Reviewerly/exHarmony huggingface dataset:
A Heuristic Load Balancing Algorithm for Cloud Computing in Heterogeneous Resource Environment

Abstract Cloud computing is an alternative technology to develop web-based services. The continuous increase of internet users causes high web service traffic that leads to the requirement of good infrastructure in managing servers in cloud computing. Load balancing between servers is one of the main challenges in cloud computing across multiple nodes. The objective is to ensure that no single resource is overwhelmed nor underused. Load balancing is optimal when maximizing throughput, minimizing response times, and avoiding overloading on one of the connection lines. To solve problems in cloud computing infrastructure, we propose the utilization of two well-known heuristic algorithms to distribute loads on cloud computing. Experimental results indicated that the heuristic algorithm could distribute task loads proportionally to cloud computing resources. Our heuristic algorithm can also distribute tasks to resources with different computing resource specifications and different tasks weights.
Risk Management and Agile Project Management

The previous chapter explicated the components of the project plan from the traditional project management (TPM) standpoint. In this chapter we explore the risk management system while linking this to the concept of Agile in managing projects. The rationale rests on the bedrock of project success which is directly proportional to the effectiveness of the risk man- agement system. The higher the uncertainty about the event scope, the more likely the management team will face a real challenge trying to develop an effective risk management system following the TPM approach. The first part of this chapter explicates the main components of the risk management system from TPM perspective. The basic concepts of Agile Project Manage- ment (APM) are then presented and discussed in the context of event man- agement. The chapter wraps up with some useful projections justifying the claims that APM tools and techniques can be utilized to proactively manage risk in the context of event thus enhancing the overall success of the event.
Prediction of Presence of Breast Cancer Disease in the Patient using Machine Learning Algorithms and SFS

Breast cancer disease is recognized as the common extensive malignant tumor in between women. Identification of the initial stage of malignant growth may treatment of this disease. Early treatment helps to alleviate the disease and helps anticipate its recurrence in women. Experts have used some fact checks and different medical methods or equipment to improve the accuracy of conclusions in clinical medical service management. In this article, it extensively discussed the implementation of data mining strategies to detection as well as prediction of breast malignant tumors, including random forest (RF), support vector classifier (SVC), k-nearest neighbors (KNN), linear discriminant analysis (LDA), Gradient Boosting Classifier (GBC), Decision Tree (DT) In addition, principal component analysis (PCA) to underline changes and show strong patterns in the informational index. The connection framework is likewise used to show the level of close relationship between attributes. The sequential feature selection (SFS) method is used for comparing the accuracy of a data set with all features and the accuracy of a classifier with selected features. The results show thatRF_sfs, KNN_sfs, SVC_rbf and SVC_sfs have the highest and equal accuracy, which is 97.66%. They perform well and can predict the growth of harmful malignant tumors.
Forward-Secure Edge Authentication for Graphs

Abstract The edge authentication of graphs has been studied in the literature because graphs are one of the most widely used data organization structures. The majority of such schemes cannot be used to authenticate general directed graphs (GDGs); other schemes cannot be used for addressing either the issue of dynamic update or the issue of information leakage (such as the existence of nodes/edges and structural relationship of the graph). Also, all the existing schemes do not consider the forward security: if the signer’s secret key has been compromised, all previously generated signatures remain valid. This property provides high-level security protection for authentication schemes. To address these issues, in this work, we propose a forward-secure edge authentication scheme for GDGs. Observe that existing such schemes can only give a proof such that ‘there is an edge between nodes $u$ and $v$’. Our scheme, however, can directly give a proof such that ‘there is no edge between nodes $u$ and $v$’, which makes the function of edge authentication schemes more diverse. Moreover, our proposed scheme is proven to be secure against an adaptive chosen-message adversary in the random oracle model. To show its desirable performance, we analyze the computational costs of our scheme and compare it with other related schemes in terms of features.
Blockchain Utility in Use Cases: Observations, Red Flags, and Requirements

On a global scale blockchain is persistently proposed in thousands of use cases by corporates, governments, and academics. However, there is a lack of systematic evaluation of these use cases and the utility of blockchain. In this work we systematically evaluate fifteen use cases that use blockchain. Based on our evaluation we observe six recurring problems in these use cases. These problems either relate to the utility of blockchain in the use case, or to how well-documented a use case description is. We point out four red flags that, whenever they occur in a use case description, signal that blockchain may be a sub-optimal solution for that use case. Notably, one of these red flags indicates that there are no clear requirements in the use case descriptions that warrant the use of blockchain. We address this by proposing a set of requirement templates for any use case that includes a transaction system.
Research on Reversible Date Hiding Algorithms Based on Bilinear Interpolation about Watermark

