Abstract

Software vulnerabilities resulting from coding weaknesses and poor development practices are common. Attackers can exploit these vulnerabilities and impact the security and privacy of end-users. Most end-user software is distributed as program binaries. Therefore, to increase trust in third-party software, researchers have built techniques and tools to detect and resolve different classes of coding weaknesses in binary software. Our work is motivated by the need to survey the state-of-the-art and understand the capabilities and challenges faced by binary-level techniques that were built to detect the most important coding weaknesses in software binaries. Therefore, in this paper, we first show the most critical coding weaknesses for compiled programming languages. We then survey, explore, and compare the static techniques that were developed to detect each such coding weakness in software binaries. Our other goal in this work is to discover and report the state of published open-source implementations of static binary-level security techniques. For the open-source frameworks that work as documented, we independently evaluate their effectiveness in detecting code vulnerabilities on a suite of program binaries. To our knowledge, this is the first work that surveys and independently evaluates the performance of state-of-the-art binary-level techniques to detect weaknesses in binary software.

Introduction

Technology and software have become integral to our daily lives. More software is now present in more systems, including many embedded devices, like refrigerators and microwave ovens, to cars and planes. Additionally, new software features continue to be added as the hardware, including the processor, memory, and storage, becomes faster, larger, and/or more capable. Thus, software programs also continue to grow in size and perhaps, complexity.

Given this growth in the amount of software in use, it is no surprise that the number of reported code vulnerabilities has been increasing in number and severity for many years [1]. At the same time, software vulnerabilities have been found to cause many disastrous real-world attacks [2], [3].

Software vulnerabilities are caused by weaknesses or flaws in the program code. These weaknesses may then be exploited to compromise the security or integrity of the system. Code in any language can be insecure when it is not developed with due care. However, some programming languages are designed with features that make them immune or more resistant to certain types of weaknesses. Such safer languages typically provide built-in mechanisms for memory management, input validation, type safety, and other security-related features. Languages like Rust and Go belong to this category of safe programming languages.

Alternatively, unsafe programming languages, like C and C++, are low-level languages with poor built-in memory, type, and thread safety. Code bugs and missing safety oversight for vulnerable code constructs are widespread in programs written using these languages [4]. In spite of these safety concerns and even though memory-safe language alternatives are available, C/C++ remains popular due to the large amount of existing legacy code, and low-level features of these languages that are desired by many performance and memory critical, embedded, and real-time systems. Consequently, C and C++ separately and consistently rank among the top five most popular programming languages according to the TIOBE index 1.

Conclusions

Our goal in this work was to comprehensively review and compare past research in static analysis based approaches to detect the most important CWE categories for program binaries. Another major goal was to evaluate the accuracy of open-source tools built to detect each studied program’s weaknesses. We made many significant, interesting, and novel discoveries and observations in this work. First, we found that we currently lack tools and techniques to accurately detect many important classes of errors in binary software. Second, we found that much research is not available in the open-source domain, and even the tools that exist are often not maintained and lack critical support. Third, many research works only evaluate their techniques on small benchmarks, and their results may not adequately represent performance in real-world applications. Fourth, many CWE detection techniques suffer from a high incidence of false positives and false negatives, underscoring the need for refinement and enhancement of existing techniques and tools. Thus, this work distinguishes itself as the first survey of binary-level CWE detection techniques, and the first independent assessment of binary-level open-source tools for identifying software weaknesses, offering valuable insights and setting the stage for further advancements in this critical field.

Abstract

Nowadays, Java programming is used in a variety of application systems as a highly portable object-oriented programming language. To assist its self-studies by novice students, we have developed the Java programming learning assistant system (JPLAS), and implemented the personal answer platform on Node.js. JPLAS offers several types of exercise problems at different learning levels, including the grammar-concept understanding problem (GUP), the value trace problem (VTP), the mistake correction problem (MCP), the element fill-in-blank problem (EFP), the code completion problem (CCP), and the phase fill-in-blank problem (PFP). Any answer is automatically marked by string matching with the correct one on the platform. In this paper, we present an implementation of the six problem types in a Java programming course in Okayama University. We generated 109 problem instances by following its curriculum and assigned them to 58 third-year students as homework before the final examination. Their solution results reveal the difficulty difference among the problem types and confirm the validity in the Java programming course.

