Reviewerly/exHarmony huggingface dataset:
Performance Analysis and Recognition of Speech using Recurrent Neural Network

Speech is one of the most natural ways to communicate between people. It plays an important role in our daily lives. To make machines able to talk with people is a challenging but very useful task. A crucial step is to enable machines to recognize and understand what people are saying. Hence, speech recognition becomes a key technique providing an interface for communication between machines and humans. There has been a long research history on speech recognition. Neural network is known as a technique that has ability to classify nonlinear problem. Today, lots of research are going in the field of speech recognition with the help of the Neural Network. Even though positive results have been obtained from continuous study, research on minimizing the error rate is still gaining lots attention. The English language offers a number of challenges for speech recognition. This paper implements the RNN to analyze and recognize speech from the set of spoken words.
Trap Space Trajectory Planning for Unmanned Aerial Vehicle Based on Human-RRT Algorithm

Aiming at the problem of UAV trajectory planning, the aircraft can fall into the trap space easily and fall into an infinite loop or spend a lot of time planning the route. In this paper, Trap space trajectory planning based on Human-RRT algorithm is proposed. First introduced the concept of trap space and its impact on the trajectory planning of UAV, and briefly introduced the existing RRT (Rapidly exploring Random Tree) algorithm and the lack of response trap space. On the basis of the standard RRT, this paper avoids the need of repeated searching for the RRT algorithm by setting the virtual target points manually, and at the same time sets the fast convergence strategy to improve the efficiency of the algorithm. Finally, the optimal trajectory planning is achieved by extending and optimizing the nodes and deleting the redundant nodes. Compared with the existing methods, the H-RRT algorithm can verify that the H-RRT algorithm can effectively solve the trap space planning problem, improve the efficiency of the trajectory planning and optimize the trajectory performance.
JECI: A Joint Knowledge Graph Embedding Model for Concepts and Instances

Concepts and instances are important parts in knowledge graphs, but most knowledge graph embedding models treat them as entities equally, that leads to inaccurate embeddings of concepts and instances. Aiming to address this problem, we propose a novel knowledge graph embedding model called JECI to jointly embed concepts and instances. First, JECI organizes concepts in the knowledge graph as a hierarchical tree, which maps concepts to a tree. Meanwhile, for an instance, JECI generates a context vector to represent the neighbor context in the knowledge graph. Then, based on the context vector and supervision information generated from the hierarchical tree, an embedding learner is designed to precisely locate an instance in embedding space from the coarse-grained to the fine-grained. A prediction function, as the form of convolution, is designed to predict concepts of different granularities that an instance belongs to. In this way, concepts and instances are jointly embedded, and hierarchical structure is preserved in embedds. Especially, JECI can handle the complex relation by incorporating neighbor information of instances. JECI is evaluated by link prediction and triple classification on real world data. Experimental results demonstrate that it outperforms state-of-the-art models in most cases.
The Use of Art Therapy in Conflictology in Education

The aim of the article is to evaluate and analyze the possibility of applying art therapy technologies to conflict management in the field of education among teachers of creative disciplines.In the paper, a comprehensive approach to analysis of interpersonal conflicts in a group as well as intrapersonal conflicts of individual team members is offered as a solution to interpersonal tensions reduction and conflict prevention.Authors propose conflictological role-playing game based on the art-therapeutic approach (ATA), which is a theatrical act including conflicts prescribed in the characters and their interaction as a creative training method.The article presents the results of art therapy application as a means of conflict management in pedagogical environment.The scientific novelty of the study is the assessing the results of the using art therapy among teachers of creative disciplines.The data obtained in the research and presented in this article can be employed in conflict consulting in creative oriented groups.
A New Type of Signature Scheme Derived from a MRHS Representation of a Symmetric Cipher

We propose a new concept of (post-quantum) digital signature algorithm derived from a symmetric cipher.Key derivation is based on a system of Multiple-Right-Hand-Sides equations.The source of the equations is the encryption algorithm.Our trapdoor is based on the difficulty of creating a valid transcript of the encryption algorithm for a given plaintext (derived from the signed message): the signer can use the encryption algorithm, because he knows the secret key, and the verifier can only check that the solution of the equation system is correct.To further facilitate the verification, we use techniques from coding theory.Security of the system is based on the difficulty of solving MRHS equations, or equivalently on the difficulty of the decoding problem (both are NP hard).
Text Steganography Based on Parallel Encryption Using Cover Text (PECT)

