sports are physical activities that necessitate a set of rules in order to learn and excel in them. Distance education, on the other hand, is frequently utilized nowadays. It is capable of providing universal education and equipping students from all walks of life with new and qualified ideas and knowledge. Educators and students are trying to keep up with the frequent changes in the current situation. It has a significant impact not just on teachers' productivity, but also on students' social lives and learning. This research was conducted by the author in order to determine the level of Modular Distance Learning in teaching sports to the students' interest, willingness, type of sports, duration and appropriateness and the level of students' performance with regards to their Third Grading Period. The study also dealt with knowing the profile of the students with respect to their age, sex and gender. This study used a descriptive research method. A total of 54 (fifty-four) Grade 8 students from Cavinti Integrated National High School were chosen as student ? respondents using a purposive sampling technique. Data collected was through a self-made questionnaire employed as a research tool. The main study instrument used a Likert scale survey questionnaire. The data collected with this instrument was examined and interpreted using statistical tool. The results showed that the respondents are using the Self-Learning Module for a reason that: it is interesting to read, they enjoy reading at their own pace, the Self-Learning Module help them know more about the sport wanted, and were given flexible time to answer, it provides activity suited for the respondents. Self-Learning Module is used by the Grade 8 students of Cavinti Integrated National High School in studying sports in the subject of Physical Education. It contains sports concepts and activities that is aligned to the Most Essential Learning Competency. The use of Modular Distance Learning Modality helps the students in the learning process as it can be seen in their academic performance through their grades in Third Grading Period. About 40.74% of the total population gained grades of 85 to 89, then it was followed by the students who had grades of 90 to 100 which is 31.48% of the total population.

Filtered-ARN: Asymmetric objective measures applied to filter Association Rules Networks

In this paper, the Filtered-Association Rules Network (Filtered-ARN) is presented to structure, prune, and analyze a set of association rules in order to construct candidate hypotheses. The Filtered-ARN algorithm selects association rules with the use of asymmetric objective measures, Added Value and Gain then builds a network allowing more exploration information. The Filtered-ARN was validated using three datasets: Lenses, Hayes-roth, and Soybean Large, available online. We carried out a concept proof experiment using a real dataset with data on organic fertilization (Green Manure) for text the proposed method. The results were validated by comparing the Filtered-ARN with the conventional ARN and also comparing the results with the decision tree. The approach presented promising results, showing its ability to explain a set of objective items and the aid to build more consolidated hypotheses by guaranteeing statistical dependence with the use of objective measures.

Directive Mode for the Semi-Heterarchical Control Architecture of a Flexible Manufacturing system

Manufacturing industries are challenged of accomplishing effective operations regardless of perturbations during execution. For this reason, manufacturing control systems must be able to manage the scheduling of production towards the efficiency and reactivity of operations. For many approaches, the use of priority rules has proven to be a simple and efficient technique to address the efficiency and reactivity objectives, compared to other scheduling methods. Still, the use of this technique is focused on offline scheduling rather the online dynamic scheduling. For this reason, this paper proposed the use of priority rules for improving the reactivity features of a semi-heterarchical control system. Our proposal consists of a bi-level architecture where a high-level component proposes a set of priority rules that lower components follow. In a called directive mode, the high-level components optimize the set of priority rules in order that a more autonomous lower component chooses during execution. The directive mode is based on a set of simple and specific rules, as well as low complexity decision mechanisms pre-tested in a simulated environment. To show the contribution, we tested our approach in a simulated model of a real FMS in a simulation-based software. Experimental results demonstrate that the increase of autonomy given to the lower levels improves the efficiency after perturbations.