In this paper, a reversible method of information hiding based on difference histogram and bilinear interpolation is proposed, which improves the embedding capacity of the information and reduces the distortion degree of the embedded image. According to the theory of histogram shift, the gray value of pixels is modified at the zero point or the minimum point of the histogram, and the information is embedded into the image. The adjacent pixels are interpolated, and the array is constructed with bilinear interpolation, and the information is embedded on both sides of the peak value. Besides, the overflow of the pixel value of the image is also solved. MATLAB simulation software was used to embed and extract the information of the carrier image and to restore the original carrier image. The experimental results show that the algorithm in this paper had little influence on the carrier image after embedding the watermark information, and the PSNR value is above 52dB.
Document Searching of EPPS Test Result Using Indexing Method

Abstract This study focuses on documents searching on the EPPS test result, mainly administered in the education sector for selecting prospective students in universities’ admissions process. There has been a considerable increase of admission data along with annually administered admission test for prospective students in every beginning of an academic year. Some faculties may administer the EPPS test in the second stage test of admission process after the prospective students pass the first stage in addition to having the general admission test. These several tests lead to a huge data accumulation with different information, making it difficult for the organizers to locate the document containing 15 aspects of required information in a comprehensive information display. Thus, it is necessary to apply an indexing method by having a predetermined index to retrieve required information in a particular document or to find other related documents by way of indexing method. This research used 74 pdf documents as the dataset. In particular, 60 datasets were used as references to retrieve phrases on the specified data, and 14 datasets were used to test data. The results revealed the most significant data with an average precision of 0.89, recall of 0.93 and f-measureof 0.91.
A Methodology to Retrieve Information from Ontologies with the Application of D2R Mapping and SPARQL

There exists enormous amount of information hidden on the internet. Numerous methods have been evolved in order to retrieve the quality amount of information from web. This requires executing complex queries that may also bring irrelevant results. Inorder to overcome such difficulties, we must adopt a method to retrieve information from meaningful data that is structured. For this researchers are working on ontology- based query processing where retrieved information is more appropriate to user’s interests. This paper proposes a methodology that maps hospital patients databases to ontologies and information retrieval is performed.
Data Science, COVID-19 Pandemic, Privacy and Civil Liberties

The world has seen pandemics, terrorism, hurricanes and other natural and man-made disasters.Each time such an event occurs we discuss technologies that can solve the problem and their impact on our privacy and civil liberties.Such discussions occurred after the 9/11 terrorist attacks and is happening now during the COVID-19 pandemic, the worst human crisis we have faced in a century.This paper discusses the applications of data science to detect and possibly prevent such pandemics and its impact on our privacy and civil liberties.
Towards a Multi-Perspective Methodology for Big Data Requirements

This poster describes work in progress that is concerned with requirements engineering in the context of big data. Drawing experience from a H2020 project, this poster argues for a classification of requirements from different perspectives that can be used to guide the requirements elicitation process and form the basis of a common methodology for requirements engineering in big data applications.
Social entrepreneurship in Saint-Petersburg as green economy aspect

Identification of the prospects for the social entrepreneurship expansion as one of the aspects of green economy uses the example of St. Petersburg. We use a statistical method for processing nominal data with the aid of SPSS. The study revealed that social-oriented not-for-profit organizations and government social agencies work with the same social groups and offer similar services. Therefore, to increase the competitiveness of not-for-profit organizations, these organizations should provide services at a high client-oriented level. We found that because of the lack of permanent sources of funding, the problem of fundraising exists. The results of the current study are applicable for improvement of commercial and social practices of social-oriented not-for-profit organizations, in the expansion of services for the additional professional education of such organizations’ personnel, in the development of practices in fundraising and crowdsourcing, in improving applications for grant support from the state, in the diversification of services. We revealed that social-oriented not-for-profit organizations and state social agencies work with the same social groups and offer similar services; we found that the problem of fundraising becomes a priority for this organizations; personnel in these organizations has different education; the representatives of not- for-profit organizations understand the purposes of social entrepreneurship.
Neural-Network-Based Detection Methods for Color, Sharpness, and Geometry Artifacts in Stereoscopic and VR180 Videos

