Reviewerly/exHarmony huggingface dataset:
Implementation and Evaluation of Communication-Hiding Method by System Call Proxy

Essential services, such as security software or logging software, are considered important because of an increase in attacks on computers. These essential services are provided by processes that sometimes involve file manipulation and communication. Moreover, these essential services can be a target of attacks and become disabled, as they can be an obstacle to attackers. Attackers can speculate essential services by monitoring the behavior of the processes. To avoid such attacks on essential services, methods for hiding their behavior are proposed. The methods use a virtual machine (VM) monitor to make it difficult for attackers to identify essential services by hiding process information and file manipulation. However, the communication information remains visible to attackers. To address this problem, this study proposes a method for hiding the communication of essential services by using a system call proxy. We assume that a process providing essential services (essential process) runs on a protection target VM and a proxy process runs on a proxy VM. In the proposed method, the system calls in the communication invoked by the essential process are executed by the proxy process. The system calls invoked by the proxy process are not executed on the protection target VM; therefore, attackers cannot identify the communication of essential services by monitoring their communication. This paper presents the design, implementation, and evaluation of the proposed method.
Nudge-Based Hybrid Intelligent System for Influencing Buying Decision

The novel idea of nudging has become increasingly popular in academics and business practices, and nudge incorporates the process of positive reinforcement and indirect suggestions as ways to influence the behaviour and decision-making of an individual or a group. In this paper, we propose a conceptual hybrid intelligent system which leverages both case-based reasoning (CBR) and expert system (ES) for product recommendations. The proposed concept will not only efficiently filter the best product, i.e. smartphone, for the consumer but will also take care of the latent factors involved in generating recommendation by its powerful double layered filtering technique.
Distribution of RSA Number’s Divisor on T3 Tree

The article investigates the detail distribution of RSA modulus' small divisor in the T3 tree in terms of the divisor-ratio.It proves that, the distribution of the small divisor in T3 tree is completely determined by the divisor-ratio and the parity of the level where the RSA modulus lies, the small divisor of a RSA modulus lying on an even level lies on the same level as where the square root of the modulus is clamped, whereas that of a modulus on an odd level possibly lies on the same level or the higher adjacent level of the square root.Through mathematical induction, it shows that a smaller divisor-ratio results in a closer position of the small divisor to the square root of a RSA modulus.
Methods of Determining Errors in Open-Ended Text Questions

Open-ended text questions allow for better assessment students’ knowledge, but analyzing the answer, determining it’s correctness and, especially, providing detailed and meaningful feedback about errors to a student are more difficult tasks than for closed-ended and numerical questions.The analysis of the answer in the form of freely written text can be performed on three different levels. Character-level analysis seeks error in characters’ placement inside a word or a token; it’s mostly used for detecting and correcting typos, allowing to discern typos from actual errors. Word-level (or token-level) analysis concerns word placement in a sentence or phrase, allowing to find missing, extraneous and misplaced words. Analysis on the semantic level tries to formally capture the meaning of the answer and compare it with the meaning of the correct answer provided by question creator in natural-language or formal form. Some tools analyze answers on several levels.Long open-ended text questions typically allow variability of correct answers, which complicates the error search. Different ways of specifying patterns for variability and the possibility of introducing error-determining methods to questions with patterned answers are discussed.
Problems and Features of Pedagogical Personnel Training in the Field of Educational Robotics

The article is devoted to the existing problems arising in the formation of teachers’ readiness for professional activities in the fi eld of educational robotics. The necessity of systemic training of pedagogical personnel meeting the demands of modern society and capable of teaching the young generation of the country at various educational levels, thereby increasing the level of engineering education, is established. The teacher of this direction should have knowledge from various scientifi c fi elds: computer science and information technology, physics, microelectronics and artifi cial intelligence. The article describes the experience of the Institute of Information Technologies and Telecommunications of the North-Caucasian Federal University in teaching children, schoolchildren and students of educational robotics, as well as the training of teachers and advanced training of existing teachers in this field. A promising area of research is the study of the organizational and methodological conditions for the formation of teacher competence and the development of teacher training technologies in the fi eld of educational robotics.
ARena: Improving the Construction Process of Line-Follower Robot Arenas Through Projection Mapping

With the increasing number of line-follower robot championships, the problem of arenas production time arises. This study is based on a recurring problem detected by the Organization of the Brazilian Robotics Olympiad (OBR) regarding the necessity of producing arenas in a short time. Therefore, a solution was elaborated, called ARena, that uses the concept of Projection Mapping to aid in the standardized production of tracks. The result obtained from user tests was favorable to the proposal, however, improvements in the tool interface are required.
Theory and Practice of Using Robotics in Educational Process