Urban vehicular traffic: Fitting the data with a hybrid stochastic model. Part II

In this second part of our research we used the models presented in Modeling vehicular traffic networks. Part I (Otero et al., 2018) to perform an analysis of the urban traffic as recorded by cameras distributed in a chosen sector of Tigre, a city localized in the province of Buenos Aires, Argentina. We found that the circulation of vehicles – the traffic dynamics –, along a whole day, can be described by a hybrid model that is an adapted blend of model 2, for an open linear system, with model 3, which is nonlinear, developed in Part I. The objectives of this work were, firstly, to verify whether the vehicular flux can be modeled as an n-step stochastic process for its evolution, n for the time. Secondly, to find out if the model, with its parameters fixed to describe the traffic of a single day, may adequately describe the traffic in other days. Thirdly, to propose changes in the already established set of the urban traffic rules in order to optimize the vehicular flow and to diminish the average time that a vehicle stays idle at the semaphores. We estimate that the goals were achieved satisfactorily within the margins of the experimental errors of the collected data.

The limits to personal self-categorization on key demographics

Any research survey is likely to apply some set of rules in order to classify respondents according to age, gender, social class, ethnicity, disability, and so on. But the rules governing how people are categorized may be less consistent than we suppose. In respect of gender and ethnicity, there is an unspoken assumption that the researcher should accept unquestioningly the category with which the data subject self-identifies. But in respect of social class, an equally important aspect of identity, respondents are content to have categories applied externally, for example, on the basis of their occupation. The article argues that rather than seeking to achieve a supposedly “correct” classification for each data subject, it might be better to apply at least two categorizations, one reflecting a person’s own self-identification, the other on how they are socially perceived. These different forms of identification are relevant to different types of research application.

An Analytical Interval Fuzzy Inference System for Risk Evaluation and Prioritization in Failure Mode and Effect Analysis

The fuzzy inference system (FIS) is useful for developing an improved Risk Priority Number (RPN) model for risk evaluation in failure mode and effect analysis (FMEA). A general FIS_RPN model considers three risk factors, i.e., severity, occurrence, and detection, as the inputs and produces an FIS_RPN score as the output. At present, there are two issues pertaining to practical implementation of classical FIS_RPN models as follows: 1) the fulfillment of the monotonicity property between the FIS_RPN score (output) and the risk factors (inputs); and 2) difficulty in obtaining a complete and monotone fuzzy rule base. The aim of this paper is to propose a new analytical interval FIS_RPN model to solve the aforementioned issues. Specifically, the incomplete and potentially nonmonotone fuzzy rules provided by FMEA users are transformed into a set of interval-valued fuzzy rules in order to produce an interval FIS_RPN model. The interval FIS_RPN model aggregates a set of risk ratings and produces a risk interval, which is useful for risk evaluation and prioritization. Properties of the proposed interval FIS_RPN model are analyzed mathematically. An FMEA procedure that incorporates the proposed interval FIS_RPN model is devised. A case study with real information from a semiconductor company is conducted to evaluate the usefulness of the proposed model. The experimental results indicate that the interval FIS_RPN model is able to appropriately rank the failure modes, even when the fuzzy rules provided by FMEA users are incomplete and nonmonotone.

Step-Detection and Adaptive Step-Length Estimation for Pedestrian Dead-Reckoning at Various Walking Speeds Using a Smartphone.

We propose a walking distance estimation method based on an adaptive step-length estimator at various walking speeds using a smartphone. First, we apply a fast Fourier transform (FFT)-based smoother on the acceleration data collected by the smartphone to remove the interference signals. Then, we analyze these data using a set of step-detection rules in order to detect walking steps. Using an adaptive estimator, which is based on a model of average step speed, we accurately obtain the walking step length. To evaluate the accuracy of the proposed method, we examine the distance estimation for four different distances and three speed levels. The experimental results show that the proposed method significantly outperforms conventional estimation methods in terms of accuracy.

A framework for annotating CSV-like data

In this paper, we propose a simple and expressive framework for adding metadata to CSV documents and their noisy variants. The framework is based on annotating parts of the document that can be later used to read, query, or exchange the data. The core of our framework is a language based on extended regular expressions that are used for selecting data. These expressions are then combined using a set of rules in order to annotate the data. We study the computational complexity of implementing our framework and present an efficient evaluation algorithm that runs in time proportional to its output and linear in its input. As a proof of concept, we test an implementation of our framework against a large number of real world datasets and show that it can be efficiently used in practice.

