Reviewerly/exHarmony huggingface dataset:
Secured Storage of Big Data in Cloud

Big Data refers to large volume of data and necessitates the usage of cloud for storage and processing. Cloud tenants data is not only stored in the cloud, but it is also shared among multiple users. The data stored in cloud must be well protected as it is prone to malicious attacks and hardware failures. Also, user’s data on cloud contain sensitive information that must be protected and highly restricted from unauthorized access. Cloud deployment models such as public cloud, private cloud, and hybrid cloud can be used for storing data of cloud tenants. This paper proposes a secured storage approach for protecting data in cloud by partitioning big dataset into blocks containing user’s sensitive data, insensitive data, and public data. Sensitive data is moved to private cloud and is well protected using proxy re encryption. Insensitive data is stored in public cloud and some data blocks are randomly encrypted. Also, the storage index information of insensitive data blocks on cloud is encrypted and shared among authorized users. Public data is also moved to public cloud and to protect it the storage path information is only encrypted and shared. The proposed approach shows better results with reduced computation overhead and improved security.
The Unique Qualities of Machines in Quasi-Social Interactions – Exploring and Understanding Otherware

Technologies like chatbots or voice assistants that users encounter and interact with in a social manner, or an alterity relationship, become more and more widespread and gain relevancy.However, approaches that make use of the unique qualities of machines in social situations instead of the attempt to replicate humans are still infant and need further research and application.To approach this topic, otherware should be understood further, through case studies and the development of methods and tools to design appropriate, non-anthropomorphic applications and interactions.Furthermore, applying speculative methods such as anticipative ethnography, but also through materialised examples, the design of otherware leading to positive experiences should be explored.
Building Graph for Events and Time in Natural Language Text

Events and time are two major key terms in natural language processing due to the various event-oriented tasks these are become an essential terms in information extraction. In natural language processing and information extraction or retrieval event and time leads to several applications like text summaries, documents summaries, and question answering systems. In this paper, we present events-time graph as a new way of construction for event-time based information from text. In this event-time graph nodes are events, whereas edges represent the temporal and co-reference relations between events. In many of the previous researches of natural language processing mainly individually focused on extraction tasks and in domain-specific way but in this work we present extraction and representation of the relationship between events- time by representing with event time graph construction. Our overall system construction is in three-step process that performs event extraction, time extraction, and representing relation extraction. Each step is at a performance level comparable with the state of the art. We present Event extraction on MUC data corpus annotated with events mentions on which we train and evaluate our model. Next, we present time extraction the model of times tested for several news articles from Wikipedia corpus. Next is to represent event time relation by representation by next constructing event time graphs. Finally, we evaluate the overall quality of event graphs with the evaluation metrics and conclude the observations of the entire work.
Latent Dirichlet Analysis Based Opinion Mining of Healthcare Report

Intelligent abstraction of knowledge from Social blogs has gained immense attention from Biomedical and online health communities. Healthcare re- views through public opinion platforms not only helped in improving the health- care but also reducing the costs. Advancement in digital healthcare systems enables patients access to Online Health Communities (OHC) through their views, opinions and remedial information. In the current study reviews provided by patients on OHC related to diabetes has been chosen to understand the community effort to address the health care issues of these patients. Reviews shared by patients at various levels of diabetic conditions have been selected and analyzed using LDA text mining techniques. In the current study we have also analyzed the gender specific differences among the diabetic community based on the opinions shared by them on public OHC platforms. Sample for the study has been gathered from a prominent online diabetic community in UK. Community members wrote on different topics from food habits, lifestyle, physical exercise and dis- ease symptoms to predict and possible remedial measures to be taken has been discussed in online health communities. Results of our study show that male users are information-centric than the female users, while female users are more emotionally attached as compared to males. Study also reveals several other findings related to community support and state of healthcare sector with reference to treatment, medication and facilities available to the community.
Pemanfaatan Sistem Informasi Akademik dan Media Video Pembelajaran Sebagai Penunjang Kinerja Guru

Academic Information System (xSIA) is a system that has one function to manage student data so as to provide convenience for teachers as the main users in recording the final grades of student learning outcomes each semester. The PKM program for teacher partners also provides technical training using the Snagit application to find out learning video material as an alternative method so that it can support teacher performance in the online teaching process. So that the Learning From Home (LFH) or Distance Learning Method can be implemented by school teachers at SDN No.133 Pari'risi Takalar. The training model applied uses the Preceptorship and Participatory training model. while the design phase of the application model used by Prototyping to graphically represent the workflow of the system. The objectives of this research are: 1) The existence of a web-based academic information system application, namely xSIA to report student assessments and application video tutorials. 2) Output in the form of the ILKOM Scientific Journal for the 2021 period UMI Computer Architecture or other Accredited journals. And 3) enrichment of teaching materials in Software Engineering courses.
Preservation of Privacy of Big Data Using Efficient Anonymization Technique

