Reviewerly/exHarmony huggingface dataset:
An Advanced Strategy of Multi User Storage System for Cloud Computing

Cloud computing is an emerging model of business computing. In cloud computing, client can use and retrieve the services anytime by using any smart devices to manage complex computing processes and to access very large data storage. The developers have recognized the required of a multi USER storage system that can help in utilizing the cloud power by enhancing its functionality and improve its performance. In this paper, proposing the architecture of advanced Multi-user storage System which primarily focuses on the price negotiation mechanism between cloud users and providers. This system presents a perfect way for scalable and open systems that are changed dynamically. The model is based on cooperative and collaborative USERs and is managed. Also this architecture is designed to monitor the user’s jobs while they are being processed.
PERANCANGAN SISTEM INVENTARIS BARANG PADA KUBE MANDIRI SEJAHTERA MENGGUNAKAN FRAMEWORK TOGAF ARCHITECTURE DEVELOPMENT METHOD (ADM)

ABSTRACT&#x0D; Management of company inventory is an activity that is needed by the company, so that every item contained in the company, can be monitored and recorded neatly. However, the management of company inventory items is often only done manually and data on the identity of the goods is not neatly arranged. To search for goods, the time required is quite long and not practical, because employees still find it difficult to know the location of goods and the condition of the goods. Of course, for this reason, making updates and data collection is rarely done. Due to the rapid advances in technology, it is time for manual search to be abolished and a system that can search for goods and inventory data more easily is eliminated. This research builds an application-based system that can search for goods, record information, and make reports of goods that are in accordance with the wishes of the user. In making this system, the TOGAF ADM method is used which is the method currently most widely used because it provides a comprehensive approach to designing, planning, implementing, and managing Enterprise Architecture. Later this system is expected to be able to simplify the company and help workers be more effective in their work.&#x0D; Keywords: Architecture Development Method, Enterprise Architecture, TOGAF&#x0D;
The Social Identity Change of Little Bee as the Main Character in Chris Cleave’s The Other Hand

&lt;p&gt;&lt;strong&gt;This research examines Little Bee’s social identity change based on Chris Cleave’s novel entitled &lt;em&gt;the Other Hand. &lt;/em&gt;The object of this research focuses on the Little Bee’s social identity change. The method used in this research is critical reading. To collect the data, the researcher did some steps including reading the novel closely and intensively, making notes, visiting library, and exploring the data from the internet which are related to the topic. In analyzing the data, the researcher used descriptive qualitative method.&lt;/strong&gt;&lt;/p&gt;&lt;strong&gt;After conducting the research, the researcher got several research findings. They are: 1). The reason of Little Bee’s social identity change are her envy towards British people’s easy life, her needs to tell her sad story, her will to survive, and her desire to overcome her past. 2). The way Little Bee learns her new identity is by reading, such as novels and newspaper, practicing how to pronounce and speak, looking for difficult words in her dictionary in order to learn the language, and learning the British life by watching television and reading books to understand the British lifestyle.&lt;/strong&gt;
A Conceptual Paper on Multi Scale Applications in Cloud Environment with Reference to Variety of Data

Developments in Cloud Computing has opened up new avenues for cutting – edge technology applications. It is the conventional technology that deploys the applications and services on internet servers instead of traditional servers. Software-as-a-Service (SaaS) allows access to a variety of applications on the cloud commonly known as Multi-scale applications. Social and Professional media like Whatsapp, Facebook, Twitter, Instagram, LinkedIn, ResearchGate etc. have migrated to cloud. The terms scale and scalability in cloud environment has been used in different contexts. The applications can be scaled up or down based on the network traffic scenario, and has thus opened up a Pandora’s Box in new research and development of multi–scale cloud based applications. This conceptual paper attempts to review literature across various domains where the term multi-scale, scaling, cloud based application have been used and arrive at a reasonable, crisp, concise yet accurate definition of the term multi – scale and multi–scale cloud application. The term “Multi – Scale” is used in various fields referring to multiple scales applied to arrive at solutions to spatio-temporal problems. This conceptual paper attempts to associate a formal definition to the term “multi-scale cloud application” in the cloud computing world. The proposed definition of the said term is extremely relevant in the context of multi–scale applications developed in recent times.
Opinion Classification

Classifying opinions have been identified as prime task of sentiment. In many applications of NLP, classifying text has been considered an essential component. Traditionally, text classification was mainly done by manual engineering tasks such as developing handcrafted features by consulting dictionaries, knowledge-based techniques or customized hierarchical components (tree kernels). It is mainly achieved by humans.
ANN Model Identification: Parallel Big Bang Big Crunch Algorithm