This article discusses the problem of using robotics in higher education, highlights the theoretical and practical aspects of the problem. The article describes the current trend of the inclusion of robotics in the educational process as one of the effective tools for the formation of students’ professional competencies, psychological readiness to implement educational programs at any level in the modern post-industrial world. The authors present psychological and pedagogical research, in which the organization of project work with fourth-grade and fifth-grade students was studied using a robot-assistant. Practical research was devoted to the perception of anthropomorphic robots by students. They studied a number of disciplines in direct contact with a static robot. The authors analyzed the possibility of using robotics in educational process while preparation of students majoring both in engineering and pedagogy. The results of this research support the idea that robots can be assistants for a teacher and motivators for students. Interaction between robot and students enhances their general emotional background, activates cognitive processes and learning in general.
Question Answer Based Chart Summarization

To summarize documents worths to summationof the main points. A summarization is this kind of summing up. Elementary school book reports are big on summarization. To provide a comprehensible declaration of the significant points is nothing but summarization. In current years, natural language processing (NLP) has stimulated to statistica1l base. Many tribulations in NLP, e.g., parsing, word sense disambiguation, and involuntary paraphrasing. In recent times, robust graph-based methods for NLP is also a lot of scope, e.g., in clustering of words and attachments of prepositional phrase. In proposed paper, we will take in account of graph-based summarization techniques, approaches used for that etc. We will talk about how arbitrary traversing on images of graphs can help in making of question answer based summarization. In current exploration work, question answer based graph summarization system for Bar Graph is shown. The extraction procedure is completely computerized using image processing and text recognition methods. The extracted information can be used to improve the indexing component for bar charts and get better exploration results. After generating questions, questions are rank the according to frequency or priority and answer of the ranked question is summary of given input.
Learning to select mates in artificial life

Artificial life (A-life) simulations present a natural way to study interesting phenomena emerging in a population of evolving agents. In this paper, we investigate whether allowing A-life agents to select mates can extend the lifetime of a population. In our approach, each agent evaluates potential mates via a preference function. The role of this function is to map information about an agent and its candidate mate to a scalar preference for deciding whether or not to form an offspring. We encode the parameters of the preference function genetically within each agent, thus allowing such preferences to be agent-specific as well as evolving over time. We evaluate this approach in a simple predator-prey A-life environment and demonstrate that the ability to evolve a per-agent mate-selection preference function indeed significantly increases the extinction time of the population. Additionally an inspection of the evolved preference function parameters shows that agents evolve to favor mates who have survival traits.
Sketch-based Registration of 3D Cine MRI to 4D flow MRI

Cardiac 4D Flow magnetic resonance imaging (4D Flow MRI) is a recent powerful technology that uniquely enables in-vivo acquisition of time-varying volumetric blood flow velocity field information in the three spatial dimensions over the cardiac cycle. Hence, 4D Flow MRI has emerged as an important medical diagnostic tool for evaluation of blood flow alteration in the heart chambers and great vessels. A critical requirement for accurate quantification and visualization of blood flow within the different heart chambers (e.g. the left ventricle (LV)) is the accurate anatomical context of cardiac chambers, which is missing in the 4D Flow MRI data. To tackle this problem, recent studies have proposed fusing the 4D Flow data with a complementary anatomical MRI scan (short axis 3D (multiple 2D slices) cine SSFP) through registration. However, since image registration is a non-linear optimization problem, the registration is slow and may not be accurate (e.g. the left ventricle can be incorrectly aligned to the right ventricle). To improve the registration performance and accuracy, localization techniques can be used. In this paper, we propose two sketch-based methods for effective localization of 4D Flow MRI to 3D cine MRI registration. We evaluate these methods and compare them with other localization methods.
Particle Swarm Algorithm

Optimization is discovering an alternative with the most cost-effective or highest-achievable performance under the given constraints, by maximizing desired factors and minimizing undesired ones. Portfolio optimization in finance depends on selecting assets from an opportunity set which yields highest expected return on each level of portfolio risk. Optimization algorithms based on natural events are called heuristic algorithms. The particle swarm optimization (PSO) is a population-based heuristic optimization technique. The technique is inspired by the ability of animals such as birds and fish to adapt to their environment by applying a “sharing of knowledge” approach, to find rich food sources and to avoid hunting. This chapter focuses on portfolio selection problems and shows how to manage financial portfolios using a particle swarm optimization (PSO) technique which is a heuristic algorithm. In order to better understand the subject, the technique has been evaluated in Istanbul Stock Exchange for three transportation sector stocks.
Technology-Enhanced Learning: Correlates of Acceptance of Assistive Technology in Collaborative Working Setting