Big data needs to be kept private because of the increase in the amount of data. Data is generated from social networks, organizations and various other ways, which is known as big data. Big data requires large storage as well as high computational power. At every stage, the data needs to be protected. Privacy preservation plays an important role in keeping sensitive information protected and private from any attack. Data anonymization is one of the techniques to anonymize data to keep it private and protected, which includes suppression, generalization, and bucketization. It keeps personal and private data anonymous from being known by others. But when it is implemented on big data, these techniques cause a great loss of information and also fail in defense of the privacy of big data. Moreover, for the scenario of big data, the anonymization should not only focus on hiding but also on other aspects. This paper aims to provide a technique that uses slicing, suppression, and functional encryption together to achieve better privacy of big data with data anonymization.
Impacts of Demonetization in the Indian State Arunachal Pradesh: A Political Gimmick or Robust Policy

On November 8th, 2016, the Indian Government announced the demonetisation of the 500-rupee and 1000-rupee currency notes. Demonetisation is the process in which particular notes or coins are withdrawn from the circulation as a legal tender. The main objectives of demonetisation are, in general, to control corruption, black money, the finance of terrorist organisations and the duplicity of currency notes within a country. In the case of India, the 500-rupee and 1000 rupee notes ceased to be a legal tender starting from December 31st, 2016, and people having this demonetised currency notes had to change them or deposit them into their bank accounts. The objective of this research paper is to highlight the impacts that demonetisation brought to India.
Transformation of Dependency and Association in UML Design Class

This paper presents two new conceptual relationships between classes of software development known as dependency and association. The design between the two relationships could be interchangeable because it always takes place in real-life situations — for instance, the relationship from friends to husband-wife and vice versa. However, in terms of coding, the most important factor is system performance. That means the designer could write the code as dependency or association to provide the same result. To improve the efficiency of the program, the researcher writes the code in the C++ language to execute four types of variables named messages, strings, calculation, and sorting. The four types of the variable used to test the performance of aggregation, composition, dependency, and functional programming, the timestamp was used to measure the execution time before and after for each case 50 times. The F-test statistic was used to compare the mean difference of each type of variable. The researcher found that for the Message variable. The functional programming is the fastest, followed by aggregation, composition, and dependency, the average C.P.U. time are 13566.60, 17891.70, 18532.66 and 19336.76, at 0.0 level of significance. For the String variable found that functional programming is the fastest followed by dependency, composition, and aggregation, the average C.P.U. time are 23785.88, 27449.76, 28478.24 and 28788.18, at 0.0 level of significance. For calculation found that functional programming is the fastest, followed by aggregation, composition, and dependency, the average C.P.U. time are 26982.68, 29311.86, 29377.50 and 29397.30, at 0.0 level of significance. For sorting found that functional programming is the fastest, followed by aggregation, composition, and dependency, the average C.P.U. time are 17925.20, 18408.36, 21641.68 and 22861.14, at 0.0 level of significance.
A State of Art on Content Based Image Retrieval Systems

The digital image data is quick expanding in capacity and heterogeneity. The customary information retrieval approaches are cannot fulfill the client's need, so there isneed to present a proficient framework for Content Based Image Retrieval(CBIR). The CBIR is an appealing wellspring of precise and quick retrieval. CBIR goes for discovering imagedatabases for explicit images that are like a given query image dependent on its features.In this paper the methodology of content based image retrieval are examined, investigated and thought about. Here, the different image substance, for example, colour, texture and shape features are mined by utilizing differentfeature extraction procedures, and furthermore extraordinary distance measures, Relevance Feedback (RF) and indexing methods are used to improve the execution of the CBIR system.The existing exploration strategies are talked about with their benefits and negative marks, so the further research works can be focused more.
OPTIMASI CONJUGATE GRADIENT PADA BACKPROPAGATION NEURAL NETWORK UNTUK PREDIKSI HASIL TANGKAP IKAN