This paper presents a self-checking text steganography which could be used efficiently where large data is involved in various IoT applications. The space complexity of its cover text is one-fourth of its message size which is very less as compared to other techniques. The cover text of this method is generated from the private message itself. Later it is used to check the correctness of the data on receiver side. This helps to reduce the time overhead in case of corruption of any part of the data. Thus, the security and trust issues of IoT are easily handled even on high traffic channels.
A Hybrid Algorithm Based on Invasive Weed Optimization Algorithm and Grey Wolf Optimization Algorithm

In this research, two algorithms first, considered to be one of hybrid algorithms.And it is algorithm represents invasive weed optimization.This algorithm is a random numerical algorithm and the second algorithm representing the grey wolves optimization.This algorithm is one of the algorithms of swarm intelligence in intelligent optimization.The algorithm of invasive weed optimization is inspired by nature as the weeds have colonial behavior and were introduced by Mehrabian and Lucas in 2006.Invasive weeds are a serious threat to cultivated plants because of their adaptability and are a threat to the overall planting process.The behavior of these weeds has been studied and applied in the invasive weed algorithm.The algorithm of grey wolves, which is considered as a swarm intelligence algorithm, has been used to reach the goal and reach the best solution.The algorithm was designed by SeyedaliMirijalili in 2014 and taking advantage of the intelligence of the squadrons is to avoid falling into local solutions so the new hybridization process between the previous algorithms GWO and IWO and we will symbolize the new algorithm IWOGWO.Comparing the suggested hybrid algorithm with the original algorithms it results were excellent.The optimum solution was found in most of test functions.
The Implementing Tad (Transition - Action - Details) Teaching Strategy On Students’ Writing Ability At First Grade Of Sma Pgri Pekanbaru

The aim of this study is to find out the effect of TAD teaching strategy on students’ writing ability at first grade of SMA PGRI Pekanbaru. The design of the research is quasi experimental research with factorial design 2x2. The samples were X IPS 1 as experimental class and X IPS 3 as control class. The samples were selected by using cluster random sampling. The instruments were writing test of recount text. These instruments were tried out first to another class which had similar capabilities to both experiment and control class. The data were analyzed by using T-test. The result of this research shows that TAD teaching strategy produces higher result on students’ writing ability of recount text compared to Small Group Discussion.
The Cultural of Fourth Industrial Revolution in Learning Process

The emergence of a new technology known as 'The Fourth Industrial Revolution' or the Industrial Revolution 4.0 has led to the current development of technology that predicts 50 billion electronic devices will interact with each other.Meeting the challenges of the Industrial Revolution 4.0, all students at the Institute of Higher Education (IPT) need to be prepared with this new era.Accordingly, a study was conducted on 30 students of the Faculty of Information Science and Technology (FTSM) Universiti Kebangsaan Malaysia (UKM) in Bangi, Selangor.This study aims to identify the level of knowledge, willingness and skills among students to pursue the Challenge of Industrial Revolution 4.0.This research is a descriptive study which use Likert scale.The data was analyzed using SPSS software.The findings show that the students' knowledge level towards the Industrial Revolution 4.0 is at a moderate level.Meanwhile, the study also found that student readiness was at moderate level.The findings also found that the skills level towards the Industrial Revolution 4.0 is at a moderate level.Researchers suggest that students should focus on maintaining the identity, sustainability of the mind, culture and the identity of students in the face of the new National Development and Transformation Plan 2050.Students need to get out of the habit and master the 4C elements of Critical Thinking & Problem Solving, Communication, Collaboration and Creativity.
Research on Machine Translation Automatic Evaluation Based on Extended Reference

Language is the main carrier of communication between cultures, but the translation between languages has become the biggest problem of people's communication. Machine translation is a process that uses computer to transform a natural language into another natural language. The automatic evaluation of machine translation is an important research content in machine translation technology. It can discover defects in translation system and promote its development. It has achieved rich fruits, and various evaluation methods emerge endlessly after several decades of development of the automatic evaluation method. In this paper, three kinds of representative evaluation methods are introduced and their respective advantages and disadvantages are analyzed. In addition, we describe the evaluation technique based on reference. It plays an important role in improving the performance of automatic evaluation methods although the coverage expansion of reference is not the main method. Finally, we summarize the development trends of automatic evaluation metric based on extended reference and related issues that need to be further addressed.
UiT-researchers’ attitudes and practices towards open access publication : lessons learnt for improving self-archiving in institutional repository

