Reviewerly/exHarmony huggingface dataset:
The modification of caesar cryptosystem based on binary vertices colouring

Abstract Recently information technology has developed rapidly like the development of computer hardware and software. By the existence of internet technology, the process of data transfer can be done by others from a separate place and outside the system environment. The ease of data access remotely (using the internet) does not mean that they are always positive or good, but they also make new problems for the information world. The biggest problems when using the internet are slow access speeds and security when transferring data. Communication via internet is very susceptible to data loss or changed that caused by system errors or actions from non responsible persons. According to Kadir (2011) errors in data transfer via internet are caused by two reasons, that are the existence of human error or improper user intervention and the existence of technical errors from its system. Many efforts have been made by internet users to solve the problem of data transfer, including encoding data that will be sent in order to no user can read the data except the person has the right to receive it. Just not encoding, there is also an authentication process or validation of the data received, this is done to ensure that the data received is correct or does not changed. In this paper, the author propose a modification of the caesar cipher where if the key used previously is a real number, then we change the key into the coloring of vertices on the graph. So the key that used in this method is a graph. The core of this research is that every character of the plaintext has affiliation with each vertex of the graph, so that if the vertex of the graph gets 4 colors for example, then the characters of the plaintext affiliated with that vertex are also added with 4. This process will be repeated until all characters in the plaintext are coded according to the color of vertex. If the number of the characters plaintext is greater than the number of vertices, then the repeat process will occur until all the characters from the plaintext have affiliation with the vertex on the key graph.
Energy Saving Heuristics for Optimization of Cloud Data Center

In the current scenario the demand for high performance computing system increases day by day to achieve maximum computation in minimum time. Rapid growth of Internet or Internet based services, increased the interest in network based computing or on-demand computing systems like cloud computing system. High computing servers are being deployed in large quantity for cloud computing in form of data Centers through which many different services on internet are provide to the cloud users in a very smooth and efficient manner. A large distributed system is described as a data center that includes a huge quantity of computing servers connected by an efficient network. So the consumption of energy in such data centers is enormously very high. Not only the maintenance of the data centers are too exorbitant, but also socially very harmful. High vitality costs and immense carbon footprints are brought in these data centers because the servers needed a substantial amount of electricity for their computation as well as for their cooling. As cost of energy increases and availability decreases, focus should be shifted towards the optimization of data centre servers for best performance alone with the policies of less energy consumption to justify the level of service performance with social impact. So in this paper we proposed energy aware consolidation technique for cloud data centers based on prediction of future client's requests to increase the utilization of computing servers as per request of users/clients which associated some demand of cloud resources for maintain the power consumption in cloud.
Preserving Location Privacy Using Blockchain

Yadav, Rishipal Nimkarde, Sumedh Jat, Gaurav Rao, Udai Pratap Parmar, DilayWith the advancement of technology and enhanced techniques of the global positioning system, the use of location-based services has significantly increased in the last decade. With the increase in the use of these services, there is also a rise in concern for the preservation of location privacy. There have been some cases where location data was disclosed, which even led to some serious crimes. Preservation of location privacy becomes a must in these situations. There are various techniques for preserving location privacy. Some use an anonymizer in between location-based services (LBS) and user, while other uses a distributed architecture for preserving location privacy. In this paper, a blockchain-based decentralized architecture for preserving location privacy is proposed. Earlier users had to trust either the anonymizer or the LBS for retrieving the query results, but with this proposed solution, advancement toward zero trust model would be possible.
Assessment of the Information System’s Protection Degree from Social Engineering Attack Action of Malefactor While Changing the Characteristics of User’s Profiles: Numerical Experiments

The article describes an approach to the analysis of changes in the user’s protection level from the social engineering attack actions of malefactor in the case of applying two strategies to increase the level of protection. The first deals with changing information system’s users (dismissal/advanced training), and second is changes in user access policies to critical information stored in such information systems. Numerical experiment is also presented.
Uncorrelated Discriminant Projection Based on Maximum Margin Criterion and Its Kernelized Extension

