Depth and regularity of tableau ideals

We compute the depth and regularity of ideals associated with arbitrary fillings of positive integers to a Young diagram, called the tableau ideals

VFFINDER: A Graph-based Approach for Automated Silent Vulnerability-Fix Identification

The increasing reliance of software projects on third-party libraries has raised concerns about the security of these libraries due to hidden vulnerabilities. Managing these vulnerabilities is challenging due to the time gap between fixes and public disclosures. Moreover, a significant portion of open-source projects silently fix vulnerabilities without disclosure, impacting vulnerability management. Existing tools like OWASP heavily rely on public disclosures, hindering their effectiveness in detecting unknown vulnerabilities. To tackle this problem, automated identification of vulnerability-fixing commits has emerged. However, identifying silent vulnerability fixes remains challenging. This paper presents VFFINDER, a novel graph-based approach for automated silent vulnerability fix identification. VFFINDER captures structural changes using Abstract Syntax Trees (ASTs) and represents them in annotated ASTs. VFFINDER distinguishes vulnerability-fixing commits from non-fixing ones using attention-based graph neural network models to extract structural features. We conducted experiments to evaluate VFFINDER on a dataset of 36K+ fixing and non-fixing commits in 507 real-world C/C++ projects. Our results show that VFFINDER significantly improves the state-of-the-art methods by 39-83% in Precision, 19-148% in Recall, and 30-109% in F1. Especially, VFFINDER speeds up the silent fix identification process by up to 47% with the same review effort of 5% compared to the existing approaches.Comment: Accepted by IEEE KSE 202

Role of Scientific Research for Lecturers of Current Universities

Scientific research for university lecturers plays an important role in training creative thinking ability, research capacity and scientific working style  for researchers. This contributes to clarifying some scientific issues and solving practical problems that arise in order to improve the quality of teaching. This article focuses on analyzing some issues about the role of scientific research for university lecturers today

High Performance Direction Finding Algorithm Based on Phase Locked Loop

Using Phase Locked Loop based single channel Direction Finding (DF) system to estimate the bearing angle or the coordinates of an incoming radio signal(s) has much more advantages than multiple receiver system does in many practical scenarios such as mobile communication. This method utilizes a bank of Phase Locked Loops (PLLs) to calculate the differential phase of signal received by an M-element uniform circular antenna array with a commutative switch followed by single channel Software Defined Radio (SDR) receiver. One important factor when using conventional phase locked loop is the requirement of the small convergence rate of the algorithm compared to the switching cycle. In order to achieve small convergence rate, we propose a method for DOA estimation with low computation complexity that improves significantly the performance of conventional PLLs system. An analysis of the challenges of computation complexity in this algorithm is presented. The simulation results for DOA estimation using the proposed structure with low complexity are shown to verify the performance of the system

Formal accounting harmonization - A new measurement scheme demonstrated by Vietnam’s data and International Financial Reporting Standards

We are living in a “flat world” of international integration and adaptive trends. This requires countries to integrate their own regulations to those from other countries. Accounting regulations are no exception. It is necessary to measure how much a nation’s accounting regulations are the same or different from those of another country or from International Financial Reporting Standards (IFRS). Extant literature reveals a rich discussion about this topic. Many measurement schemes have been initiated and employed. However, it is argued that data classification processes in those works contain some flaws. This paper contends the data specifically used to evaluate accounting measurement issues. The data will be divided into initial and subsequent recognition because such partition collectively affects the financial report figures. Therefore, the similarity of accounting regulations as a whole should be the multiplication of initial and subsequent recognition similar degree. To this extend, this work contributes to the research theme