Ontology based vertical web service composition

Recently, the composition of Web services has become an arduous task for several essential reasons. The task of automatically composing Web services involves two main composition processes, Vertical and Horizontal composition. Vertical composition consists of defining an appropriate combination of simple processes to perform a composition task. Horizontal composition process consists of determining the most appropriate Web service, from among a set of functionally equivalent ones for each component process. Several recent research efforts have dealt with the vertical Web service composition problem. Nevertheless, these efforts present some limitations and require more investigations. In an attempt to facilitate and streamline the Vertical composition process, the proposed approach OVWSC for Ontology based Web Service Composition includes two main steps. The first is a filtering process of the domain service ontology aiming at reducing the Web services research's space. The second step is a generation process of an abstract workflow. Our goal is i to propose a semantic composition approach based on an ontology called Domain services ontology; ii to suggest a set of novel rules in order to automatically select candidates abstract web services for the composition process; and iii to automatically generate an abstract workflow based on concepts and semantic links transformation.

Procedural Generation of Traversable Buildings Outlined by Arbitrary Convex Shapes

Abstract Virtual reconstructions are commonly used in archaeology to represent cultural heritage monuments that had been lost or damaged by natural causes. Traditionally, these reconstructions require a huge number of human resources and large ranges of time, resulting in high costs of production. To tackle with this issue, many researchers developed semi-automatic techniques to produce virtual models expeditiously. These procedural techniques provide different ways of represent buildings, including interiors and outer facades, in an archaeological or modern context. However, the existing techniques focusing building interiors only support the production of virtual models composed mainly by regular shapes such as rectangles. In this paper it will be presented the first steps of a novel methodology to provide a solution for the generation of building interiors constrained by arbitrary convex shapes. This methodology uses a specific ontology with a set of rules in order to regulate the generation process. The sequence of steps includes the room placement and area definition, section cuts and area readjustments, room linking and finally the extrusions and roof placement to deliver the final 3D model.

CBC: An associative classifier with a small number of rules

Associative classifiers have been proposed to achieve an accurate model with each individual rule being interpretable. However, existing associative classifiers often consist of a large number of rules and, thus, can be difficult to interpret. We show that associative classifiers consisting of an ordered rule set can be represented as a tree model. From this view, it is clear that these classifiers are restricted in that at least one child node of a non-leaf node is never split. We propose a new tree model, i.e., condition-based tree (CBT), to relax the restriction. Furthermore, we also propose an algorithm to transform a CBT to an ordered rule set with concise rule conditions. This ordered rule set is referred to as a condition-based classifier (CBC). Thus, the interpretability of an associative classifier is maintained, but more expressive models are possible. The rule transformation algorithm can be also applied to regular binary decision trees to extract an ordered set of rules with simple rule conditions. Feature selection is applied to a binary representation of conditions to simplify/improve the models further. Experimental studies show that CBC has competitive accuracy performance, and has a significantly smaller number of rules (median of 10 rules per data set) than well-known associative classifiers such as CBA (median of 47) and GARC (median of 21). CBC with feature selection has even a smaller number of rules.

Waterwall: a cooperative, distributed firewall for wireless mesh networks

Firewalls are network devices dedicated to analyzing and filtering the traffic in order to separate network segments with different levels of trust. Generally, they are placed on the network perimeter and are used to separate the intranet from the Internet. Firewalls are used to forbid some protocols, to shape the bandwidth resources, and to perform deep packet inspection in order to spot malicious or unauthorized contents passing through the network. In a wireless multihop network, the concept of perimeter is hard to identify and the firewall function must be implemented on every node together with routing. But when the network size grows, the rule-set used to configure the firewall may grow accordingly and introduce latencies and instabilities for the low-power mesh nodes. We propose a novel concept of firewall in which every node filters the traffic only with a portion of the whole rule-set in order to reduce its computational burden. Even if at each hop we commit some errors, we show that the filtering efficiency measured for the whole network can achieve the desired precision, with a positive effect on the available network resources. This approach is different from the protection of a space behind a wall: we use the term waterwall to indicate a distributed and homogeneous filtering function spread among all the nodes in the network.