Objective.Using The Munin Repository at UiT The Arctic University of Norway as a case study, a survey was conducted among the UiT researchers to examine their attitudes and practices towards the repository and towards open access (OA) publications.Methods.The survey questionnaire was sent to all employees at UiT between August and October 2016, but only academic staff in research positions, including PhD students, were selected for the analysis.200 researchers responded to the survey.Results.The study found that researchers are supportive of the principle of OA but are less keen on contributing to their institutional repositories for a range of reasons.These include the process being quite a hassle, academics' uncertainty over certain legal, copyright and peer review issues, prestige issues, and uncertainty over the appropriate version to deposit or self-archive.Contribution.The statistical results can be used by readers as benchmarks to compare with their own institution's figures.Conclusively, the study found that more than 90% of the researchers agreed with the OA principles, but still 54% of UiT researchers did not archive their research documents in The Munin Repository.
An Efficient Multi-Core Resource Allocation using the Multi-Level Objective Functions in Cloud Environment

Background: In recent years, the computational memory and energy conservation have become a major problem in cloud computing environment due to the increase in data size and computing resources. Since, most of the different cloud providers offer different cloud services and resources use limited number of user’s applications. Objective: The main objective of this work is to design and implement a cloud resource allocation and resources scheduling model in the cloud environment. Methods: In the proposed model, a novel cloud server to resource management technique is proposed on real-time cloud environment to minimize the cost and time. In this model different types of cloud resources and its services are scheduled using multi-level objective constraint programming. Proposed cloud server-based resource allocation model is based on optimization functions to minimize the resource allocation time and cost. Results: Experimental results proved that the proposed model has high computational resource allocation time and cost compared to the existing resource allocation models. Conclusion: This cloud service and resource optimization model is efficiently implemented and tested in real-time cloud instances with different types of services and resource sets.
Design of a Digital Baseband Transmission Signal Coding Software Based on Java

Aiming at the lack of visual interface in digital baseband signal coding system, this paper designs a coding software for digital baseband transmission signal based on Java. Firstly, the common pattern of baseband transmission and its coding principle are given. Then the software interface is designed by Java Swing, and the algorithm of common coding is given at the same time. By giving the original message code, the AMI, HDB3 and other coding results can be obtained. The experimental results show that the software can perform digital baseband coding on the message code.
Biometric based Multi-Authority Inner Product Encryption for Electronic Health Record

Introduction:Attribute-based encryption enhances the security of electronic health records outsourced to the cloud.At the same time, single authority attribute based encryption leads to user privacy breech and attribute management complexity.Multi authority attribute-based encryption enhances user privacy and attribute management, but lacks privacy and integrity of data.Therefore, biometric-based multi-authority inner product encryption is proposed to improve data integrity, data privacy, and user privacy.Objectives: The proposed scheme aims to enhance data integrity, data privacy, and user privacy in cloud-based Electronic Health Record.Methods: An exhaustive literature review has been made related to securing electronic health record outsourced to the cloud and found that data integrity is lacking in existing schemes.So an efficient encryption scheme has been proposed which adopts elliptic curve cryptography to enhance data integrity.The security analysis and computational complexity of the proposed scheme is done and compared with existing schemes.Results: The proposed scheme guarantees data integrity and privacy of the sensitive information stored in the electronic health records.The scheme also satisfied the security requirements user privacy, fine-grained access control, and scalability needed for electronic health record outsourced to the cloud.The computational complexity of the proposed scheme is compared with the existing schemes.The result shows that user access rejection complexity and key generation complexity are comparatively low.Conclusion: Biometric, as a global identifier of the user, could improve data integrity for the electronic health records outsourced to the cloud.Multi-authority Inner Product Encryption hides the access structure, along with the data, could improve data privacy and user privacy.Employing NOT gate in the access structure reduces user access rejection complexity.
A Practical Approach of Different Programming Techniques to Implement a Real-time Application using Django