Abstract A new linear dimensionality reduction algorithm called uncorrelated discriminant projection (UDP) is proposed in this paper. The proposed UDP algorithm is based on the maximum margin criterion (MMC) which aim at maximizing class separation after dimension reduction. By imposing an uncorrelated constraint in the objective function, UDP extracts statistically uncorrelated features which are important in many pattern recognition problems. Moreover, we propose performing UDP in reproducing kernel Hilbert space (RKHS) which leads to a nonlinear variant of UDP called kernel uncorrelated discriminant projections (KUDP). In order to demonstrate the effectiveness and efficiency of the newly proposed algorithms, we conducted experiments on two benchmark face databases. The experimental results indicates that both UDP and KUDP are able to find face subspaces optimal for recognition.
Video Object Segmentation Using Convex Optimization of Foreground and Background Distributions

In this study, a video object segmentation approach using convex optimization of foreground and background distributions is proposed. The proposed approach consists of four stages. First, optical flow computation and superpixel segmentation are performed on video frames. Second, convex optimization with a mixed energy function is employed to estimate the initial foreground and background distributions of video frames. Third, binary label maps for video frames are generated by maximum a posteriori (MAP) estimation. Fourth, the binary label maps are refined to obtain the final video object segmentation maps. Based on the experimental results obtained in this study, the performance of the proposed approach is better than those of three comparison approaches.
Cyber Defamation Awareness Among Adolescent: Case Studies in One Private Institution

Defamation sounded itself is unprecedented proliferation. This unprecedented proliferation in information and communication technology (ICT) such as electronic mail (e-mail), chat groups, online forums, web logs (blogs), social media platforms and many others, our daily lives have changed significantly. This great feature allows Internet users to publish [25]and distribute information freely to the general public. Therefore, the finding of this research shown the level of awareness for cyber defamation among adolescent users is still at medium level which is 3.01. Awareness in cyber defamation cases among adolescent can practically differ from the perspective of early adulthood and the aim of this research is to increase the behavior of helping and academic shared writing. These findings should be the early initiatives in helping our students to be aware of this cyber defamation. Future research regarding this should be applied into the bigger population,respective fields and difference ranged because this research limit only for respondents on specific target (case studies) and these findings can not be generalized to Malaysian context.
Machine Learning Algorithm Based on Big Data

Abstract Machine learning technology is indispensable for big data. In machine learning, data on a large scale can improve the accuracy of the model. However, complex machine learning algorithms require the key technology of distributed memory computing in time and performance. Big data memory computing can implement the parallel operation of the algorithm, which is conducive to the processing of big data sets by the machine learning algorithm. Hence, a nonlinear machine learning algorithm implemented in the big data memory environment is proposed in this paper, where data compression, biased sampling or loading based on the implementation is optimized. To fully configure resources for the script running by batch, we also implemented a machine learning framework to schedule the optimized algorithm mentioned above. The experimental results showed that the mean error of the three algorithms after optimization was reduced by 40%, and the mean time was reduced by 90%.
Pre-diagnostic of diabetes with the use of statistical machine learning

The aim of this work is to make a prediction of diabetes, which included acquiring knowledge about diabetes, their habits, food income, daily activity. Processing data, producing a statistical model and implementing the model into an application. Which can predict the probability of being a diabetic, based of his lifestyle and genetic assumptions.
Some Remarks on the Local Unitary Classification of Three-Qubit Pure States

Abstract Characterization of multipartite entanglement is in general a challenging task. The entanglement polytope is particularly useful to study three-qubit systems since it allows a qualitative analysis of the related non-local properties from a geometric perspective. In this work, using numerical approaches, we show that there exist a correspondence between some classes of entanglement and some specific regions of the polytope for three-qubit systems.
Research on Distributed New Energy Information Interconnection Mode Based on Blockchain Consensus Mechanism

Under the background that distributed new energy is widely connected to the power grid, the operation mode of traditional power grid data acquisition and processing platform is prone to data transmission errors and high risk of information security. This paper proposes a distributed new energy information interconnection mode based on the consensus mechanism of blockchain, which is based on the characteristics of decentralized, traceable and tamper-proof of blockchain technology. The purpose of this mode is to ensure the security and reliability of large-scale new energy access to the power grid in the future and integrate the distributed new energy generation and consumption data from all over the country. Considering that there is no Byzantine fault tolerance in the power system itself, the improved RAFT consensus algorithm is selected to be applied to the scenario of grid information interconnection, and the corresponding measures are given under the condition of grid fault splitting. Finally, the rationality of the algorithm is analyzed from the perspective of algorithm complexity and fault tolerance, which can provide a reference for the future development of distributed new energy information interconnection mode.
ViSeR: Visual Self-Regularization