Shooting video in 3D format can introduce stereoscopic artifacts, potentially causing viewers visual discomfort. In this work, we consider three common stereoscopic artifacts: color mismatch, sharpness mismatch, and geometric distortion. This paper introduces two neural-network-based methods for simultaneous color- and sharpness-mismatch estimation, as well as for estimating geometric distortions. To train these networks we prepared large datasets based on frames from full-length stereoscopic movies and compared the results with methods that previously served in analyses of full-length stereoscopic movies. We used our proposed methods to analyze 100 videos in VR180 format-a new format for stereoscopic videos in virtual reality (VR). This work presents overall results for these videos along with several examples of detected problems.
A Safety Stick for Elders

Technology in recent decades is developing day-by-day in such a way that even a small device is equipped with many features. In this paper, we provide a detailed prototype of a safety stick that helps the elders to lead their life independently. Most commonly elders use a walking stick for their support, we are going to make that normal walking stick into a smart safety stick. Here, we use IoT and embedded technology. In an emergency like when they need medical help, by long-pressing, a simple button implemented walking stick the alert message is sent to the caregiver along with location information. Also, when in the threat of thieves they can use the same stick for their protection by pressing simple click it will give a shock to the thief and also a message is sent to caregiver simultaneously and even the neighbors with a siren sound. This upgraded smart stick makes aged people feel safe, secure, and independent.
An Implementation of Text Mining Decision Feedback Model Using Hadoop MapReduce

A very large amount of unstructured text data is generated everyday on the Internet as well as in real life. Text mining has dramatically lifted the commercial value of these data by pulling out the unknown comprehensive potential patterns from these data. Text mining uses the algorithms of data mining, statistics, machine learning, and natural language processing for hidden knowledge discovery from the unstructured text data. This paper hosts the extensive research done on text mining in recent years. Then, the overall process of text mining is discussed with some high-end applications. The entire process is classified into different modules which are test parsing, text filtering, transformation, clustering, and predictive analytics. A more efficient and more sophisticated text mining model is also proposed with a decision feedback perception in which it is a way advanced than the conventional models providing a better accuracy and attending broader objectives. The text filtering module is discussed in detail with the implementation of word stemming algorithms like Lovins stemmer and Porter stemmer using MapReduce. The implementation set up has been done on a single node Hadoop cluster operating in pseudo-distributed mode. An enhanced implementation technique has been also proposed which is Porter stemmer with partitioner (PSP). Then, a comparative analysis using MapReduce has been done considering above three algorithms where the PSP provides a better stemming performance than Lovins stemmer and Porter stemmer. Experimental result shows that PSP provides 20–25% more stemming capacity than Lovins stemmer and 3–15% more stemming capacity then Porter stemmer algorithm.
An HPSG approach to Chinese syllable structure and tone sandhi

In Head-driven Phrase Structure Grammar (HPSG), the phonological module has been strikingly underinvestigated compared to other parts of the grammar. To remedy this situation, the aims of this study are both theoretical and empirical. We employ revised Typed Feature Structures (TFSs) to describe a number of phonological structures in Mandarin Chinese, mostly concerned with syllable structure and tone, based on the X-bar approach to the syllable structure of Mandarin (van de Weijer, 2012, van de Weijer and Zhang, 2008, Zhang, 2008). Specifically, we integrate tone features in the HPSG framework, and analyze tone sandhi (TS) by way of TFSs and constraints. We demonstrate that the enriched definitions and constraints using TFSs in phonology, like in morphology and syntax in HPSG, can successfully capture the relevant phonological generalizations and are therefore a step on the way toward a more comprehensive HPSG account of Mandarin grammar, illustrating its advantages such as simplicity, constructional consistency, modularity and facilitating interface description.
Image Degradation by Zeroing Block DCT vs. Full DCT Coefficients

As Discrete Cosine Transform (DCT) has been applied widely in image processing, it has been extensively studied in recent years. It is an interesting research topic to reveal the processing effect of block DCT and full DCT on the same image. In this paper, it is found that the block size of DCT is directly related to the impact of different DCT coefficients on an original image. To be more specific, keeping the proportion of zero coefficients unvaried, the distortion level of the image increases as the DCT block size decreases, appearing more and more uneven ripples. In contrast, the same operation by full DCT causes less distortion to the image. Additionally, with a constant block size, a larger percentage of zero coefficients results in a greater distortion. For the above results, this paper studies further about the mathematical relationship between PSNR value and the percentage of zeroing DCT coefficients.
Protection of End User’s Data in Cloud Environment