The need for fish catch by a company or fisherman in Rembang Regency affects market process and also welfare. The catch made by the fishermen is not on target, due to the weather and type of fishing gear. An accurate method is needed in making predictions and a correlation between catch and weather so that fisherman can get maximum predictions results, so that price adjustment can be made. The research was conducted using an experimental method, to determine the accuracy of the effect of the Conjugate Gradient on the Back Propagation Neural Network in obtaining the best value. Based on the results of the Cycle training test with the Conjugate Gradient Backpropagation Neural Network method, the smallest average value is obtained at the 400th Epoch compared to the Epoch Gradient Descent With Momentum method at Epoch 800.Thus it is proven that using the Conjugate Gradient Backpropagation Neural Network method is better with an average value of- MSE average 0.2223 in three stages of testing Training Cycle, Learning Rate and Hidden Layer.
Martin Feldstein

Martin Feldstein was a remarkable economic scholar, an extraordinary teacher, a fabulous economic and academic administrator, and a dedicated public servant who had a tremendous impact. He played an enormous role in advancing empirical research in economics and was a mentor to generations of undergraduates and graduate students. Beyond his research and teaching, Feldstein was a transformative figure within economics, through his three decade leadership of the National Bureau of Economic Research. He is going to be very deeply missed.
Electronic speckle pattern interferometry: Applications to the musical acoustics of percussion instruments

Electronic speckle pattern interferometry provides real-time images of operating deflection shapes of acoustically driven structures. This technique is particularly useful in the study of musical percussion instruments containing bars, plates, and membranes. Musically relevant examples involving drumheads, crotales, and the musical saw are presented. Imaging through water is described as well as methods for studying coupled vibrations between the two heads of a drum.
Review of “Configuration and Intercomparison of Deep Learning Neural Models for Statistical Downscaling”

This study investigates the ability of convolutional neural networks (CNN) for downscaling daily temperature and precipitation over Europe.The added value is found for extreme temperature and most metrics for precipitation.The paper is well written and the results demonstrate the importance of accounting for non-linearity for downscaling precipitation.I have a few concerns that the authors should be able to address.Major comments 1.It would be good to say something more specifically about European applications and the related data needs.Climate change studies are mentioned in the text.But nothing is said about the types of applications/users in the Introductionand this influences the types of information required -e.g., whether spatial consistency
A Story Systematic Methodical Facts Ownership Examine Agreement in Threat Storage

As a vital software program software program in allotted computing, allotted garage gives purchaser ascendible, flexible and immoderate first-rate information stockpiling and calculation administrations. A growing acquire of reading of facts of records residence proprietors want to supply records statistics to the cloud. Because of allocated garage servers do no longer seem like actually dependable, facts house proprietors need honest recommends that to check the ownership for their records re-appropriated to a protracted way flung cloud servers. To address this massive disadvantage, a few some distance off facts possession checking (RDPC) conventions are given. Be that as it can, some present plans have vulnerabilities in power or gaining information of factors. Amid this paper, we offer a glittery new sparing RDPC conference reinforced homomorphic hash carry out. The new project keep in mind is undeniably comfy in the direction of imitation attack, supplant attack and replay attack upheld a boring safety display. To assist mastering factors, Associate in Nursing hobby file desk (ORT) is familiar with look at obligations on record squares. We keep an eye fixed on extra gives every specific progressed usage to the ORT that makes the charge of getting to ORT approximately ordinary. Also, we are able to in large shape the top notch execution studies that demonstrates that our problem don't forget has endowments in calculation and correspondence fees. Display execution and trials show that the problem is attainable for real programs
A Better Understanding of Relationship between Job Satisfaction and Affective Organizational Commitment

The public sector in UAE is the focus of this paper. Applying the concept of job satisfaction to examine its effect on employees’ affective organizational commitment. The data was collected from 452 officers from 7 sectors in the ministry of interior in UAE and analysed using structural equation modelling via SmartPLS 3.0. The result showed that job satisfaction has a positive impact on affective organizational commitment. The proposed model explained 11.4% of the variance in employees’ affective organizational commitment.
Utilizing User Feedback Data for Content Recommendations

Nowadays, Internet contains massive amount of information. In this environment, people who seek specific information could be overwhelmed by the options that they can reach through the Internet. To help users filter the information and overcome the information overload problem, recommender systems play an important role. Here, we deal with a specific recommendation problem – recommending content to users in a content management system utilizing users’ feedback data. We have tried both content-based and collaborative filtering approaches. In the content-based approach, once the content profile is built, user profile could be built based on different categories of user feedback data. We have explored the effect of these different feedback categories on the recommendation result. In the collaborative filtering approach, the feedback data is used for building the user-content rating matrix and matrix factorization is then applied. The experiment result shows that content-based approach outperforms collaborative filtering approach for this particular problem.
An Efficient and Privacy-preserving Ranked Fuzzy Keywords Search over Encrypted Cloud Data