Introduction

Java is a powerful, general-purpose object-oriented programming language. Java was created in 1995, and is running on more than three billion devices worldwide as one of the most popular programming languages. Nowadays, Java is used in a variety of application systems, such as web applications, mobile applications, desktop applications, games, IoT application systems, and cloud service systems, as a highly portable object-oriented programming language. Thus, many universities and professional schools are offering the courses to cultivate Java programming engineers.

To assist self-studies of Java programming by novice students, we have developed the Java programming learning assistant system (JPLAS) and implemented the personal answer platform on Node.js that will be distributed to students on Docker [1].

Evaluation

In this section, we present the implementation results of the 109 instances to 58 third-year students taking a Java programming course in Okayama University.

A. Solution Performances by Problem Types

First, we analyze differences of solution performances of the students by the six problem types. Table II shows the number of students who submitted answer files, the average correct answer rate, and the average number of answer submission times for each instance by them for each problem type. From this table, it can be observed that GUP, VTP, MCP, and EFP are easier for these novice students. The average correct answer rate and the average number of answer submission times are similar in them. CCP and PFP are harder for them, where both the correct rate and the number of submission times become worse. The average correct answer rate of two CCP instances at ID = 8 and 10, and one PFP instance at ID = 12 is less than 90%. According to Table I, they are on override, overload, structure, inheritance, abstract class, and interface, which can be difficult for novices to understand. The hint function should be implemented for them, which will be in future works.

Abstract

Nowadays many researchers are developing various distributed and decentralized frameworks for federated learning algorithms. However, development of such a framework targeting smart Internet of Things in edge systems is still an open challenge. In this paper, we present our solution to that challenge called Python Testbed for Federated Learning Algorithms. The solution is written in pure Python, and it supports both centralized and decentralized algorithms. The usage of the presented solution is both validated and illustrated by three simple algorithm examples.

Introduction

Federated learning was introduced by McMahan et al. [1] as a decentralized approach to model learning that leaves the training data distributed on the mobile devices and learns a shared model by aggregating locally computed updates. They presented FedAvg, a practical method for the federated learning of deep networks based on iterative model averaging, see Algorithm 1 FederatedAveraging in [1] on page 5. The main advantages of federated learning are: (i) it preserves local data privacy, (ii) it is robust to the unbalanced and nonindependent and identically distributed (non-IID) data distributions, and (iii) it reduces required communication rounds by 10–100x as compared to synchronized stochastic gradient descent (FedSgd).

McMahan’s seminal paper [1] inspired many researchers’ papers and in this limited space we mention just few of them. Immediately after [1], Bonawitz et al. [2] introduced an efficient secure aggregation protocol for federated learning, and Konecny et al. [3] presented algorithms for further decreasing communication costs. More recent papers are focused on data privacy [4, 5].

Conclusion

In this paper, we developed the federated learning framework targeting smart IoTs in edge systems called Python Testbed for Federated Learning Algorithms (PTB-FLA), with the primary intention to be used as a framework for developing FLAs on a single computer. The solution is written in pure Python, and it supports both centralized and decentralized algorithms. The PTB-FLA usage is both validated and illustrated by three simple algorithm examples.

The main PTB-FLA advantages are the following: (i) it keeps the application footprint small so to fit to smart IoTs and (ii) it keeps the installation as simple as possible (with no external dependencies).

Abstract

     Autonomous ships would require higher cyber-physical interaction in comparison with traditional shipping operations, thus increasing the vulnerabilities associated with cyber security. The increasing complexity surrounding the innate characteristics of the shipping industry makes it challenging to build a resilient framework for ensuring cyber security. This study proposes a multi-criteria decision-making (MCDM) framework for assessing cyber security risk in the autonomous shipping context. The research was validated through surveying subject matter experts, system designers and seafarers. Different types of equipment and systems are ranked based on their perceived vulnerability to cyber threats. Survey data from 28 subject matter experts are collected and analysed through the Bayesian best–worst method (BWM). At system level, the results indicate that navigational systems are the most vulnerable to potential cyber threats, while propulsion systems are the least vulnerable element in the context of future autonomous shipping operations. On a sub-system level, the three most vulnerable parts are Global Navigation Satellite System (GNSS), Electronic Chart Display and Information System (ECDIS) and the communication devices on shore control centres (SCC), while the least vulnerable parts are engine controls, SCC integration platforms and cargo handling at ports.