We propose using large set of unlabeled images as a source of regularization data for learning robust representation. Given a visual model trained in a supervised fashion, we augment our training samples by incorporating large number of unlabeled data and train a semi-supervised model. We demonstrate that our proposed learning approach leverages an abundance of unlabeled images and boosts the visual recognition performance which alleviates the need to rely on large labeled datasets for learning robust representation. In our approach, each labeled image propagates its label to its nearest unlabeled image instances. These retrieved unlabeled images serve as local perturbations of each labeled image to perform Visual Self-Regularization (ViSeR). Using the labeled instances and our regularizers we show that we significantly improve object categorization and localization on the MS COCO and Visual Genome datasets.
Proposed Technique for Encryption JPG and BMP Images

The technique of chaotic system is considered one of the applications for high security encryption, where a color images sent is encrypted for the purpose of preserving from hacker futility. The color images which are used to study are in both formats JPG and BMP. This paper deals with the method of the numeric encryption key to encrypt each one of the two formats type images and then decrypt the result images using the same key. The measures where used to study the statistical characteristics of result images are the histogram of images, original signal divided by noise signal rate (SNR), maximum squared errors (MSE) and peak signal to the noise rate (PSNR). It is found that the chaotic key with control parameter (r) in rang (3.4567-- 4) and initial value(x) within rang (0.6716 -- 1) make high mixing. Encryption results for BMP color images are very good and efficiently, because of the decryption process given the same input image and have the same pixels values without any loosed values. The result of encryption technique for the color images in JPG format is given a little and acceptable loss because of resaving process for resaving output images using compress data in jpg format algorithm.
SIMULATION OF REVERSE FLOWS IN THE TRANING MODEL

Approaches to mathematical modeling of reversible technological flows in training models of computer training complexes are considered.
Towards Automated Translating Natural Language Sentences into Intensional Constructions

This paper aims to introduce a new and original implementation of the semantic machine of transparent intensional logic. This logic system is characterized by its procedural semantics, thanks to which it is used in the field of logical analysis of natural language. Transparent intensional logic is a high-order logic, where partial functions of the intensional typed λ-calculus allow to remove the semantic ambiguities of natural language. Natural language expressions are usually analyzed in three steps - type analysis, synthesis of construction, and type control, which is an optional step. The presented implementation of the semantic machine of transparent intensional logic is focused on the first two of the mentioned steps of formalizing the meaning of sentences. This semantic machine uses outputs from the Stanford CoreNLP parser. Its functionality and correctness are demonstrated at the end of the paper through the analysis of several sentences of natural language.
Decentralized Interledger Gateway Architectures in Authorization Scenarios with Multiple Ledgers

Combining multiple Distributed Ledger Technologies (DLTs) that include public and private/permissioned ledgers can allow different tradeoffs in terms of performance, cost, privacy, and transparency. However, multiple DLTs must be interconnected in a way that securely binds transactions on different ledgers and allows reliable and trusted transfer of information across the ledgers. We present decentralized interledger gateway architectures for IoT authorization scenarios that include the interconnection of two ledgers: an authorization ledger and a payment ledger. The proposed architectures differ in their complexity, transaction cost, and ability to handle transactions involving multiple ledgers.
Research on Terror Video Identification Technology Based on Multi-instance Learning

The main purpose of terror video study is to analyze the emotions contained in video itself. The main content of this paper includes the following four parts: first, video in the data set was preprocessed to extract the visual features expressing horrible video, such as optical flow features, color moment and other traditional features. In addition, relevant features were extracted by using convolutional neural network. Second, the features of each frame in video are regarded as instances, and video is expressed as positive and negative packages of multi-instance learning. Then select visual semantics to construct visual projection space according to the principle of maximum point density; Then the nonlinear function is used to calculate the feature of visual projection. Thirdly, support vector machine and extreme learning machine training classifier are used to classify and identify whether it is terror video or not. Fourthly, relevant experiments are carried out to analyze the differences between traditional features and features extracted by convolutional neural network as well as two algorithms, support vector machine and extreme learning machine, and to evaluate the model.
Models and Formal Languages

In this chapter, we introduce the notions of mathematical model and formal language and discuss the notion of a system of interpretation as a homomorphism between a formal language and a model.
Research on Multimedia Community Teaching Platform into Problem-Based Learning (PBL) - An Empirical Study on the Teaching Mode of Creative Innovation and Entrepreneurship Courses for Technical and Vocational Students

