Reviewerly/exHarmony huggingface dataset:
Efficient Implementation of Big Data Access Control Scheme with Privacy-Preserving Policy

In the time of big data, cloud computing, an immense measure of information can be created rapidly from different IT, non-IT related sources. Towards these big data, cloud computing, customary PC frameworks are not up to required skilled to store and process this information. Due to the adaptable and flexible figuring assets, distributed computing is a characteristic fit for putting away and preparing big data. With cloud computing, end-clients store their information into the cloud server and depend on the advanced cloud server to share their information to different clients. To share end-client's information to just approved clients, it is important to configuration access control systems as indicated by the prerequisites of end clients. When re-appropriating information into the cloud, end-clients free the physical control, virtual physical control of their information. In addition, cloud specialist co-ops are not completely trusted by end-clients, which make the entrance control additionally testing. on the off chance that the conventional access control systems (e.g., Access Control Lists) are connected, the cloud server turns into the judge to assess the entrance approach and settle on access choice. Subsequently, end-clients may stress that the cloud server may settle on wrong access choices purposefully or accidentally and uncover their information to some unapproved clients. To empower end-clients to control the entrance of their own information, a proficient and fine-grained huge information access control plot with protection saving strategy is proposed. In particular, the entire trait (as opposed to just its qualities) in the entrance strategies are scrambled. To help information decoding, encoding, a novel Attribute Bloom Filter is utilized [14][16] to assess whether a characteristic is in the entrance arrangement and find the accurate position in the entrance approach on the off chance that it is in the entrance strategy. Just the clients whose traits fulfill the entrance arrangement are qualified to unscramble the information.
Fitness-Orientated Mutation Operators in Genetic Algorithms

Genetic Algorithms are the most popular metaheuristics used in search-based program solving. They search for a solution by repeating the following operators: selection, crossover, and mutation. By going through several generations of these operators, a solution is reached. The total number of generations depends on the reproduction of offspring. Of the reproduction operators, the mutation operator tends to be chosen with a random approach because the concept of mutation is from the natural evolution cycle. But the effectiveness of genetic algorithms should be monitored, especially in software testing. The effectiveness is represented by the total number of generations, which corresponds to the speed of solution acquisition. This work focuses on mutation as a factor in reducing the total number of generations and devises two contrasting ways to define mutation operators. One is fitness-positive, and the other is fitness-negative. The fitness-negative definition thus appears to fit more aptly. This work determines which of these two methods of mutation achieves the higher effectiveness through conducting a controlled experiment. The result shows that the fitness-positive method takes a smaller number of generations than the fitness-negative method.
Characterization of a Mobile Waste-Robot: A Heuristic Method to Path Planning using Artificial Neural Networks

In the field of mobile robotics, path planning is one of the most widely-sought areas of interest due to its nature of complexity, where such issue is also practically evident in the case of mobile robots used for waste disposal purposes. To overcome issues on path planning, researchers have studied various classical and heuristic methods, however, the extent of optimization applicability and accuracy still remain an opportunity for further improvements. This paper presents the exploration of Artificial Neural Networks (ANN) in characterizing the path planning capability of a mobile waste-robot in order to improve navigational accuracy and path tracking time. The author utilized proximity and sound sensors as input vectors, dual H-bridge Direct Current (DC) motors as target vectors, and trained the ANN model using Levenberg-Marquardt (LM) and Scaled Conjugate (SCG) algorithms. Results revealed that LM was significantly more accurate than SCG algorithm in local path planning with Mean Square Error (MSE) values of 1.75966, 2.67946, and 2.04963, and Regression (R) values of 0.995671, 0.991247, and 0.983187 in training, testing, and validation environments, respectively. Furthermore, based on simulation results, LM was also found to be more accurate and faster than SCG with Pearson R correlation coefficients of rx=.975, nx=6, px=0.001 and ry=.987, ny=6, py=0.000 and path tracking time of 8.47s.
Design of Automatic Braking System Using Interval Type-2 Fuzzy Logic System

The car braking system is a very important part of car safety.The effectiveness of Conventional braking system is strongly influenced by the ability and the driver's experience.It is very vulnerable to accidents if the driver fails to operate the braking system.Because of that, an automation of the braking system is needed that can decrease the accident's impact.The Automatic braking systems require the reasoning to manage information before making a decision that brakes or slows down.There is uncertainty in determining membership function of distance and velocity.It is difficult to determine an exact membership function.In this paper, the system used Interval type-2 fuzzy logic control that can work well in an unstructured environment and have the ability to overcome the uncertainty of information.The result is that the pressure on the brake pads is gradually increased with an average increase of 5.6%.At a distance of 34 m, the pressure is at maximum value.
Geological and Structural Positions of Ore Fields and Deposits of Ore Area Auminzatau-Beltau