Introduction

     Merchant ships have been an integral part of international trade, carrying about 90% of the goods transported globally (OECD 2021). Since its inception, eforts have been made by the stakeholders to make this mode of transport safer, more efcient and more cost efective. The introduction of advanced technologies along with the skills development of the workforce over the years has reportedly increased workplace safety in the maritime domain (Chauvin et al. 2013; Allianz 2020).

     Consequently, modern technologies such as Automatic Identifcation Systems (AIS), Global Navigation Satellite Systems (GNSS) and Electronic Chart Display and Information Systems (ECDIS) have contributed to safer navigation. The increased reliability and efciency of these technologies has resulted in their broader adoption in the shipping industry such that large ships are now being efciently operated by fewer crews (Lee and Sanquist 1996). The notion that it is possible to maintain navigational safety in the future even without any crew onboard has led to the discussion towards the adoption of autonomous shipping technology. Wróbel et al. (2017) stated that the introduction of Maritime Autonomous Surface Ships (MASS) could further reduce the likelihood of navigational accidents, such as collisions and groundings, as human error played a role in the majority of navigational accidents. However, the probability and consequences of non-navigational accidents such as fre and structural failure are likely to increase if no crew members are onboard (Wróbel et al. 2017).

Conclusions

     This study has presented an integrated approach to elicit cyber-security vulnerabilities in the context of future autonomous shipping utilizing a MCDM method. Our results suggest that navigational systems are the most vulnerable to cyber-threats, whereas propulsion control systems are the least. This necessitates communication devices and protocols used for the navigation of autonomous ships be redundant and cyber-safe. Other emerging new systems in the context of autonomous ships, such as the shore control centre and associated equipment, although opaque for the time being, are potentially vulnerable to multidimensional cyber-threats as described in this study. The unpredictable nature of cyber-security threats, along with inherent repercussions, calls for a multifaceted approach to safeguard the future of shipping. A pre-emptive approach should include not only technical guidelines but also policy and industrial ones and best practices in risk management, and in the behavioural and cultural aspects of shipping. In addition, experts concur that only compliancebased actions would not be sufcient to safeguard against cyber threats. Rather, an all-out comprehensive approach, considering the above aspects, would ensure a safe and secure adoption of autonomous shipping.

Abstract

     Background and Context: Though still a nascent area of research, serious games have been presented as means of engaging students in computer programming and computational thinking due to their immersive and interactive nature. Existing research is limited in its ability to provide systems based on sound instructional design models, and only a few studies validate their design with statistical support.

     Objective: This study investigated the effects of a game which is based on experiential learning theory under framework of the fourcomponent instructional design model on undergraduate students’ learning performance in conceptual knowledge of object-oriented programming and computational thinking skills.

     Method: A pre-test and post-test quasi-experimental design was used to study the effects of the experiential serious games on conceptual knowledge of OOP and CT skills of 61 nonengineering students with and without prior programming knowledge

     Findings: The statistical analyses reveal that students with and without programming experience significantly improved their understanding of fundamental concepts of OOP. There were only weak correlations among students’ creative problem solving, attitudes towards digital game-based learning of programming, and learning.

     Implications: We provide several recommendations for researchers and practitioners for designing and developing an effective serious game to teach novice programmers computer programming.

Introduction

     Computer programming is one of most important skills of today’s world because modern societies rely on computer systems to drive industry, trade and nearly every aspect of human life. Additionally, computational thinking (CT) skills are the fundamental skills that lie at the bottom of computer programming. CT is a problem-solving approach in which solutions to problems are generated in a way that computers are able to perform (Wing, 2006). However, students have problems in understanding and analyzing a problem, building step-by-step algorithmic solution designs for problems (Guenaga et al., 2021; Xinogalos, 2016) and visualizing the programming concepts from a problem situation (AlSakkaf et al., 2019; Mladenovic et al., 2021) which result in demotivation. Hence, researchers adopted a digital game-based learning approach to help novice programmers overcome their learning problems and improve their performance on computer programming.