Under the project of technical and vocational reengineering, Teachers’ teaching research and teaching practice is an indispensable step, facing the rapid change of industrial environment, Teachers use of the network community and the ability to solve the problem have become the important factors to shorten the learning gap. The purpose of this study is to explore the application of network community and problem-based learning effectiveness and learning attitude and learning satisfaction. This paper aims to understand the teaching outcomes of the network community application and problem-based learning model, and to construct relevant teaching strategies, teaching materials and teaching activities. According to the results, this study found that the integration of Google Hangouts and PBL has obvious effects on the learning of the creativity, innovation and entrepreneurship course. In particular, there are obvious differences in overall academic performance. Secondly, students’ learning attitude is also better because of real-time discussion through Google Hangouts. This result reflects that the multimedia teaching method and grouping design strategy can indeed improve the learning effect of students.
An Effective Adversarial Training and Prediction Network for Time Series Anomaly Detection

Abstract Due to the scarcity and uncertainty of anomalies, anomaly detection becomes a challenging problem in communities. This paper proposes an effective time series anomaly detection network based on prediction and adversarial training. First, we employ LSTM model based on differential attention mechanism to fully extract the inner characters of time series. To prevent the over-fitting due to only normal samples used to train our LSTM model, we introduce an adversarial training strategy by adding adversarial samples to enhance the generalization of the model on normal data. The effectiveness of our model is demonstrated on several public datasets.
Science Communication и популяризация науки в массмедиа

The paper focuses on the specifics and types of science popularization in the contemporary mass media. The article describes the role of science communication in popularization of science. It justifies the impact of financial support and PR for research and development on the formation of image of science, which is considered to be an indicator of the importance of the state as a socio-political institution.
Development Of Socio-Emotional Competence Through Collaborative Learning In Primary School

The classroom should feel like a family and every student can fulfill his potential. Collaboration strategies can be powerful tools for teachers in order for them to facilitate an atmosphere of trust and a sense of belonging. Through cooperation children can learn about themselves and about other as well. They can learn to work together to find solutions to problems or to discuss other topics of interest. Collaborative learning refers to situations when learning occurs in classroom groups or pairs. There are many benefits to collaborative learning that include: developing oral communication, self-management, student-student interactions, understanding diverse perspectives of society. The teacher has to navigate with confidence when addressing problems of logistics and infrastructure in the classroom or school. Addressing the problem correctly and honestly teachers can find that collaborative learning has always been happening in their classrooms and maybe even in their schools. Students have a tendency to work together and try to solve their tasks during a school day. Teachers need to develop a more meaningful approach when it comes to creating a collaborative environment where the pieces of the learning process are fit together and the results are relevant to students’ learning experience. This paper focuses on the approaches, resources and tools needed to create a classroom where the students can have a collaborative learning experience which helps them achieve their potential.
Internet plus -based Micro-monitoring Platform Teaching Monitoring Method

Abstract Traditional teaching quality monitoring is a relatively static inspection process, while teaching activities are a continuous and dynamic process. Therefore, exploring an effective, continuous and dynamic process teaching quality monitoring method, improving the effectiveness of teaching quality monitoring, and promoting the continuous improvement of teaching quality(TQ) is an important issue facing teaching management departments. The purpose of this article is to study the teaching monitoring method of micro-learning platform based on Internet +. In terms of methods, it is proposed to reform the teaching evaluation system, improve the efficiency of TQ monitoring, and ensure the effectiveness of TQ and staff training. Four improvement measures are proposed, including the three-level department lecture system and teaching supervision system, the implementation of teaching supervision system, and the establishment of a TQ information feedback system to improve the effectiveness of teaching monitoring. The data are found, and it is found that the improved monitoring method proposed in this paper is more effective than the original teaching method of micro-learning platform. Based on student feedback, I hope to promote the monitoring method proposed in this article.
A General Framework for Saliency Detection Methods

Saliency detection is one of the most challenging problems in the fields of image analysis and computer vision. Many approaches propose different architectures based on the psychological and biological properties of the human visual attention system. However, there is not still an abstract framework, which summarized the existing methods. In this paper, we offered a general framework for saliency models, which consists of five main steps: pre-processing, feature extraction, saliency map generation, saliency map combination, and post-processing. Also, we study different saliency models containing each level and compare their performance together. This framework helps researchers to have a comprehensive view of studying new methods.