Reviewerly/exHarmony huggingface dataset:
(2011-6287) A fuzzy non-parametric time series model based on fuzzy data

Parametric time series models typically consists of model identification, parameter estimation, model diagnostic checking, and forecasting. However compared with parametric methods, nonparametric time series models often providea very flexible approach to bring out the features of the observed time series. This paper suggested a novel fuzzy nonparametric method in time series models with fuzzy observations. For this purpose, a fuzzy forward fit kernel-basedsmoothing method was introduced to estimate fuzzy smooth functions corresponding to each observation. A simple optimization algorithm was also suggested to evaluate optimal bandwidths and autoregressive order. Several common goodness-of-fit criteria were also extended to compare the performance of the proposed fuzzy time series method compared to other fuzzy time series model based on fuzzy data. Furthermore, the effectiveness of the proposed method was illustrated through two numerical examples including a simulation study. The results indicate that the proposed model performs better than the previous ones in terms of both scatter plot criteria and goodness-of-fit evaluations.
DeFRCN: Decoupled Faster R-CNN for Few-Shot Object Detection

Few-shot object detection, which aims at detecting novel objects rapidly from extremely few annotated examples of previously unseen classes, has attracted significant research interest in the community. Most existing approaches employ the Faster R-CNN as basic detection framework, yet, due to the lack of tailored considerations for data-scarce scenario, their performance is often not satisfactory. In this paper, we look closely into the conventional Faster R-CNN and analyze its contradictions from two orthogonal perspectives, namely multi-stage (RPN vs. RCNN) and multi-task (classification vs. localization). To resolve these issues, we propose a simple yet effective architecture, named Decoupled Faster R-CNN (DeFRCN). To be concrete, we extend Faster R-CNN by introducing Gradient Decoupled Layer for multi-stage decoupling and Prototypical Calibration Block for multi-task decoupling. The former is a novel deep layer with redefining the feature-forward operation and gradient-backward operation for decoupling its subsequent layer and preceding layer, and the latter is an offline prototype-based classification model with taking the proposals from detector as input and boosting the original classification scores with additional pairwise scores for calibration. Extensive experiments on multiple benchmarks show our framework is remarkably superior to other existing approaches and establishes a new state-of-the-art in few-shot literature.
Foreign Direct Investment and Social Sectors Development: A Review

Various studies in the past have examined the effect of foreign direct investment on economic growth, the key basic assumption common to these papers is that economic growth is a good proxy for welfare of both developed and developing countries. However, research on the influence of foreign direct investment on an expanded conception of socio-economic progress such as human development (measured by the human development index) is absent. This research paper presents the literature on foreign direct investment (FDI) and social sectors (health & education) development in the context of developing economies.
Using 3Ds Max Application Create An Eyeball

This paper represents an experiment report of VR (Virtual Reality) and AR (Augmented Reality) for medical learners. Four main purposes of this experiment through 3Ds Max application create an eyeball. The environment of experiment also be discussed. As well as the studies of organ in medical points of view. Two parts, two objects and three modifications as core activities for representing anatomical studies. More details about creation in experiment essential features demonstrated consequently. Thirty steps in experiment, descriptions show with screenshots. Output of this experiments would be able to understand medical learner’s anatomical perspective studies, as well as medical educators introduce organ and medical knowledge to no-medical learners. Combining VR and AR to medical learning activities, as well as clinical presentations. This paper no only practices the application 3Ds Max create organ models, but also medical learning perspective discussion.
Encoding Syriac Letters in Partition Theory Using Extended Vigenere Cipher