Real-time web-applications surround our daily life.The most popular are shopping platforms like Amazon or Zalando.Behind these websites huge and complex shop-systems are typically used.These systems manage and handle thousands of parallel synchronous and asynchronous tasks like user interactions, user authentications, communication with an ERP system (enterprise resource planning), or the handling of the website visualization in the background.Through the independence of the computer system and the portability on other devices, like touch panels or mobile phones the web-technology is also used for industry 4.0 applications.The applications can be e.g. predictive maintenance dashboards or monitoring dashboards for the production line.The key task for these dashboards are the visualization and analyzing of a huge amount of data in real-time.For both scenarios, the data have to be preprocessed continuously in different steps.In summary, the industry 4.0 application has to manage a different kind of tasks compared to a typical webshop.These tasks contain for example big data preprocessing tasks, which are mostly CPU bound, in contrast to the typically I/O bound task of a webshop (e.g.database access).The main problem with web application frameworks is that they are not designed to execute a lot of CPU bound tasks and handle webrequests at the same time.The purpose of this paper is to compare two different programming techniques for the work with CPU bound tasks in a real-time web application for the use in industry 4.0 without the use of third-part software like Spark.The research employed two approaches: on the one hand multiprocessing and on the other hand task queues.In this work, two kinds of web-applications on the base of python's web framework Django are implemented.The first application uses multiprocessing and the second application uses the Celery task queue to organize different tasks.The general building blocks are the sensor data streaming via MQTT and the data transformation in various steps.The result of this transformation can be used to visualize the data in real-time charts for example.The implemented web-applications are evaluated by the following criteria: Performance while working with a huge amount of streaming data. Possibilities to optimize the load distribution and avoid overloading. Possibilities to scale up and down hardware.
A Comparative Study on Cyber Crime Mitigation Models

With the advancement of technology and smartphones, most of the transactions are performed online. This increased dependence on online transactions has resulted in the perpetration of cyber-crime. Any crime committed using electronic gadgets can be termed as Cybercrime. Cybercrime revenues are estimated at 1.5 trillion dollars for the year 2018. Lack of awareness in Cybercrime vigilance and reporting leads to many such crimes going unreported. This paper gives an insight into types of Cybercrime and reporting mechanism. A model has been proposed which would remove the bottlenecks of existing mechanism and lead to effective cyber-crime investigation. The paper would be of immense help to the stakeholders such as online users, investigative agencies and cyber-crime researchers.
Convolutional Neural Networks on Apache Storm

the performance of deep learning largely depends on the size of data. One data source is real-time streaming data, produced from mobile devices, sensors or social media, etc. Streaming data is high-speed and large-scale, which needs real-time processing. However, current mainstream frameworks are mainly designed for off-line data. To suit this, we first propose a deep learning framework based on Apache Storm, which is a distributed stream processing frame, fast and fault-tolerant. Our framework implements the distributed training of CNNs. which is different from MMLSpark or TensorFlowOnSpark that is a pure Java implementation. The design of message passing and synchronization is also suitable to other MapReduce-family distributed computing platforms. To validate our work, MNIST and Cifar -10 datasets are used for evaluation and comparison with similar architectures. The results show our framework, in resource-limited environment, realizes about 10 times speedup.
DIGITAL APPS IN FORMATIVE ASSESSMENT: TODAY’S AID IN TEACHING AND LEARNING IN HIGHER EDUCATION

Assessment is an extensive topic that encompasses a wide variety of definitions within the field of education, and evaluating the effectiveness of many types of assessment is often an arduous task. Master, G. 2015 stated, “The fundamental purpose of assessment in education is to establish and understand the points that students (either as individuals or groups) have reached in their learning at the time of assessment’. Assessment is the process of gathering data. More specifically, assessment is the way instructors gather data about their teaching and their students’ learning (Hanna, G and Dettmer, P, 2004).There are many different forms of assessment, each with their own specific purpose. Determining what kind of assessment is most effective is highly dependent on the purpose of the data a teacher is trying to collect. Since assessment is often used in making major decisions, there is a great deal of discussion regarding strategies that could improve student performance. Trends in formative assessment and using data to positively impact student learning has become an integral part of a teacher’s everyday and ongoing practice. This article addresses formative instructional practice and the many strategies, techniques and tools available to both teachers and students. These tools and apps give teachers and students many options and opportunities for classroom success. KEYWORDS: Formative assessment, summative assessment, assessment of learning, formative assessment apps.
Homomorphic CNN for Privacy Preserving Learning On Encrypted Sensor Data

Homomorphic encryption assisted privacy preserving learning has become one powerful technology in actual scenarios and applications. In this paper, we design a novel homomorphic CNN in conjunction with homomorphic encryption to make prediction on encrypted sensor data. We carry out privacy preserving learning experiments with different activation functions and evaluate the accuracy on the MSTAR dataset. Experimental results show the models’ effectiveness on encrypted sensor data with privacy preserving.
Radar modulation recognition based on MLP neural network

In this paper, a radar modulation recognition method based on improved multi-layer perception (MLP) neural network is proposed. Some improvements are made in following aspects: initialization of the network model weights, normalizetion of the input parameters and setting of the learning rate. Simulation experiments show that this method has high recognition and generalization capability with the relatively simple network model. When the signal-to-noise ratio is as low as 0 dB, the correct recognition rate is over 90%, and meanwhile it has good recognition performance for complex intra-pulse modulation signals.