Reviewerly/exHarmony huggingface dataset:
3D Motion Trajectories Recognition using Optimal Set of Geometric Primitives, Angular and Statistical Features

This paper presents the 3D motion trajectories (lower case 3D alphabetic characters) recognition using optimal set of geometric primitives, angular and statistical features. It has been observed that the different combinations of these features have not been used in the literature for recognition of 3D characters. The standard dataset named “CHAR3D” has been used for analysis purpose. The dataset consists of 2858 character samples and each character sample is 3 dimensional pen tip velocity trajectory. In this dataset only single pen down segmented characters have been considered. The recognition has been performed using Random Forest (RF) and multiclass support vector machine (SVM) classifier on the optimal subset of extracted features. The best obtained recognition accuracy of 83.4% has been recorded using 3D points, angular and statistical features at 10 fold cross validation using SVM classifier. Moreover, the highest recognition accuracy of 96.88% has been recorded using an optimal subset of 32 dimensional features namely, geometric primitives, angular and statistical features at 10 fold cross validation by RF classifier.
Comparative Study of Isolation Forest and LOF algorithm in anomaly detection of data mining

[Purpose] In the research of data mining, anomaly detection algorithms can accurately find samples of abnormal behaviors to achieve the purpose of data mining. the isolation forest algorithm and the LOF algorithm play an important role as the classic representatives of anomaly detection algorithms, but which algorithm is more suitable for processing massive amounts of data is a constant concern. [Method] Select the isolation forest algorithm and the LOF algorithm. Firstly, analyze the principle and process of the two algorithms; then use the two algorithms to conduct experimental simulations through the data set to compare and study the accuracy and stability of the two algorithms in data anomaly detection. [Conclusion] The experimental data shows that the isolation forest algorithm is more suitable for anomaly detection in data mining; at the same time, improvements are proposed for the shortcomings of the isolation forest algorithm.
Application of Bayesian Regularization Algorithm for Evaluation of Performance Software Complexity Prediction Model Based on Requirement

Model performance evaluation is a method and process of evaluating the model that has been built. The model that will be evaluated is software complexity prediction model based on requirement. This model allows measuring software complexity before actual design and implementation. The experiment used three datasets namely training dataset, validation data set , and test dataset. For performance evaluation using Mean squared error. Mean squared error is very good at giving a description of how consistent the model is built. By minimizing the value of mean squared error, it means minimizing model variants. Models that have small variants are able to give relatively more consistent results for all input data compared to models with large variants. This research proposes the application of the Bayesian regularization algorithm for evaluating the performance of software complexity prediction model based on requirement. With this research it is expected to know how much the performance of the model that has been built
MEACS-2020

International Conference on Mechanical Engineering, Automation and Control Systems 2020 (MEACS-2020) was successfully held on September 17, 2020. Initially, the conference was planned to be held in March, as part of the exhibition of equipment for metalworking and welding “MashExpo Siberia 2020” in Novosibirsk. However, due to the lockdown and all the restrictions associated with the coronavirus pandemic, MEACS-2020 was postponed until autumn. The conference was hosted by Novosibirsk State Technical University (Novosibirsk, Russia). Today, Novosibirsk is one of the largest scientific centers in Russia and in the Commonwealth of Independent States. MEACS-2020 provided an opportunity for researchers and practitioners to present and discuss up-to-date developments and innovations in the fields of mechanical engineering, automation of production processes, materials science, and industrial engineering. The participants presented papers on the issues of technological machines and equipment, mechanics and mechatronics, materials science in mechanical engineering, manufacturing process management, automation and optimization of production engineering. After careful reviewing, the most significant and valuable papers were accepted for publication. The volume consists of 79 relevant manuscripts. They present a wide range of technical solutions for automated mechanical engineering, and prospects for using information technologies and modeling methods in engineering. We would like to express gratitude to the Organizing Committee for hosting MEACS-2020, despite the unfavourable epidemiological situation. We would also like to thank all the participants and the editors for their valuable contribution in preparing the MEACS-2020 proceedings. We hope that readers would find this volume relevant and useful. On behalf of the editors, Vadim Skiba, Nikita Martyushev List of Programme Committee and Organizing Committee are available in this pdf.
Countermeasures for timing-based side-channel attacks against shared, modern computing hardware