Disseminated registering spared and on-demand cases are all things considered given by master associations. Hybridization of the two alternatives can cost essentially extra when renting resources from the cloud. Nevertheless, it is a noteworthy test to choose the fitting proportion of held and on-demand resources to the extent customers’ essentials. In this paper, the work procedure booking issue with both held and on-demand models is considered. The objective is to constrain the total rental cost under due date obliges. The idea about issue is numerically illustrated. A different progression based most prompt culmination time procedure is proposed to assemble plans for the work forms. Four interesting standards are used to create beginning errand circulation game plans. Types and measures of benefits are settled by an accessible time square-based schedule advancement segment. New groupings are made by a variable neighborhood search strategy. Exploratory and real examinations and results show that the proposed figuring produces broad cost venture finances when stood out from the estimations with just on-demand or spared models.
An Overview of Automatic Speaker Recognition in Adverse Acoustic Environment

In the last 50 years, the task of automatic speaker recognition has undergone intense development and is still a popular topic. New ways are also being sought to implement speaker recognition tools in order to improve the educational process. These include support systems and services, such as recommendation and personalization-based content delivery, personalized speech assistant for human-computer interaction, or improved speech recognition systems based on speaker adaptation. Applications in the real environment stress upon technological issues such as dealing with acoustical environment variability, background noise, simultaneous speech or speaker's behaviour. The article provides a review of the state-of-the-art techniques for automatic speaker recognition with focus on its deployment in real and adverse acoustic conditions.
Institutional Support for Data Management Plans: Five Case Studies

Researchers are being prompted by funders and institutions to expose the variety of results of their projects and to submit a Data Management Plan as part of their funding requests. In this context, institutions are looking for solutions to provide support to research data management activities in general, including DMP creation. We propose a collaborative approach where a researcher and a data steward create a DMP, involving other parties as required. We describe this collaborative method and its implementation, by means of a set of case studies that show the importance of the data steward in the institution. Feedback from researchers shows that the DMP are simple enough to lead people to engage in data management, but present enough challenges to constitute an entry point to the next level, the machine-actionable DMP.
Breast Cancer Prediction Using Enhanced CNN-Based Image Classification Mechanism

The research has focused on breast cancer prediction using enhanced convolution neural network (CNN). The data set related to breast cancer has been considered during this research. The convolution neural network implementation has been made to predict breast cancer. CNN mechanism classifies image and breaks it down into features, reconstructed and predicted at the end. The edge-based samples have been considered to reduce the comparison time and space. This results in an increased accuracy. The introduction section introduced basic concepts of breast cancer prediction system. The existing researches in a relevant field have been represented in the second section. The motivation and challenges to research have been explained afterward. Later proposed work and results are representing the simulation of work. Simulation results have shown that edge-based image processing in the convolution neural network has reduced the time and space. The accuracy has been also increased.
Fpga Based Multi-Robot Collision Avoidance System

Abstract This paper presents a Multi-robot collision avoidance system suitable for FPGA implementation. The proposed scheme attempts a novel method of Reciprocal Collision Avoidance(RCA) along with Accelerating Velocity Obstacles(AVO). Parallel architecture is used for developing the collision analysis of robots with different targets. Each of these robots moves and reaches its target, avoiding collisions on its path. Simulations for the digital design hardware have been carried out in Vivado software and the robotic movements have been demonstrated using V-rep software. The proposed scheme would result in reduced cost and power dissipation while deploying in FPGAs. The details of the collision detection algorithm and the working of different modules used in the scheme along with test results are presented in the paper.
Recognition of Hate or Offensive Tweets in the Online Communities

The paper focuses on classification of text into categories as hate speech or offensive language which represent unhealthy phenomena complicating learning and communication in online space. This classification was achieved by training a model using a deep neural network. The network was tested with different amounts of neurons in the hidden layer, with three distinctive optimizers and with various learning rates.
Adaptation of a Refactoring DSL for the Object-Oriented Paradigm

Many development environments offer refactorings aimed at improving non-functional properties of software, but we have no guarantees that these transformations indeed preserve the observable behavior of the source code they are applied on. An existing domain-specific language makes it possible to formalize automatically verifiable refactorings via instantiating predefined transformation schemes with conditional term rewrite rules.&#x0D; We present a proposal for adapting this language from the functional to the object-oriented programming paradigm, using Java instead of Erlang as a representative. The behavior-preserving property of discussed refactorings is characterized with a multilayered definition of equivalence for Java programs, including the conformity relation of class hierarchies. Based on the decomposition of a complex refactoring rule, we show how new transformation schemes can be identified, along with modifications and extensions of the description language required to accommodate them. Finally, we formally define the chosen base refactoring as a composition of scheme instances.