NetFPGA Based OpenFlow Switch Extension for Energy Saving in Data Centers

The increasing demand for data centers in both scale and size has led to huge energy consumption. The cost and environmental impact of data centers increases due to large amounts of carbon emissions. One solution to this problem is to intelligently control the power consumption of switches used in data centers. This paper proposes an extension to OpenFlow switches to support different power saving modes. The extension includes defining new messages in the OpenFlow protocol stack and designing an OpenFlow Switch Controller (OSC) that is able to turn on/off switches and disable/enable ports. To prove the soundness of the proposed extension, the functions of an OSC has been integrated in a NetFPGA based OpenFlow switch used in the ECODANE framework. The results presented in this paper can also be used by the OpenFlow compliant switches manufacturer or by power aware research community

Capsule network with shortcut routing

This study introduces "shortcut routing," a novel routing mechanism in capsule networks that addresses computational inefficiencies by directly activating global capsules from local capsules, eliminating intermediate layers. An attention-based approach with fuzzy coefficients is also explored for improved efficiency. Experimental results on Mnist, smallnorb, and affNist datasets show comparable classification performance, achieving accuracies of 99.52%, 93.91%, and 89.02% respectively. The proposed fuzzy-based and attention-based routing methods significantly reduce the number of calculations by 1.42 and 2.5 times compared to EM routing, highlighting their computational advantages in capsule networks. These findings contribute to the advancement of efficient and accurate hierarchical pattern representation models.Comment: 8 pages, published at IEICE Transactions on Fundamentals of Electronics Communications and Computer Sciences E104.A(8

Characterization of pig farms in Hung Yen, Hai Duong and Bac Ninh provinces

peer reviewedIn order to characterization of pig farms in the Red River Delta, a study was conducted on 90 pig farms in Hung Yen, Hai Duong and Bac Ninh provinces from June to December 2006. Results show that most of the pig farms had been built for five years with a small size (0.5 hectare per farm). The invested capital was about 300-400 millions VND per farm. Four main sow groups used in the farms included crossbred exotic sows (51.1%), crossbred sow between local and exotic breeds (14.4%), purebred Landrace and Yorkshire breeds (15.6 and 18.9%, respectively). The boars were various (Duroc 30%, Yorkshire 21%, Landrace 13%, PiÐtrain × Duroc 36% and others). The pigs farms were faced with several difficulties such as limited land, lack of invested capital, uncontrolled quality of breeding pigs, high costs of feed, poor hygiene condition and diseases

A Combination of Artificial Neural Network and Artificial Immune System for Virus Detection

In this paper, we propose an Artificial Neural Immune Network (ANIN) for virus detection. ANIN is a combination of Artificial Neural Network (ANN) and Artificial Immune Network (AiNet). In ANIN, each ANN is considered as a detector. A pool of initial detectors then undergoes a mature process, called AiNet, to improve its recognizing ability. Thus, more than one ANN objects can cooperate to detect malicious code. The experimental results show that ANIN can achieve a detection rate of 87.98% on average with an acceptable false positive rate

Academic Anxiety of Vietnamese Secondary School Students as a Reason for Applying Online Learning

Academic anxiety is one of the major problems in student psychology across the world. It applies equally to students of all ages, from elementary school to college and university students. Research shows that learning online is an effective way to defuse feelings of academic anxiety. Elimination of anxiety is clearly visible regardless of age, gender, or prior online learning experience. The study aims to identify academic anxiety as one of the most important reasons for moving to online learning or blended learning in secondary school. The study investigated academic anxiety among secondary school students in Vietnam. After surveying 677 students in classroom learning, the results showed that 13.7% of secondary students suffered from frequent anxiety, and 3.0% of them suffered from very frequent anxiety. Lower anxiety was observed among students actively participated in-class activities, and students with excellent academic performance. These factors can be optimally enhanced through blended and online learning. There were no differences in academic anxiety among male and female students, urban and rural students. There was a moderate correlation between a student's anxiety level and pressure of the school, parental expectations, students' motivation for high performance, and especially, among students who have the melancholic temperament. And the influence of these negative factors can also be optimally reduced with the help of online learning. Regression model could provide useful suggestions for parents, teachers and students in reducing academic anxiety for students, including the use of full or blended online learning