There are several vulnerabilities in computing systems hardware that can be exploited by attackers to carry out devastating microarchitectural timing-based side-channel attacks against these systems and as a result compromise the security of the users of such systems. By exploiting microarchitectural resources, adversaries can potentially launch different variants of timing attacks, for instance, to leak sensitive information through timing. In view of these security threats against computing hardware, in a recent study, titled 'Are timing-based side-channel attacks feasible in shared, modern computing hardware?', currently undergoing the review process, we presented and analysed several such attacks. This extended study proceeds to build upon our recent study in question. To this end, we analyse the existing countermeasures against timing attacks and propose new strategies in dealing with such attacks.
MOVIE RECOMMENDATION BASED ON HUMAN EMOTION USING CNN

Emotion plays a vital role in human’s action. Humans highly influenced by movie and music they listen and watch. During this pandemic time, video has become popular and dominant medium and especially OTT (Over the top) service has seen an exponential a growing activity and many companies are now focusing on trying to focus on this field. In past few years, there is much improvement of new techniques to gather real time emotions. In this Paper, Tried to provide the System design model by which recommendation system can be improved on the basis of human emotions to provide dynamic content recommendation to the user. By capturing real-time emotion using deep neural network technique and combining traditional Content- based recommendation we will recommend the list of movies to user. For this implementation we used python to demonstrate its working. Our approach is trying to overcome problem in existing method.
A Novel Text to Speech Technique for Tamil Language using Hidden Markov Models (HMM)

Application of digital signal processing in speech processing plays a major part in our everyday life. Text to speech system lets people to see and read out loud consecutively. Text-to-speech synthesizers use synthesis techniques that require good quality speech. Text to speech conversion (TTS) can apply to many applications such as automation, audio recording and audio-based assistance system. Text to speech conversion can be applied for various multinational language as well as for a number of local languages. An efficient text to speech conversion for Tamil language with extreme accuracy is proposed in this work. Multi feature, with a Hidden Markov Model (HMM) predictor is used to convert text to speech efficiently. By using the proposed method, the precision of the framework is enhanced by a factor of 6% when contrasted with the traditional system.
The use of non-traditional visual means of education in the formation of meteorological and climatic concepts in school geography

The purpose of this article is to highlight the methodology of formation the meteorological and climatic concepts in school geography through the use of new teaching tools. Main material. The article describes visual methods of meteorological and climatic concepts formation in the course of school geography. Unconventional visual tools to be used in learning the following concepts at school: animated maps, dynamic schema, climate diagrams, logic model are considered in the article. The experience of teaching the weather and climate, analyzing the basic concepts that are diffi cult to assimilate by students has been described. Analysis of psychological and age features of school students, their interest in studying these subjects as well as the experience of using animation in the study of school geography, positive and negative factors of animation manuals are outlined. It is shown how to use animation at the lessons of formation of meteorological and climatic concepts. The options for its use at the lessons on the study of weather and climate have been proposed. The possibility of further use of the developed materials in the classroom and in extracurricular activities of secondary schools has been shown. Conclusions and prospects for further research. The use of new visual tools for the formation of meteorological and climatic concepts in the classroom of school geography may interest pupils in the study of new topics. This not only develops pupils’ ideas, but also establishes a logical link between the phenomena. The advantages of using the proposed means of education over traditional means, in our opinion, are signifi cant. But the study of the teachers’ experience shows that very few of them use the latest tools and for various reasons prefer traditional means of education. The reason may be that the new tools require much more time to prepare than the traditional tools. This applies primarily to the creation of new animated maps, dynamic diagrams, search for new information. In the future, we will develop didactic tools linking specifi c types of climate and their graphical representations with the corresponding landscapes of continents.
Efficient publicly verifiable conjunctive keyword search over encrypted data in cloud computing

Cloud computing has brought many appealing features for its users, such as on-demand computing resources, flexible and ubiquitous access and economical cost. Individuals and enterprises are motivated to outsource mass data to the cloud server. However, the privacy and security of the users' data are the main obstacles preventing the application of cloud computing. Searchable encryption technique is a positive way to protect users' sensitive data, while preserving search ability over encrypted data on the server side. However, the cloud server may be lazy and return part of the search results to its users as a result of intending to relieve their workload. Therefore, a verification mechanism should be established to guarantee the correctness and completeness of the search results. In this paper, we present an efficient publicly verifiable keyword search scheme over encrypted data. Our scheme achieves the conjunctive keyword search and ensures the correctness and completeness of the search results even if the untrusted cloud server returns an empty set deliberately. Compared with existing conjunctive keyword search schemes, our scheme is more efficient in the aspect of verifying the search results. Furthermore, we prove that the proposed scheme can achieve the desired security properties.
Charged System Search Algorithm