Results and analyses

Effects of a serious game on students’ conceptual knowledge of OOP and CT skills

     Conceptual knowledge of OOP and CT skills play an important role in understanding problems, designing and implementing solutions to problems in CS (Laakso et al., 2021; Liu et al., 2011; Wing, 2008). Therefore, the first two questions of the study focused on the effects of playing the developed game on students’ learning of conceptual knowledge of OOP and CT skills. The analyses of both groups’ data showed that both freshman and sophomore students significantly improved their conceptual knowledge of OOP and CT skills after playing the developed game. This result is consistent with the idea that serious games can be effective in fostering novice programmers’ programming knowledge (Abbasi et al., 2021; Guenaga et al., 2021; Livovsky & Poruban, 2014; Mathrani et al., 2016; Miljanovic & Bradbury, 2017; Muratet et al., 2011; O’Kelly & Gibson, 2006; Phelps et al., 2005; Sun et al., 2021).

Abstract

Study 1

Blockchain technology (BT) in the supply chain/logistics has drawn much attention. Several benefits associated with the technology include cost-saving, sustainability enhancement, and economic viability. However, there are limited BT performance measurement models. The topic of the BT performance in supply chain management (SCM) is scattered across multiple disciplines. This study identifies different dimensions of BT performance and supports SCM managers to understand the systematic and holistic assessment of BT performance.

Study 2

The specific features of blockchain provide promise in managing supply chain risks. Given the growing research scrutiny in blockchain-based supply risk management, the development of a reliable and valid instrument to measure supply chain risk management is imperative. Nonetheless, no systematic research has been done to develop such an instrument. This study employs a comprehensive and rigorous procedure to develop a multifaceted measurement scale through an empirical analysis. We defined and operationalized the concept of blockchain-based supply chain risk management followed by validation and item measurement development.

Study 3

The supply chain management field is experimenting with the integration of blockchain, a cutting-edge and highly disruptive technology. However, blockchain research in supply chain risk is still nascent, especially the relationship between blockchain adoption and its impact on both risk management performance and supply chain competency. We aim to investigate the potential influence of BT-based security management in mediating the effects of blockchain adoption on both risk management performance and firm performance. We plan to administer a survey to review the opinions and views of supply chain practitioners.

Abstract

Document Sections

I. Introduction (heading 1)

II. Basic Technology of Blockchain

III. Blockchain Technology Architecture

IV. The Applications of Blockchain

V. Challenges and Future Trends

VI. Discussion and Future Directions

Abstract:

Starting from the basic concept of blockchain technology, the paper introduces the architecture of blockchain technology and the typical application of blockchain technology, and finally summarizes the challenges faced by blockchain technology, so as to provide some reference for the technological path innovation and data value reconstruction of deep integration of blockchain technology and various fields.

I. INTRODUCTION (HEADING 1)

Blockchain Technology (BT) is a new decentralized infrastructure and distributed computing paradigm, which uses encrypted chained data blocks to verify and store data, uses distributed node consensus algorithm to generate and update data, and uses smart contract to program and operate data. Its outstanding feature is that it doesn’t rely on the third-party trusted organization to realize the value communication between nodes without trust relationship.

Blockchain is a novel type of distributed infrastructure and computing paradigm that integrates technologies including distributed data storage, peer-to-peer (P2P) transmission, consensus algorithm, and cryptography [1]. It uses block-chain data structures to store and verify data, uses distributed consensus algorithms to generate and update data, uses cryptography technologies to ensure the security of data transmission and access, and uses smart contracts composed of automated script codes to program and operate data [2]. Through the integration of multiple technologies, the cluster jointly maintains the security and operation of the blockchain network and builds trust with machines.

Blockchain has four major supporting technologies: distributed database, consensus algorithm, secure cryptographic protocol, and smart contract. Based on the underlying key technologies of the blockchain, it has the following characteristics [3]: (1) Decentralization. There is no centralized hardware or management organization in the blockchain network. The rights and obligations of all nodes are equal, and the data in the system is maintained together. The suspension of any node does not affect the overall operation of the system.