Review the examination geologic-structural position gold mine location of Auminzatau-Beltau is mine of district Central of Kizilkum. Everybody location placement in seven structural of position formations north-lasterly, northwesterly, full-scale systems fractures and theirs is combine Find inosreslarch recommended using how criterion search gold mines manifestation.
Video Content Encryption as a Service and Performance Implications of Video Data Encryption on Cloud

The immense growth in the video content retrieval and video content analysis have motivated the practitioners to migrate the video contents and the analytic applications on to the cloud. The cloud computing platform provides scalability for applications and data, which enables the application owners to deal with complex algorithms, which is needed for video content analysis and retrievals. The primary concern of the video data retrieval on cloud services are the weak security for the standard data during migrating from one VM to another VM. Also, the standard encryption algorithms have failed to demonstrate higher performance during encryption of a large file. Hence, the demand of the recent research is to ensure reduced performance implications for video content encryption over cloud services. This work proposes an adaptive encryption and decryption algorithm for large video data over cloud as Encryption as A Service (EAAS). This work proposes a novel key age calculation dependent on Quartic Polynomial Randomization. The quartic part utilized in the proposed calculation can produce numerous defining moments, which makes the calculation results hard to foresee and the utilization of polynomial randomization can further build the haphazardness of the defining moments. Likewise, the higher size of the video information must be diminished without rotting the data and without trading off the security. Subsequently, this work proposes a novel key edge comparability extraction procedure utilizing versatile movement. The similitude areas in the key casings contains comparable data and, in this manner, can be scrambled all around. This diminishes the time unpredictability to a more noteworthy broaden. Associated with the comparable line of advancement, this work likewise proposes time limited encryption and unscrambling calculations, which can separate between the comparable and unique areas and decrease the time intricacy further. The proposed algorithm demonstrates nearly 40% improvements over the standard encryption algorithms.
Research of Task Management and Resource Allocation in Cloud Computing

This paper presents the task management framework in which tasks are identified, analysed and grouped based on the user QoS requirements. As a next step, cost and time based scheduling algorithm is employed to select the suitable pair of resources and tasks. The performance of the proposed algorithm is tested with execution time and cost allocated to the resources. Two existing algorithms are considered along with proposed algorithm for performance comparison. The results proved the efficiency of the proposed algorithm.
A Sigmoid based Learning in Heterogeneous Distortion for Data Privacy

Neural network-based learning models along with an access to huge data have made a remarkable outcome in recent years. These models are contributing a lot to improvise the working dimensions of various domains like Speech recognition, Image processing, Text analysis and many more. The well represented data is the main resource in the current research, but this data is often privacy sensitive and it definitely needs a proper attention failing which leads to serious privacy concerns. The proposed work demonstrates how learning models can be applied to analyze the data sensitivity and classify them to various privacy classes. Once the privacy class distribution is performed the model applies Inverse laplacian query model to check the data utility. The data should not get compromised on utility with the curse of privacy. With this intention the given experimental study succeeded in training the network to perform privacy analysis under a modest privacy budget, complexity training efficiency and data utility.
Health Adviser: Social Question and Answer System using Datamining

Question and answer systems play a crucial role in providing answers to users that seek it, primarily in the case of non-factual questions. A Social Q&amp;A system aims to integrate properties of social networks like similar interests of the users and mutual trust between within a set of friends which makes the reliability of certain answers more prudent. In simple terms, it aims to find a expert that can provide the right answers, ideally within the user’s social circle. Our project endeavors to serve pregnant mothers in answering their private or non-private questions regarding their pregnancies. Our system provides a platform for the to-be mothers to ask their questions to experts who can suggest suitable advises for their benefit. The user also has the added advantage of accessing information from experts who belong to their social circle thereby reducing the anonymity of the expert. We have compared the accuracy of few machine learning algorithms that includes Decision Tree (DT), Support Vector Machine (SVM), K-Nearest Neighbor kNN), Logistic Regression (LR), ZeroR (the baseline classifier). Our model has an accuracy of over 75%, while demonstrating robustness across learning algorithms.
Modern with Advanced Direction in Green Cloud