Charged System Search (CSS) algorithm was developed by Kaveh and Talatahari [1, 2] as an efficient population-based metaheuristic using some principles from physics and mechanics and was applied successfully to various types of structural optimization problems [3–7]. CSS utilizes the governing Coulomb laws from electrostatics and the Newtonian laws of mechanics. In this algorithm each agent is a charged particle with a predetermined radius. The charge of magnitude of particles is considered based on their quality. Each particle creates an electric field, which exerts a force on other electrically charged objects. Therefore, charged particles can affect each other based on their fitness values and their separation distance. The quantity of the resultant force is determined by using the electrostatics laws, and the quality of the movement is determined using Newtonian mechanics laws.
Software tools for analysis and synthesis of stochastic systems with high availability (XIII)

Article is dedicated to methodological support and software tools for analysis and synthesis of stochastic systems with high availability at nonstationary (e.g. shock) disturbances. The methodology is based on Haar wavelet expansions and wavelet canonical expansions. Theory of vector complex statistical criterion is developed. Basic general vector algorithm is for complex statistical criteria worked out. Partial algorithm in case of parameters linear approximation and additive vector noises is given. Special section is devoted to software tools. Test examples and computer experiments. For shock disturbances wavelet technologies have evident advantages. Results were used for accuracy analysis and optimization in case of vector complex statistical criteria in shock stochastic information-control system with high availability.
Motion-aware vehicle detection in driving videos

This paper focuses on vehicle detection based on motion features in driving videos. Long-term motion information can assist in driving scenarios since driving is a complicated and dynamic process. The proposed method is a deep learning based model which processes motion frame image. This image merges both spatial (frame) and temporal (motion) information. Hence, the model jointly detects vehicles and their motion from a single image. The trained model on Toyota Motor Europe Motorway Dataset reaches 83% mean average precision (mAP). Our experiments demonstrate that the proposed method has a higher mAP than a tracking-based model. The proposed method runs real-time in driving videos which enables the model to be used in time-critical applications such as autonomous driving and advanced driving assistance systems.
Browser APIs

Now that you have installed the WebdriverIO tool and learned how to locate an element in a web page, the next step is to develop a capability to perform actions on a located element. This chapter shows you how to perform various actions on a web page’s located elements. You learn various commands to interact with elements, and you learn how WebdriverIO implements them with ease. This chapter covers the following.
Treatment of Carwash Waste Water and Its Reuse to Manage Water Supply

The current problem to water supply is the contamination of water resources by pollution. water pollution generally known to be caused by Domestic and Industries there are also some more Auxiliary units which producing more waste water which is not perceived yet as a cause of waste water . In that Car wash waste water is the major producer of waste water which is let untreated in to water bodies. Transportation of people now uses mostly personal vehicle which leads to increase of more number of individual transport mode. In that particularly usage of car as well as its maintenance leading to create more waste wash water and also utilizing more volume of water for that purpose impact the environmental water balance in many ways as the conventional treatment of waste water costing more they are not think about treatment, So in this work the two samples was collected, and initial characterization was done and it was treated by Adsorption method with different Adsorbent which could be prepared from low cost and easily available material. For this work Prosopis juliflora leaves (AD1)and Casuarina equisetifolia (AD2) leaves were used as adsorbent and waste cotton cloth(AC1) which is cellulosic fiber rich in carbon content was used to prepare Activated carbon The treated water was found to be in excellent reduction level of various pollutant level as follows, the level of TSS showed 94.5% &amp;BOD 95.4% ,COD 96.6% was reduced for the sample 1 . The pollutant due to the usage of detergent, that is the Methylene blue Anionic substances level also reduced from 1.8 mg/lit to 0.2 mg/lit of about 88.89%by prepared activated carbon(AD1).and very good reduction level of Oil and Grease was resulted by AD1,AD2,AC2 99.5%, 98.7%, 99.1% It is evident from the result that the waste cloth material and the un wanted growth of Prosopis juliflora which has been insisted to cut down of save underground water could be used in another way for treating waste water and and to solve the problem of water management and supply.
Hybrid Technique for Effective Knowledge Representation in Normal Life

Knowledge is essential for daily life. People can gather knowledge from their routine work like reading newspaper, watching T.V, discussions, experience and many more are the source of knowledge. An artificial intelligent (AI) system must be able to do the same. So there is a requirement of effective knowledge representation (KR) technique that is able to represent the knowledge from above defined sources and works for daily life. An inference mechanism is also required for reasoning the knowledge from artificial intelligent system. The purpose of this paper is to present the implemented conceptual view of hybrid KR technique that works for representing the knowledge required for daily life.
Enhance the Security of Data Storage and Retrieval in Cloud Computing Through Authentication Based Encryption