Abstract

Document Sections

I. Introduction

II. Objective

III. Proposed Idea

IV. Related Works

V. Levels of User Authentication and Securing User Data

VI. Architecture

VII. Methods to Secure the Web Application

VIII. Implementation

IX. Results

X. Novelty

XI. Conclusion

Abstract:

     Website security is a major concern for large organizations as well as individual developers, the rarer the technology used the harder it becomes to come up with secure practices for developing a website. Vulnerabilities that are not fixed during development, and are deployed as such become easy targets for hackers. This could cause the company or the individual to lose a lot of money. It is not just the developers who are affected, end users who end up on vulnerable websites may get exposed to XSS attack which could compromise their system or an unsecured configuration of database system could lead to a potential data leak and hence the password of every registered user on the website is compromised, users who use the same password on multiple websites are affected the most The motivation for this paper comes from the fact that there is an overwhelming number of vulnerabilities in any application under development and every developer, experienced or not needs a starting point to patch these vulnerabilities that might have occurred in their application, this research provides the most common vulnerabilities which should be taken care of in any application and thus provide the much-needed starting point for developers. The objective of this paper is to design and develop a secure web application according to Open Web Application Security Project (OWASP) guidelines.

I. INTRODUCTION

A Web application (Web app) is an application program that is stored on a remote server and delivered over the Internet through a browser interface. A web application generally consists of three main parts –

 Front – End,

 Back – End and

 Database.

For this paper, the front-end has been made using HTML with ejs, for back-end NodeJS has been used and for the database MySQL has been used.The front end is used to display the main content of the webpage, it is usually the only thing that the client sees once the site is visited. If the website is static then only the front end is required.HTML is used to form the layout of the whole page, CSS is used to give styling to the page, and JavaScript is used to give logic to the page. Websites having only the frontend are prone to Cross-Site Scripting (XSS) injection attacks.

RESULTS
A. SQL – NodeJS is a relatively new backend language therefore automated vulnerability scanners don’t work well with most of the NodeJS applications. Manual testing of vulnerabilities is one of the ways to validate the security of the application.

1- Introduction

2- Previous work

3- Data transcription and annotation

4- Detecting stuttering events

5- Features of the classifiers used to detect stuttering events

6- Automatic speech recognition system

7- Experiments

8- Conclusion

References

Abstract

Stuttering is a speech disorder that, if treated during childhood, may be prevented from persisting into adolescence. A clinician must first determine the severity of stuttering, assessing a child during a conversational or reading task, recording each instance of disfluency, either in real time, or after transcribing the recorded session and analysing the transcript. The current study evaluates the ability of two machine learning approaches, namely conditional random fields (CRF) and bi-directional long-short-term memory (BLSTM), to detect stuttering events in transcriptions of stuttering speech. The two approaches are compared for their performance both on ideal hand-transcribed data and also on the output of automatic speech recognition (ASR). We also study the effect of data augmentation to improve performance. A corpus of 35 speakers’ read speech (13K words) was supplemented with a corpus of 63 speakers’ spontaneous speech (11K words) and an artificially-generated corpus (50K words). Experimental results show that, without feature engineering, BLSTM classifiers outperform CRF classifiers by 33.6%. However, adding features to support the CRF classifier yields performance improvements of 45% and 18% over the CRF baseline and BLSTM results, respectively. Moreover, adding more data to train the CRF and BLSTM classifiers consistently improves the results.

Introduction

Stuttering, also known as stammering, is a speech communication disorder that can have severe social, educational and emotional maladjustment consequences, not only for the people who stutter but also for their families [1, 2]. It is presumed that early intervention is best to offset potential later impacts of having a stutter on one’s psycho-social and communication developments [3]. During the assessment phase, clinicians carefully measure the stuttering events to determine if the stuttering is normal disfluency, borderline stuttering or beginning stuttering [4]. There are several approaches to determine stuttering severity. The fluency of very young children is commonly assessed through a conversational task, whereas for children older than seven years, a reading task may be used [5, 6]. The clinician asks the child to read from a passage, and then records each instance of disfluency while the child is reading. Clearly, this process is extremely dependent on the clinician’s experience [7, 8, 9, 10].