This paper proposes a modification to existing big bang big crunch optimization algorithm that uses the concept of more than one population. In this the search begins with all the populations independently in parallel and as the algorithm proceeds the local best of the individual populations interact with global best to avoid local minima. In order to validate the proposed approach the authors have identified two models one from control field namely rapid battery charger and second a rating system for institutes of higher learning and compared its results with simple BB-BC based approach .The author further compared results of the proposed approach with the results of other recent soft computing based algorithms for ANN model identification. The proposed algorithm outperformed all of the other 7 algorithms in terms of MSE and convergence time.
Human Development Index Forecasting using Exponentially Weighted Moving Average

Human Development Index (HDI) is an indicator to see the ruling government achievement, especially in the human development aspect. It was introduced by United Nations Development Programme (UNDP) in their 1990 report and integrated three basic dimensions of human development, i.e., the ability to lead a long and healthy life, to acquire knowledge, and to achieve a decent standard of living. Indonesia itself was ranked 113 out of 188 UNDP countries and ranked fifth among ASEAN countries for HDI value based on UNDP Human Development Re-port 2016: Human Development for Everyone. In this study, we are trying to forecast Indonesia’s HDI using Exponentially Weighted Moving Average (EWMA) method. EWMA is a popular and widely used method to smooth out random fluctuations. Based on the experiments conducted in 34 provinces in Indonesia, EWMA has been successfully applied to forecast the human development index in Indonesia. It has an average score of MSE and MAPE values at 1.002279153 for MSE and 1.374754513% for MAPE.
Android App to Connect Farmers to Retailers and Agricultural Machinery Rental Business

Agriculture serves the foremost basic task of providing humanity with adequate food provides. The farmer's economy must be met so as to accomplish this task. One amongst the most effective solutions for fulfilling the task is that the selling of agriculture. It involves the acquisition from farmers of agricultural product. Some farmers square measure unaware of costs of commodities and sell their merchandise to third-party suppliers at low prices, leading to vast losses for farmers. we are going to produce associate degree automaton application of farmer product selling to unravel this way of scenario, that provides higher information of the artefact costs of current selling ways at completely different locations. This helps to seek out patrons and appropriate sellers. It additionally permits farmers to pick out the most effective position on the marketplace for the products to be sold . They additionally needed completely different farming machinery for crop production, however they're unable to shop for the high price instrumentation because of poor economic conditions, another alternative left is to require the machines on loan. It helps to make a web-based business management system to confirm that agricultural machinery rental businesses square measure run with efficiency and handled transparently.
Evaluation of WebPages performance W.R.T UI/UX developed using different frameworks

Last decade observed the exponential growth of World Wide Web (WWW) due to the growing trend of migration of each &amp; every information on the web. In order to design the User Interface (UI) of web based application, a number of frameworks are used such as Bootstrap, WordPress, and Joomla etc. But sometimes the use of Dynamic HyperText Markup Language (DHTML) to design from scratch is preferred over frameworks. In this paper, research was carried out to compare the performance with respect to UI on different parameters of web-portals designed using frameworks viz-a-viz use of DHTML technology. Performance was compared using the tools, GTMetrix and Pingdom. Results can be used to facilitate the decision to select the framework or not, while designing the UI of webpages.
Noise in Time-Series

Overview of key identifying features of noise as can typically occur in geoscience time-series. Categorisation according to noise colour; white, red and blue noise. Consideration of autocorrelation and autoregression, power spectral density and power-law. Worked red-noise example to illustrate.
Saliency Object Detection from Video Streams using Salient-Graph Model with High-Level Background Prior

This study proposes a novel salient graph model with high-level background prior. As usual, the collected data is pre-processed and then used for segmentation analysis. Object detection is still a daunting task due to increased complexity of false positive rate. Thus, a salient graph model is constructed using high-level background prior. Initially, the contrast of an image enhanced for superpixels and used for finding the shortest path of visible region. Then, saliency map is formed by spatial analysis of those visible superpixels. In salient post-processing, the salient graph is constructed by labelling background nodes with minimized cost. Based on formed salient region, each adjacent superpixel with background nodes are used for queries. Atlast, the estimated saliency and objectness measures detects the objects with minimal constraints. The proposed framework is analyzed on SegTrack and SegTrack 2, video segmentation dataset. The results states that the proposed method achieves better results than state of the art models by improved precision, recall, F-measure and computational time.
A Supervised Classification Techniques to Optimize Error Evaluation and Space Complexity

Bayesian classification is based on Baye’s Theorem, which is applied on a conditional probability basis of posterior and prior probabilities in parallel with future evidence. Prior Probabilities are the original probabilities of an outcome which will be updated with new information to create posterior probability. The revised probability of an event occurring after taking into consideration new information.A Bayesian classifier is used to predict the values of features for members of that class. It is used to over come the diagnostic and predictive problems. This classification provides a useful perspective for understanding and evaluating machine learning algorithms.It is a probabilistic learning algorithm which calculates the explicit probabilities for hypothesis, among the most common learning problem.The proposed work has focused on designing of two classification algorithms naïve space and naïve Mine classification to optimize space complexity and error evaluation forlarger data sets.
Knowledge Spillovers From Superstar Tech-Firms: The Case of Nokia