As the Multiple Clouds or Cloud are very flexible and very vast field of data with the correspondence advantages. But as the traffic loads are increasing day by day, which directly hits on the speed of cloud computing. Hence, Green Cloud is added with cloud computing to maintain the traffic load balancing, Cloud computing is introduced with (ICTs) to face advance challenges for the cloud platform and general securities. Cloud computing advancements have an assortment of use spaces, since we are getting solid versatility, reliability and superiority at general minimal efforts. The Cloud computing upheaval is upgrading current systems administration like Cloud computing implement it’s on tool and merge to take the advantages of Green cloud, and also offering promising natural security prospects just as monetary and innovative favorable circumstances. These advances can possibly improve vitality productivity and to decrease carbon impressions and e-squander. Cloud computing can be changed by this type of highlights into green Cloud computing. After deep study of Green Cloud, It has been founded that Green Cloud should be audit by the primary accomplishments. I have worked in the paper to turn in the modern direction to improve the old research, so that cloud computing take more or latest advantagesby the use ofgreen cloud, and the clients can work smoothly without facing said issue.
Sentiment Analysis using Artificial Neural Network

In the modern era, there are massive amount of web resources present such as blogs, review sites and discussion forums. These resources form the platform where users can share their opinions or reviews` about anything whether it is a product, movie or a restaurant. Analysis of public sentiments deals with the determination of the polarity of different public opinions or reviews into either the category of positive, negative or neutral. Thus, there comes the need of sentiment analysis which not only helps other individual to make a decision regarding buying a product, visiting a restaurant or watching a movie but also helps the producers of various products and owners of different restaurants to gain the knowledge of preferences of customers, so that it could be possible to increase the profit and economic value. The paper presents a survey with main focus on performance of different artificial neural networks used for opinion mining or sentiment analysis while it also includes various machine learning approaches such as Naïve Bayes, Support Vector Machine, lexicon-based approach and Maximum Entropy.
Development of knowledge based sentiment analysis system using lexicon approach on twitter data

In today's digital world, knowledge management systems aim at extracting semantic and knowledgeable information by examining natural language impressions. Social networking sites such as Twitter are becoming an integral part of our daily lives, contributing to a large increase in the growth of knowledgeable data. It is one of the sources of information especially for detecting interest of people. So, in this paper we propose to use a lexicon based approach to analyse knowledge based sentiment from tweets. We use a set of words with pre-defined polarity model to pick out polarity words with semantic scores assigned to them by classifying them using Naïve Bayes classification, and incorporates part of speech tagging. We then incorporate Natural Language Processing techniques on the resulting score to further improve the accuracy. We are also proposing additional knowledgeable sentiment detection using features like emoticons to assess sentiment. The proposed model achieved a minimal error rate of 0.36 and accuracy of 97% when compared to other similar methods.
A Novel Security based Image Watermarking using NBP-IWT-DCT-NSVD-GA-PSO Algorithm

In this novel technique, a modified singular value decomposition named normalized singular value decomposition (NSVD) used for select the original image features to embedding the watermark image into these features. So, the quality of the original image won’t be affected. To select the Normalization Constant of NSVD, the optimization technique used is Genetic Algorithm (GA). In embedding stage, Particle Swarm Optimization (PSO) is used to optimize watermarking constant. Instead of these preliminaries the novel approach also used normalized block processing (NBP) to make the watermarked image more robust to rotation and flipping attacks. The various experiments are conducted on the novel approach to estimate the performance. The experimental results achieved good Robustness for most of the attacks compared to conventional approaches.
Design of Programmable Pixel Shader Computing Unit

With increased focus on improved Graphics, the gaming industries for computer and mobile devices requires more complex graphical algorithms, which in need of better computational resources. In general graphical computation requires more computational units, which will be major constraint in mobile devices as it demand for more power. In order to develop such efficient computational units, one need to focus on the Graphical pipeline to have better visual effects. In this paper design of programmable Pixel shader computing unit is discussed. In GPU, the Pixel shaders are used to process and manipulate the each pixel. Also the Pixel Shaders are used to provide final coloring to the processed data. Here the programmable Pixel shader computing unit design is discussed, which help the programmer to add his own code for achieving better visual effects or results. The possible Instructions of Pixel shader are designed using Verilog HDL. The SIMD(Single Instruction Multiple Data) concept is adopted for the designing of each instruction.
Design and Implementation of Component based Metric for Software Complexity Measurement