1- Introduction

2- Literature and related work

3- Design and methodology

4- Results and discussion

5- Conclusion and future work

References

Abstract

Several non-monotonic formalisms exist in the field of Artificial Intelligence for reasoning under uncertainty. Many of these are deductive and knowledge-driven, and also employ procedural and semi-declarative techniques for inferential purposes. Nonetheless, limited work exist for the comparison across distinct techniques and in particular the examination of their inferential capacity. Thus, this paper focuses on a comparison of three knowledge-driven approaches employed for non-monotonic reasoning, namely expert systems, fuzzy reasoning and defeasible argumentation. A knowledge-representation and reasoning problem has been selected: modelling and assessing mental workload. This is an ill-defined construct, and its formalisation can be seen as a reasoning activity under uncertainty. An experimental work was performed by exploiting three deductive knowledge bases produced with the aid of experts in the field. These were coded into models by employing the selected techniques and were subsequently elicited with data gathered from humans. The inferences produced by these models were in turn analysed according to common metrics of evaluation in the field of mental workload, in specific validity and sensitivity. Findings suggest that the variance of the inferences of expert systems and fuzzy reasoning models was higher, highlighting poor stability. Contrarily, that of argument-based models was lower, showing a superior stability of its inferences across knowledge bases and under different system configurations. The originality of this research lies in the quantification of the impact of defeasible argumentation. It contributes to the field of logic and non-monotonic reasoning by situating defeasible argumentation among similar approaches of non-monotonic reasoning under uncertainty through a novel empirical comparison.

Introduction

Uncertainty associated to incomplete, imprecise or unreliable knowledge is inevitable in daily reasoning and in many real-world contexts. Within Artificial Intelligence (AI), many approaches have been proposed for the development of inferential models capable of addressing such uncertainty. Among them, nonmonotonic reasoning emerged from the area of logical AI as an alternative to deductive inferences in logical systems. These were perceived as inadequate for decision making in realistic situations (Bochman, 2007). Hence, reasoning is non-monotonic, or defeasible, when a conclusion can be withdrawn in the light of new information (Reiter, 1988; McCarthy, 1980; Kowalski & Sadri, 1991; Longo, 2015; Brewka, 1991). A number of approaches for dealing with quantitative reasoning under uncertainty exist (Parsons & Hunter, 1998), including computational argumentation (also referred to as defeasible argumentation) (Prakken & Vreeswijk, 2001), fuzzy reasoning (Zadeh et al., 1965) and expert systems (Durkin & Durkin, 1998). These approaches have led to the development of non-monotonic reasoning models based upon knowledge bases often provided by human experts. Intuitively, since these models have been developed with a human-in-the loop intervention, their reasoning processes and their inferences have an intrinsic higher degree of interpretability and transparency when com pared to data-driven approaches for inference.

1- Introduction

2- Model and principle

3- Results and discussion

4- Conclusions

References

Abstract

As an indirect imaging technique, computational ghost imaging (GI) obtains the object information by calculating the intensity correlation between a series of computer-generated matrices and the corresponding bucket signals, which thereby offers a potential application in optical encryption. Here, we propose a new steganography scheme, called ghost steganography, based on the principle of computational GI. In our ghost steganography scheme, the bucket intensity signals of a secret image are concealed into the ones of a non-secret image by applying a non-conspicuous number integration process. To further increase the security, we introduce RSA cryptography to encode the integrated bucket signals after the steganography process. Simulation and experiment results fully demonstrate the feasibility of our optical ghost cryptography and steganography scheme. Our work paves a way to the application of GI in steganography and also enriches the knowledge of symmetric and asymmetric optical cryptography.

Introduction

Ghost imaging (GI), also known as correlated imaging, is an indirect imaging modality which obtains the object information from the intensity fluctuation correlation of two beams. One beam, called object beam, going through the object, is measured by a bucket detector. The other beam, called reference beam, interacting without the object, is detected by a spatially resolved camera. GI was firstly achieved with entangled photon pairs experimentally in 1995 [1], and later extended into the classical region with various thermal light sources [2–11]. Different from the two-detector GI, Shapiro proposed the computational GI in 2008 [12], which generated the active illumination patterns by using the spatial light modulator instead of passive measurements of reference beam. Similar to the single-pixel camera technique [13], computational GI can recover the object image with only a single-pixel detector [14,15], which largely simplifies the experimental setup. Meanwhile, the correlation between the computer-generated random matrix and the object beam intensity of computational GI offered a potential application in optical encryption [16-30]. To increase the efficiency and security of optical encryption based on GI [16], different methods are proposed and developed, including gray-scale and color encryption [17], multiple-image encryptions [18,26,29], specific phase masks schemes [22,25,27,30], XOR operation scheme [24].