Do workers hired from superstar tech-firms contribute to better firm performance?To address this question, we analyze the effects of tacit knowledge spillovers from Nokia in the context of a quasi-natural experiment in Finland, the closure of Nokia's mobile device division in 2014 and the massive labor movement it implied.We apply a two-stage difference-in-differences approach with heterogeneous treatment to estimate the causal effects of hiring former Nokia employees.Our results provide new evidence supporting the positive causal role of former Nokia workers on firm performance.The evidence of the positive spillovers on firms is particularly strong in terms of employment and value added.
Image Forgery Detection and Localization using Modified JPEG Ghost

Authenticity of an image taken digitally suffers severe threats as a result of increase in various powerful digital image editing tools. These tools modifies the image contents without leaving footprint of such modifications. We come up with a technique that analyzes digital image forgery detection in JPEG images which goes through multiple compression. Nearly all digital devices uses JPEG as a standard storage format to maintain the storage space. JPEG is a lossy compression standard. By using any image processing tools, when assailant changes any part of a JPEG image and save it, the alter part of the image has different compression artifacts. JPEG ghost algorithm is used to detect disparity in JPEG blocks that rise from improper alignments of JPEG blocks respect to original structure and detect local footprint of JPEG compression. In our work, our proposed technique will modify JPEG ghost detection to detect and localize digital image forgery.
Study 4: Customer Responses to Service Robots: Comparing Human-Robot Interaction with Human-Human Interaction

This paper investigates how service failures affect customers by comparing human-robot interactions with human-human interactions. More specifically, it compares customers’ satisfaction in a service robot interaction depending on a service failure with the customers’ satisfaction in a frontline service employee interaction. On a theoretical basis, extant literature on the uncanny valley paradigm proposed that service robots would create lower satisfaction than human frontline employees would.
Inspiration-wise swarm intelligence meta-heuristics for continuous optimisation: a survey - part III

Global optimisation techniques aim to find the best solution of a given a problem. In general, these problems are complex, nonlinear and intractable. On the other hand, there is a plethora of optimisation techniques. Swarm intelligence is based on models inspired by swarming behaviours. In the first part of this survey, we propose an inspiration-wise taxonomy of such metaheuristics and reviewed those that are inspired by physical system's properties. In the second part, we surveyed techniques that are inspired by fictional metaphors, flocking and schooling behaviours. In this third part, we concentrate on bioinspired methods that are guided by swarming, herding and proliferating behaviours. Overall, we point out the common inconvenience of using many of these metaheuristics, which is related to the setting required parameters. There are some proposed techniques that reduce the required parameters to a minimum. Moreover, dynamic adjustment of these parameters is usually exploited.
Retraction Note to: Exponential Relationship Based Approach for Predictions of Defect Density Using Optimal Module Sizes

The authors have retracted this article [1] because after publication they became aware that incorrect calculations in equations 2 and 3 mean that the conclusions drawn in equation 4 are incorrect.
7 Lexical decomposition: Foundational issues

Theories of lexical decomposition assume that lexical meanings are complex. This complexity is expressed in structured meaning representations that usually consist of predicates, arguments, operators, and other elements of propositional and predicate logic. Lexical decomposition has been used to explain phenomena such as argument linking, selectional restrictions, lexicalsemantic relations, scope ambiguites, and the inference behavior of lexical items. The article sketches the early theoretical development from noun-oriented semantic feature theories to verb-oriented complex decompositions. It also deals with a number of theoretical issues, including the controversy between decompositional and atomistic approaches to meaning, the search for semantic primitives, the function of decompositions as definitions, problems concerning the interpretability of decompositions, and the debate about the cognitive status of decompositions.
Predicting Click-Through Rates using Data Mining Technique on Digital Advertisements

In times of increasingly busy use of social media, placing advertisements on social media such as Facebook is an attractive alternative. With the various advantages of advertising on social media, making it very suitable for MSMEs. The use of video as a format for delivering advertising messages is also rife because of the faster internet speed. However, the cost of making ads in the form of videos is relatively more expensive so it needs a lot of consideration when making it be efficient. One thing that advertisers often pay attention to on social media is the click-through rate. This variable becomes one of the measures of the effectiveness of an advertisement. Hence predicting click-through rate is become important nowadays for advertisers, especially to those who have budget constraints. This research tries to predict the click-through rate using data mining techniques. This paper use CRISP method. The dataset was taken from a Facebook advertisement from a small-medium enterprise in Indonesia. Video watches at 25%, 50%, 75%,95% and 100% is use as predictors. The results show that data mining can be used to predict the click-through rate using video watches percentage. Deep learning is the most suitable model for this prediction. The interpretation of the results from data mining is done and managed to find the variables that support the predictions and contradict the predictions.
Crustal Reworking in the Archaean: Geochemical Evidences from Granitoid of the Western Dharwar Craton