Pesquisas sobre a lei Sarbanes-Oxley: uma análise dos journals em língua inglesa

The credibility crisis in the U.S. capital market arising from various accounting scandals resulted in the publication of the Sarbanes-Oxley Act (SOX) in July 2002, focused on defining a set of rules in order to certify the accuracy of accounting information. Since SOX is directly reflected in financial statements, it should be took as a subject of investigation by various researchers. Thus, the purpose of this paper is to examine the SOX studies published in accounting journals contained in the Institute for Scientific Information (ISI) database on the list of journals with impact factor. Was selected eight accounting journals listed in ISI database on the list of journals with impact factor. The search included the period from 2002 to 2010 and reached a total of 23 papers in five journals. The research found that there is a concentration of publications related to SOX in the Journal of Accounting Research, Journal of Accounting & Economics and The Accounting Review. We found some homogeneity in the publications regardind the period and a group of six authors stood out from a total of 55 authors. From the 35 universities linked to the papers, 7 institutions deserve special mention in relation to the number of published researches. Internal control has been the most mentioned topic in the papers, including perspectives relating to internal control weaknesses, cost of equity, stock prices, quality of accruals and qualification and turnover of Chief Financial Officers.

Risk Awareness and Transparency

Since 1995, the Association of the Scientific Medical Societies (Arbeitsgemeinschaft der Wissenschaftlichen Medizinischen Fachgesellschaften, AWMF) maintains an open access, quality assured guideline register currently containing almost 700 guidelines and expert recommendations developed by the 168 AWMF`s member societies (www.awmf.org). The AWMF established a set of rules in order to support the development of high-quality guidelines by its member societies and to assure the overall quality of the register (1). The set of rules is monitored and amended continually. In 2010, a rule addressing the declaration and management of conflicts of interest was introduced. The analysis reported by Langer et al. provides initial insights into the status quo after the introduction of this rule (2). Therefore we consider it a welcome contribution towards quality management. Remarkably, one year after the introduction of the new rule, the declaration of financial and academic interests can be regarded as an established standard—in contrast to the situation reported internationally (3). In order to prevent misunderstandings arising from the conclusions reached by Langer et al., the following requires clarification: Internationally, conflicts of interest are defined as circumstances that lead to a relevant risk of affecting professional judgment (4). The fact that conflicts of interests exist does, however, not allow a legitimate conclusion that individual authors or guideline contents are biased in any way. Since guidelines are collated by groups, rather than by individuals, biases for the group as a whole need to be assessed. This risk is counterbalanced in guidelines by protective factors: a systematic evidence base (scientific basis), interdisciplinary composition of the guideline group (pluralism of interests), structured consensus formation (avoiding undue influence by individual interests). Currently, no gold standard exists for the management of conflicts of interest, and neither does robust evidence about the effects of using or not using regulations (3, 4). The AWMF member societies have taken on this scientific challenge and are testing different approaches to reduce the risk of bias originating from conflicts of interest on an experimental basis in current guideline projects. These include efforts to estimate and differentiate the value of conflicts of interest, the corresponding restriction of the influence exerted by individuals, delegation of evidence reports to methodology experts, and peer review or public consultation. Creating empirical proofs—in the sense of evidence based medicine—is the prerequisite for the development of a standard, as demanded by Langer et al..