1- Introduction

2- The Principle of GA

3- The GALA software system

4- Conclusions

References

Abstract

This paper presents an Intelligent Information System for education. The system was created for teaching students to use genetic algorithm in application to optimization tasks. The system allows to quickly encode a solution of the problem and pick up most suitable configuration of genetic algorithm. The paper also demonstrates a specific example of usage of educational system to solve an optimization task. The paper contains a description of the educational system and its features, a description of its capabilities of working with genetic algorithm and its graphical interface.

Introduction

The use of information technologies (IT) in various spheres of human activity, the exponential growth of information volumes and the need to respond quickly in any situations necessitate the search for adequate ways to solve new and new emerging problems [1]. One of the most effective ways is the study, application, development and further improvement of algorithms and methods of machine computing. Finding the optimal solution is one of the problems that researchers face, both in the theoretical field and in solving engineering and production problems. Optimization problems imply the need to choose from the set of possible solutions the best from the point of view of certain criteria, satisfying given conditions and limitations.

1- Introduction

2- Related work

3- Materials and method

4- Experimental results

5- Discussion

6- Conclusion and future direction

References

Abstract

Epilepsy, a common neurological disorder, is generally detected by electroencephalogram (EEG) signals. Visual inspection and interpretation of EEGs is a slow, time consuming process that is vulnerable to error and subjective variability. Consequently, several efforts to develop automatic epileptic seizure detection and classification methods have been made. The present study proposes a novel computer aided diagnostic technique (CAD) based on the discrete wavelet transform (DWT) and arithmetic coding to differentiate epileptic seizure signals from normal (seizure-free) signals. The proposed CAD technique comprises three steps. The first step decomposes EEG signals into approximations and detail coefficients using DWT while discarding non-significant coefficients in view of threshold criteria; thus, limiting the number of significant wavelet coefficients. The second step converts significant wavelet coefficients to bit streams using arithmetic coding to compute the compression ratio. In the final step, the compression feature set is standardized, whereupon machine-learning classifiers detect seizure activity from seizure-free signals. We employed the widely used benchmark database from Bonn University to compare and validate the technique with results from prior approaches. The proposed method achieved a perfect classification performance (100% accuracy) for the detection of epileptic seizure activity from EEG data, using both linear and non-liner machine-learning classifiers. This CAD technique can thus be considered robust with an extraordinary detection capability that discriminates epileptic seizure activity from seizure-free and normal EEG activity with simple linear classifiers. The method has the potential for efficient application as an adjunct for the clinical diagnosis of epilepsy.

Introduction

Epilepsy is a common brain disorder that affects people of all ages. It is a chronic neurological disorder in which recurrent seizures occurs due to abnormal neuronal activities within the human brain and affects the sensorium, mood and/or movement of the human body [1]. World Health Organization statistics indicate that approximately 50 million people currently live with epilepsy worldwide and an estimated 2.4 million people are diagnosed with epilepsy each year [2]. The incidence of the malady is higher in developing countries;i.e., between 7 and 14 per 1000 people. Treatment mostly comprises antiepileptic drugs and/or surgery [3]. The electroencephalogram (EEG) commonly detects seizure activity as it reflects electrophysiological conditions of the brain at a given time [4] and is widely used for diagnostic due to its low cost. EEG signals, enhanced with physiological and pathological data, are employed to evaluate and assess the treatment and progress of epileptic patients. Typically, clinicians evaluate EEG signals for three types of activity: (i) normal EEG activity that records healthy subjects with eyes open or closed; (ii) inter-ictal/seizure-free EEG activity that may contain small spikes and/or subclinical seizures that occur between two clinical episodes in epileptic patients; (iii) and ictal EEG activity containing sudden spikes.