Considering stagnant interest in science, technology, engineering and math (STEM), on the one side, and an increasing dropout rates in computer science at different levels of the university education due to difficult and complex learning contents, on the other side, appropriate technology solutions which support learners are very promising. In this study we examine a digital learning assistant in the form of tangible objects interacting with a multi-touch tabletop. This learning tool is meant to support students in the way they learn complex material. In an experimental setting, participants working in groups had the task to acquire novel learning content (i.e., regular expressions) and, using the assistive technology, to assign the correct expressions to their predefined terms. Results revealed that the users’ psychological factors and performance factors which result from the interaction with the learning tool are significantly connected with the correlates of acceptance and affect, therefore, the later adoption of such technology. The positive overall assessments show a high attachment for, and willingness to, use such technology. However, user diversity has to be considered in the design and further development. Knowledge gained discloses expedient hints for technology-enhanced learning that can support and accompany the education at different levels, i.e., in schools, vocational training, and university education.
Smartphone-Based Drivers Context Recognition

Various embedded sensors such as accelerometer and gyroscope have opened a new horizon in the scientific studies. One of the most prevailing areas of research is context recognition which can be adopted for smartphone-based parking, road condition detection and sports. To the best of our knowledge, the existing context recognition research covers human's basic contexts such as walking, jogging and are position dependent that require tightening sensors in fixed position of the body. Furthermore, none of the work has seen to be more specific to detect the contexts of driver. Therefore, to be more specific, in this study, we have constructed a position-independent approach to recognize driver's contexts that occurs while a driver parks car or leaves from parking place. The support vector machine, random forest and decision tree are employed and the accuracies of 83.38, 93.71 and 98.41% are obtained, respectively.
Multi-level Container Checkpoint Performance Optimization Strategy in SDDC

In order to improve the performance of large-scale cloud applications deployed on containers in Software Defined Data Centers (SDDC), this paper proposes a multi-level container checkpoint performance optimization strategy. Traditional checkpoint method exist the problem of frequent copying and transmission of dirty pages,so a based on the delayed copy incremental checkpoint algorithm predicted by dirty page degrees is proposed, reducing the size of checkpoint files from the source and solving the problem of long downtime. The goal of performance optimization is to reduce application downtime and recovery time. The effectiveness of the strategy is verified by plenty of experiments. The given experiments show that the strategy can reduce the downtime by 21%, the recovery time by 18% and the checkpoint files size by 24%.
Analysis of Network Threats Based on Data from Server-Traps

The paper presents the results of data analysis from a geographically distributed network of honeypot servers. A network of 4 honeypot servers was deployed more than two years ago. Analysis of the collected data has allowed us to build a network intrusion model. This model includes blacklists of attacking addresses for various internet services, statistics on the intensity of everyday intrusion attempts and an analysis of attacking addresses as belonging to providers of various countries. Special mention should be made of the ranked lists of major vulnerabilities that attackers use to attempt to break content management systems.
An OverView of Different Image Algorithms and Filtering Techniques

Image filtering is a very important part of image processing techniques. This is the most popular technique which comes under image processing. It is used to get maximum information from original picture. This is becoming popular in the market as most of the people are using different filters in their cell phones. Actually, the basic idea behind the filters is to recognize the edges and reduce the difference between the pixels of edges. Different types of algorithms are there to do such type of functions.
Fast robust matching algorithm based on BRISK and GMS

Abstract In the image stitching technology, traditional feature extraction algorithms have uneven feature points distribution, many redundant features, time-consuming feature points precision matching and low image registration accuracy. In view of these problems, this paper proposes an improved image registration algorithm based on BRISK and GMS. In this method, the image is first divided into meshes and BRISK algorithm is used for extracting image features, then the Brute Force matching algorithm is used for rough image matching. Finally, the mesh motion estimation method is used for feature quantity statistics, and error matching is removed to obtain a set of fine matching feature points for image registration. This paper verified the robustness of the improved algorithm by comparing it with other methods in the Mikolajczyk data set. The experimental results show that this algorithm achieves higher matching accuracy on the basis of maintaining speed compared with the original algorithm, and the average accuracy is improved by 8.02%. It has better performance than the traditional algorithm, which can be used for occasions with higher requirements on registration accuracy and real-time performance.
Lessons from Building Diverse Adaptive Instructional Systems (AIS)