The digital computing infrastructure is rapidly moving towards cloud based architecture. The protection of data is becoming a difficult task in the current scenario as more and more confidential and sensitive data is stored in cloud environment and transmitted between cloud users. In a cloud computing environment, the entire data reside over a set of networked resources of remote servers and locations. These data has been accessed by unauthorized cloud users through virtual machines. To provide additional level of cloud data security, Biometric based authentication with encryption using public key cryptography is proposed in this paper. The proposed model Authentication Based Encryption (ABE) helps to enhance the security of data as well as the authentication of cloud user. The sensitive data is initially encrypted and then stored secretly with biometric finger print image. The resultant image is transmitted through the in-secured channel. However to avoid unauthorized access, the image is decomposed and stored in cloud separately as encrypted message and finger print. Before beginning the decryption process, the finger print of the cloud user is being compared with the stored image for authentication. If the match is found, the encrypted data is decrypted by the authenticated cloud user. Otherwise access to the data is denied to ensure security. Thus, the proposed framework provides an additional level of protection to public key algorithm with authentication.
Impact of Opposition Intensity on Improved Cuckoo Search Algorithm for Privacy Preservation of Data

Nowadays, large amounts of data are stored and retrieved frequently in day-to-day life.The data stored in the system may contain sensitive data which necessitates the implementation of preserving privacy in big data.For this reason, Privacy-Preserving Data Mining (PPDM) models are emerged to handle the privacy problems by avoiding unauthorized access and misuse if sensitive data.In current years, a lot of researches were implemented to handle the preservation issues.However, the challenges due to large data and computationally expensive issues limit the performance of PPDM approaches.Thus, this paper plans to implement a new PPDM model with two stages such as data sanitization and data restoration.In both the sanitizing and restoring process, key extraction is a major process that is optimally selected by means of modified CSA approach called Opposition Intensity-based Cuckoo Search Algorithm (OI-CSA).Finally, the performance of the proposed model is analyzed by varying co to 0.2, 0.4, 0.6, 0.8 and 1, respectively.
Actual Interactions for Component-Based Software using Straight and Circuitous Links

Interaction and integration complexities of various pieces of codes play a vital role in the overall behavior of software. As the code count increases the interaction level of software also increases as per the requirements of the software. In this paper we propose a metric to assess the actual number of interactions made by components in component-based software. On the basis of interactions among components we define an Interaction-graph. Interaction-graph contains ‘Links’ and ‘Components’. To assess the actual interactions we define inner and outer interactions of particular components. Links are further categorized as straight and circuitous links. Proposed interaction metric is easy to calculate and contains information about the component which is used by designers and developers of the component-based software for future development.
React-bratus: Visualising React.js Component Hierarchies

The ninth IEEE Working Conference on Software Visualization (VISSOFT 2021) builds upon the success of the previous eight editions of VISSOFT, which in turn followed after six editions of the IEEE International Workshop on Visualizing Software for Understanding and Analysis (VISSOFT) and five editions of the ACM Sympo0sium on Software Visualization (SOFTVIS). In 2021, VISSOFT will take place and be co-located with ICSME.
Give students ownership of credentials with blockchain technology

When the enrollment services division at Central New Mexico Community College started looking at digital services for diplomas, transcripts, and other credentials, they were guided by the institution's strategic plan. One of the plan's priorities was to shift from college-owned technology to student-owned technology, said Feng Hou, chief information officer and chief digital learning officer.
Technique to Generate the Secure Symmetric Key Under Variable Keys Environment using Genetic Algorithm

The purpose of information security is to protect information or data from misuse, unauthorized access and also to ensure the secured communication between transmitter and receiver. In this regard, one of the primary and foremost necessity is to protect the key by any means and should also be unbreakable. In this context, a mechanism namely, Automatic Variable Key (AVK) has been introduced to maintain the secrecy of the key. However, in this approach the initial key must be established earlier, then only it changes the keys and make unpredictable to guess, for every new block of data transmission. Thus, to overcome from this extra burden of initial distribution of key, we propose a new technique using Artificial Intelligent (AI) in order to generate the initial key automatically using the Genetic Algorithm (GA). We have demonstrated the importance of AI in the area of cryptography by our scheme. A comparative study is also carried out with the existing schemes to prove the proficiency of proposed scheme. Thus, in order to prove the unpredictability among the auto-generated keys, we have verified the randomness with the help of National Institute of Standards Technology (NIST) Test suite.