As cloud computing becomes widespread, more and more users prefer to outsource their local sensitive data into the cloud. In order to protect data privacy, these sensitive data usually have to be encrypted before outsourcing, which makes effective data utilization a very difficult task. Although traditional searchable encryption techniques allow users to securely search over encrypted cloud data, they only support exact single keyword search, i.e. they do not allow any minor spelling errors or format inconsistencies. Besides, these traditional schemes only support Boolean search, without capturing any relevance of data files and rarely sort the search result. Recently, fuzzy keyword search over encrypted data techniques are introduced to resolve the problem of spelling errors and format inconsistencies. But these methods may incur large index size, search result inaccuracy and high search complexity, which greatly reduce the system usability and efficiency. This paper proposes the solution for privacy preserving ranked fuzzy keyword search over encrypted cloud data with small index. K-grams and Jaccard coefficient are utilized to construct fuzzy keyword set and produce fuzzy results, and an efficient relevance criteria is also provided to capture the relevance between data files and search requests. Extensive experimental results show the efficiency of our proposed method.
Musings on Collection Analysis and Its Utility in Modern Collection Development

A commentary on moving views of collection analysis forward to focus more on access rather than ownership, and services rather than physical resources to measure the ability of a library to support academic programs of study.
An Efficient Path Planning Algorithm for Networked Robots using Modified Optimization Algorithm

Path planning has played a significant role in major numerous decision-making techniques through an automatic system involved in numerous military applications. In the last century, pathfinding and generation were carried out by multiple intelligent approaches. It is very difficult in pathfinding to reduce energy. Besides suggesting the shortest path, it has been found that optimal path planning. This paper introduces an efficient path planning algorithm for networked robots using modified optimization algorithms in combination with the η3 -splines. A new method has employed a cuckoo optimization algorithm to handle the mobile robot path planning problem. At first, η3 - splines are combined so an irregular set of points can be included alongside the kinematic parameters chosen to relate with the development and the control of mobile robots. The proposed algorithm comprises of adaptive random fluctuations (ARFs), which help to deal with the very much manageable neighborhood convergence. This algorithm carries out the process of accurate object identification along with analyzing the influence of different design choice by developing a 3D CNN architecture to determine its performance. Besides offering classification in real-time applications, the proposed algorithm outperforms the performance of state of the art in different benchmarks.
Assessing Factors Affecting Intention to Adopt AI and ML: The Case of the Jordanian Retail Industry

Aim: The aim of this research is to evaluate the factors that affect the adoption intention of AI and ML in the context of Jordan’s retail industry Method: For this research paper, primary data was collected with the help of surveying different retail companies that are operational in Jordan with a sample of 400 participants. The survey questionnaire was based on a Likert scale where five points ranging from strongly agree to strongly disagree were provided to the participants. Structural Equation Modelling (SEM) used to analyses the impact and significance of the different factors on the adoption of AI and ML in Jordanian retail sector. Results: It has been concluded from this research paper that communication, government regulations, market structure, and technological infrastructure are important factors that influence the adoption of AI and ML in the retail industry of Jordan. However, the results of this research have pointed out that managerial support and vendor relationship do not have a significant influence on the adoption of AI and ML. Limitations: The scope of the research is restricted to the context of the retail industry only. This research has been carried out in the context of Jordan thus it cannot be applied on to other geographical backgrounds. Due to the time and scope limitations, there are restricted factors considered in the framework.
An improved non-local means filter for image mixed noise

The non-local means filter (NLMF) can improve the denoising effect of the image while retaining good edge details. However, the smoothing coefficient h needs to be manually determined before processing. In advance, the non-local means filter has a weak denoising ability for impulse noise and SPN salt-and-pepper noise. In order to overcome these problems, this letter improves the non-local means filter for image noise, with combining with median filtering, and using an adaptive smoothing coefficient h. The proposed method can be used to mixed noise. Gaussian noise, impulse noise, or salt-and- pepper noise are processed in this letter. Compared with median filter, mean filter and standard non-local mean algorithm, the adaptive non-local means filter proves the effectiveness.
Correction: Extrafoveal attentional capture by object semantics

[This corrects the article DOI: 10.1371/journal.pone.0217051.].