An Algorithmic Approach to Inferring Cross-Ontology Links while Mapping Anatomical Ontologies

Automated and semi-automated mapping and the subsequently merging of two (or more) anatomical ontologies can be achieved by (at least) two direct procedures. The first concerns syntactic matching between the terms of the two ontologies; in this paper, we call this direct matching (DM). It relies on identities between the terms of the two input ontologies in order to establish cross-ontology links between them. The second involves consulting one or more external knowledge sources and utilizing the information available in them, thus providing additional information as to how terms (concepts) from the two input ontologies are related/linked to each other. Each of the two ontologies is aligned to an external knowledge source and links representing synonymy, is-a parent-child, and part-of parent-child relations, are drawn between the ontology and the knowledge source. These links are then run through a set of simple logical rules in order to come up with cross-ontology links between the two input ontologies. This method is known as semantic matching. It proves useful and reasonably accurate; in this paper, we call it the source matching predictions (SMP) procedure.Not all cross-ontology links that semantically (i.e., from a biological/anatomical standpoint) exist between the two input ontologies will be discovered by either DM or SMP. To improve the discovery of cross-ontology links we propose a novel algorithmic procedure which involves a probability-like scoring scheme. This procedure is called the child matching predictions (CMP) procedure. Describing the DM, SMP, CMP procedures, and particularly the CMP procedure in formal terms is the main goal of this paper.

Impact of the sample matrix composition on the signal enhancement in the capillary electrophoretic separation of poliovirus samples

The development of capillary electrophoretic applications aiming to provide reliable stability assessment of viral suspensions, to detect subviral particles from cell extracts or to study the interactions between virus particles and various biomolecules, cannot be done without a thorough understanding of the sample matrix contribution to the observed electrophoretic behaviour. The present study thoroughly investigates the effect of the sample matrix on the electrophoretic behaviour of poliovirus injected as sample plugs of 1%, 5% and 12% effective capillary length. The effect of the sample matrix for three different poliovirus batches was evaluated. Additionally, simulated samples, obtained from concentrated poliovirus suspensions of high purity and diluted with commonly used lab buffers in order to obtain samples with either high or low conductivities, were also investigated. The goal of the study was to obtain a better understanding of the effect of the sample matrix on the signal enhancement, in order to define a general approach allowing a repeatable capillary electrophoretic (CE) separation of poliovirus from complex samples. This study clearly demonstrates that the sample matrix has an important influence on the sensitivity of the CE poliovirus separations. Translation of these observations into routine practice involves several compromises and a set of rules in order to reduce day-to-day variation and to maximize sensitivity.

RENAR

Named entity recognition has served many natural language processing tasks such as information retrieval, machine translation, and question answering systems. Many researchers have addressed the name identification issue in a variety of languages and recently some research efforts have started to focus on named entity recognition for the Arabic language. We present a working Arabic information extraction (IE) system that is used to analyze large volumes of news texts every day to extract the named entity (NE) types person, organization, location, date, and number, as well as quotations (direct reported speech) by and about people. The named entity recognition (NER) system was not developed for Arabic, but instead a multilingual NER system was adapted to also cover Arabic. The Semitic language Arabic substantially differs from the Indo-European and Finno-Ugric languages currently covered. This article thus describes what Arabic language-specific resources had to be developed and what changes needed to be made to the rule set in order to be applicable to the Arabic language. The achieved evaluation results are generally satisfactory, but could be improved for certain entity types.

A hybrid computer simulation-artificial neural network algorithm for optimisation of dispatching rule selection in stochastic job shop scheduling problems

Industrial systems are constantly subject to random events with inevitable uncertainties in production factors, especially in processing times. Due to this stochastic nature, selecting appropriate dispatching rules has become a major issue in practical problems. However, previous research implies that using one dispatching rule does not necessarily yield an optimal schedule. Therefore, a new algorithm is proposed based on computer simulation and artificial neural networks (ANNs) to select the optimal dispatching rule for each machine from a set of rules in order to minimise the makespan in stochastic job shop scheduling problems (SJSSPs). The algorithm contributes to the previous work on job shop scheduling in three significant ways: (1) to the best of our knowledge it is the first time that an approach based on computer simulation and ANNs is proposed to select dispatching rules; (2) non-identical dispatching rules are considered for machines under stochastic environment; and (3) the algorithm is capable of finding the optimal solution of SJSSPs since it evaluates all possible solutions. The performance of the proposed algorithm is compared with computer simulation methods by replicating comprehensive simulation experiments. Extensive computational results for job shops with five and six machines indicate the superiority of the new algorithm compared to previous studies in the literature.