This paper designs a single component based metric to measure the complexity of any software in any phase of software development life cycle. The metric is designed on the basis of existing coupling and cohesion metrics like normalized hamming code (NHD), lack of cohesion in methods (LCOM), conceptual coupling(CoCC), structural and semantic coupling metric(SSCM). The designed metric also covers the coupling between parent and its inherited class, static import, anonymous class contribution and the coupling between inner and outer class to analyze the complexity of software precisely. The analysis of the metric has been done on seven industrial and academic projects against existing state of art coupling and cohesion metric i.e. NHD, COCC, SSCM, LCOM5 and method attribute cohesion metric. The result and analysis shows the significance of the designed metric.
Using Natural Language Processing to Search for Textual References

This paper explains how recent advances in NLP technology can be harnessed to search for allusions and influences. The algorithms that have been used in several recent projects in the Digital Humanities are briefly analyzed with the aim of determining how effective these algorithms are in detecting the variety of reference forms that were used in ancient literature. Using the lessons learned from this analysis, a generic NLP algorithm is presented, including a set of syntax rules for textual references. The algorithm is designed to be generic so that it can be used to detect any type of textual reference in any type of text (or even an oral allusion to an oral speech). Finally, the benefits of this approach are highlighted using the results from testing the algorithm on the Pastoral Epistles.
Analyzing the Students Behavior with Down Syndrome in a Gamified Learning Environment

The main purpose of this study was to analyze the behavior of students with Down syndrome in a gamified learning environment of SPED school in Baguio City, Philippines. Education focused in the area which uses unique instructional methods, materials, learning aids, and toolkit to meet the educational needs of students with learning incapacities. This study investigated the extent to which a sample of special education teachers addressed various topical issues with students who have learning disabilities and behavior disorders. In collecting data, a survey instrument was employed to gather information pertaining to topical issues that involve sensitive subjects typically not addressed in the traditional academic curriculum in school. The authors also utilized literary studies, and observation, draws together the findings from a series of interviews, and applies these in an educational context. The data were analyzed with psychological, educational, and socio-cultural approaches, with qualitative descriptive analysis. Down syndrome (DS) is the most common genetic cause of intellectual disability worldwide, with language being one of the most affected area. The authors hypothesized factors that contribute to engagement with a learning activity and strategies and provide an example of the use of these factors in practice, and other hypotheses were supported by data. Based on the rating, the authors motivated to enhance the functionality of the Gamified Learning Environment (GLE) in the future. The target user strongly agrees with the ratio of 4.6 which means that the GLE has the functionality needed in their school course and has a greater impact to their academic performance.
TWTRENDS — A VISUALIZATION SYSTEM ON TOPIC MAPS EXTRACTED FROM TWITTER TRENDS

Twitter provides a list of trending topics, a list of social trends or issues that users of the social networking services (SNS) will be interested in at a given time.However, they are presented as a list of key phrases making it challenging to identify or infer the message of the trending topics.In an attempt decipher the meaning and the structure hidden in the list of trending topics provided by Twitter, we developed a system to visualize topic maps according to the co-occurrence structure of typical tweets that mention the trending topics.This paper describes an overview of the system structure, the main logic to construct topic maps, and a discussion on several findings from its operation between January 1 and June 30, 2019.Also the possibilities of modification to suit the system for the other languages is discussed.
The Role of Maturity Driven Software Process Improvement in an Industry

In the current industrial environment, big organizations have adopted maturity driven process improvement efforts.Most of these efforts have been inspired of maturity models like the CMM (Capability Maturity Model) and been guided by the area of Software Process Improvement (SPI).The maturity of an organization's processes is measured through its maturity level.An organization at high maturity level, means, that it possesses mature processes and it is more trust worthy.In this perspective for an ad hoc organization with no experiences of doing SPI and CMM the efforts have mostly been prolonged, expensive, and not often delivered the effects back to the organizations in the same dimension as investigations.In this paper, we investigate the role of maturity driven process improvement in an industry.In order to achieve this goal, literature study was used to learn from the experiences of companies those have experienced maturity based process improvement.Maturity driven process improvement is suitable for big organizations those can afford consultancy costs and have enough resources to devote for software process improvement team.It seems difficult for smaller companies; one possible reason for many companies to give up software process improvement is that they set their goal to reach a particular certification level or merely achieving a maturity level.The main focus should be on achieving the process improvement instead of achieving a label of specific maturity level.