This paper presents lessons learned from building a wide range of Adaptive Instructional Systems (AISs), ultimately bearing on the question of how to characterize the space of potential AISs to advance the cause of standardization and reuse. The AISs we consider support coached practice of complex decision-making skills—e.g., military tactical decision-making, situation assessment, and systems troubleshooting and management. We illustrate forces that affect system design and dimensions along which systems then vary. The relevant forces derive from the AIS’s area of application, the project structure within which it is built, and the customer’s priorities. Factors to consider include (1) The extent to which the target domain is well-defined versus ill-defined; (2) The degree of fidelity required, preferred, and/or available for an exercise simulation environment; (3) The intended roles of automated and human instructors in instructional delivery; and (4) Imperatives for short-term and/or long-term cost containment. The primary dimensions of AIS design we consider in this paper include (1) Exercise Environment; (2) Knowledge Models; (3) Tutor Adaptations; and (4) Supporting Tools. Each of these is further broken down into a set of more detailed concerns. Together, they suggest structures that can inform an ontology of AIS methods and modules.
Design and Formal Analysis of an Authentication Protocol, eWMDP on Wearable Devices

For wearable devices, this paper proposes an authentication protocol eWMDP. We formally model and analyze it. In the execution model of the protocol, a Dolev-Yao adversary is constituted. With the above, various security properties can be defined for measuring security performance. Our protocol can achieve the injective-auth property, in which case it surpasses another existing wearable authentication protocol-WMDP. In addition, our analysis work is confirmed by Scyther and tamain-prover, and the two protocols are implemented on PC and on Ti CC3200 LAUNCHPAD widely used in wearable devices in the meanwhile. The results of experiments highlight the higher transmission efficiency of eWMDP under various configurations, including different platforms, sorts of combinations of encryption algorithms and hash algorithms, and sending packets in different sizes.
Approach to Automatic Determining of Speakers of Direct Speech Fragments in Natural Language Texts

Natural language text consists of an author’s or narrator’s text and direct speech fragments. They have different speakers so they could use different vocabularies and syntactic structures. In order to analyze the dependency of vocabulary and sentence structure on speaker, it is necessary to attribute each text fragment to its speaker. The results of such analysis can be used in natural language text generation tasks, allowing to convey different narrative voice depending on the purpose of the generated text. The authors developed a set of rules for attributing direct speech fragments to speaking characters, created a method of direct-speech scene analysis and implemented it in a software tool. In order to evaluate the accuracy of the attribution of direct speech fragments to speakers, an experiment was carried out. The results of the experiment show the viability of the developed method and allow to improve it for further use. The potential applications of the developed method and the software tool are discussed.
FURTHER VOCATIONAL EDUCATION ROLE IN PERSONALITY’S CONTINUING EDUCATION

The article is devoted to further vocational education as a pedagogical and social-economic phenomenon. The author identifies the place and role of further vocational education in the structure of a personality’s continuing education, substantiates its importance for an individual’s professional and personal formation under the modern conditions. The main interpretations of the notions under consideration and approaches to their studying are presented.
PPD-DL: Privacy-Preserving Decentralized Deep Learning

Privacy is a fundamental challenge for the collection of massive training data in deep learning. Decentralized neural network enables clients to collaboratively learn a shared prediction model, which can protect clients’ sensitive dataset without the need to centrally store training data. But distributed training process by iteratively averaging client-provided model updates will reveal each client’s individual contribution (which can be used to infer clients’ private information) to the server which maintains a global model. To address such privacy concern, we design privacy-preserving decentralized deep learning which we term PPD-DL. PPD-DL includes two non-collusion cloud servers, one for computing clients’ local update safely based on homomorphic encryption, the other for maintaining a global model without the details of individual contribution. During the training and communications, PPD-DL ensures that no more information will be leak to the honest-but-curious servers and adversary.
From Attacker Models to Reliable Security

Attack trees are a popular graphical notation for capturing threats to IT systems. They can be used to describe attacks in terms of attacker goals and attacker actions. By focusing on the viewpoint of a single attacker and on a particular attacker goal in the creation of an attack tree, one reduces the conceptual complexity of threat modeling substantially. Aspects not covered by attack trees, like the behavior of the system under attack, can then be described using other models to enable a security analysis based on a combination of the models.