Earth's continental crust is not uniform, but has a more mafic lower crust and a more felsic upper crust.The generation of granites by extraction of melt from the lower portion and its emplacement in the upper portion is the principal process by which continents are differentiated.Thus to study the petrogenesis of granites is important to understand the crustal reworking processes.Dharwar craton of south India has a number of late-Archaean intrusive granites which are considered to be produced by reworking of crustal sources with or without significant mantle input.In this study we are trying to show the geochemical manifestation of this process taking example of one of the plutons from the Western Dharwar Craton.The pluton was intruded into the granite-greenstone basement, SW of Chitradurga Greenstone Belt.It mainly consists of medium-coarse grained, grey and pink granites with quartz, K-feldspar, plagioclase and biotite as major minerals.Field obesravtions suggest that this granite was formed by melting of TTG (Tonalite-Trondhjemite-Granodiorite) gneisses, where TTG is present as enclaves in granite.Geochemical modeling was carried out to test this hypothesis.The trace element composition of melts were determined using a model of equilibrium non-modal melting, taking average TTG of this terrane as source.Quartz, Kfeldspar and plagicolase, together with small amounts of biotite and epidote were assumed to melt, generating a major element composition consistent with the granite.The effect of small amounts of accessory mineral phases such as monazite, zirocn, sphene, apatite, ilmenite and magnetite was also included.Our result shows that 11% melting of TTG can produce this granite without significant mantle input.The model is consistent with Nd isotope data which show epsilon Nd values of granite similar to that of TTG at 2600Ma ( -1.5 to -6.7 for TTG and -4.1 to -7.7 for granite).We, based on these results infer that there was an extensive reworking of the preexisting TTG crust in Neoarchaean and some granites were formed only through crustal anatexis without any mantle input.
Atla Association Update

The Atla Association Update provides highlights of Atla’s accomplishments for the year, an overview of finances for fiscal year 2019, and a brief preview of the projected budget for fiscal year 2020.
Improving student’s ICT-competencies when working in a remote format

The article provides an analysis of educational platforms in order to improve the quality of the educational process. The positive and negative aspects of their application are shown.
Chapter 3: Various Greedy Algorithms

Recall that Φ is a sensing matrix of size m × n with m < n or m ≪ n and b is a vector which is compressible in the sense that there exists a vector xb with ∥xb∥0 < m such that b = Φxb. Typically, Φ consists of tight wavelet framelets which can be useful for image compression. Indeed, we find the sparse solution xb for a compressible image b using the minimizationNote that the size of a standard image is 512 × 512 and hence it is of size 5122 × 1 after converting it to vector b. The size of the sensing matrix Φ is 5122 × n with n ≫ 5122, where n is the size of the dictionary consisting of various patterns of image blocks. When the sparsity s is relatively small, a greedy algorithm is definitely preferred over many other sparse solution approaches discussed in other chapters.
Innovative Uses of Social Media in Information Literacy Education, Library Outreach, and User Engagement: An International Perspective

Social media tools have been pervasively adopted as an effective way for library marketing and outreach.This paper presents best practices of innovative uses of social media (e.g.WeChat and QQ) in library instruction education and user engagement based on first-hand experiences of the IEEE Client Services team in China.Use cases to be shared include making social media official accounts functional for maximum outreach, conducting real-time information literacy trainings via social media, establishing target user groups by subject and engaging faculty and students 24x7, building a self-sustaining learning community and facilitating embedded learning in users' daily lives, and working with student groups to design and implement interactive games (e.g.large-scale in-person library scavenger hunt "Library Break") with the assistance of social media.Positive relationships are revealed between the use of social media and the levels of both library activity turnout and user engagement.Interesting findings and metrics in designing and implementing different types of events using social media are also discussed.This paper not only brings an international perspective to US tales of using social media, but also provides practical suggestions to academic librarians who are interested in seeking innovative ways of end user outreach, education, and engagement.As the engineering community is the main audience in the use cases, this paper also contributes to engineering education.
HydroDot Wave Prep

Biomedical Safety & Standards: March 15, 2020 - Volume 50 - Issue 5 - p 39 doi: 10.1097/01.BMSAS.0000656888.66370.87
Retraction Note to: Adaptive neuro-fuzzy selection of the optimal parameters of protective spur dike

The Editors-in-Chief have retracted this article [1] because validity of the content of this article cannot be verified.