Since the topic of e-Abacus diagram appeared in 1978 through its application within the partition theory, where this representation was one of the ideas of cryptography, many researchers study this subject from a purely theoretical perspective without practical application to it. Many researchers have shown interest in it by starting to apply purely mathematically, but starting in the past few years they have been searching for ideas that serve the topic. This research deals with the Vigenere Cipher, which is one of the multi­alphabet ciphers and in the past it was one of the most popular ciphers. For its simplicity and resistance to frequency analysis tests for messages encoded with simple ciphers such as Caesar’s Cipher through. Its application to the Syriac letters, which is one of the oldest ancient languages, used exclusively when reading religious hymns, for Christians in some regions of the world and in other regions as a trading language. In 2017 it was carefully studied through the above diagram and made the application more confidential among its users in terms of trying to find a suitable agreement between the English language letters and Syriac. Special signs and symbols were used in the Syriac language to be able to address the problem of the difference in the number of letters between the two languages. As well as the possibility of using the Cipher between the letters of the Syriac language.
Survey on Energy Efficiency in Cloud Computing

Data centers are serious, energy-hungry infrastructures that can run large scale Internet based services. Energy ingesting representations are essential in designing and improving energy-efficient operations to reduce excessive energy consumption in data centers. This paper presents a survey on Energy efficiency in data centers, importance of energy efficiency. It also describes the increasing demands for data center in worldwide and the reasons for data centers energy inefficient? In this paper we define the challenges for implementing changes in data centers and explain why and how the energy requirements of data centers are growing. After that we compare the German data center market at international level and we see the energy consumption of data centers and servers in Germany from 2010 -2016.
Recommending Insurance products by using Users' Sentiments

In today's tech-savvy world every industry is trying to formulate methods for recommending products by combining several techniques and algorithms to form a pool that would bring forward the most enhanced models for making the predictions. Building on these lines is our paper focused on the application of sentiment analysis for recommendation in the insurance domain. We tried building the following Machine Learning models namely, Logistic Regression, Multinomial Naive Bayes, and the mighty Random Forest for analyzing the polarity of a given feedback line given by a customer. Then we used this polarity along with other attributes like Age, Gender, Locality, Income, and the list of other products already purchased by our existing customers as input for our recommendation model. Then we matched the polarity score along with the user's profiles and generated the list of insurance products to be recommended in descending order. Despite our model's simplicity and the lack of the key data sets, the results seemed very logical and realistic. So, by developing the model with more enhanced methods and with access to better and true data gathered from an insurance industry may be the sector could be very well benefitted from the amalgamation of sentiment analysis with a recommendation.
Unsupervised Domain-adaptive Hash for Networks

Abundant real-world data can be naturally represented by large-scale networks, which demands efficient and effective learning algorithms. At the same time, labels may only be available for some networks, which demands these algorithms to be able to adapt to unlabeled networks. Domain-adaptive hash learning has enjoyed considerable success in the computer vision community in many practical tasks due to its lower cost in both retrieval time and storage footprint. However, it has not been applied to multiple-domain networks. In this work, we bridge this gap by developing an unsupervised domain-adaptive hash learning method for networks, dubbed UDAH. Specifically, we develop four {task-specific yet correlated} components: (1) network structure preservation via a hard groupwise contrastive loss, (2) relaxation-free supervised hashing, (3) cross-domain intersected discriminators, and (4) semantic center alignment. We conduct a wide range of experiments to evaluate the effectiveness and efficiency of our method on a range of tasks including link prediction, node classification, and neighbor recommendation. Our evaluation results demonstrate that our model achieves better performance than the state-of-the-art conventional discrete embedding methods over all the tasks.
Preprocessing of Document Images Based on the GGD and GMM for Binarization of Degraded Ancient Papyri Images

Thresholding of document images is one of the most relevant operations that influence the final results of their further analysis. Although many image binarization methods have been proposed during recent several years, starting from global thresholding, through local and adaptive methods, to more sophisticated multi-stage algorithms and the use of deep convolutional neural networks, proper thresholding of degraded historical document images is still an open challenge. Due to the release of the recent challenging DIBCO 2019 dataset, containing two categories of images with the particularly demanding papyri dataset (track B), most of the earlier proposed methods turned out to be significantly less effective for them. Analysing the competition's results of the DIBCO 2019, apart from the variety of submitted trendy methods based on the use of deep learning, the great importance of image preprocessing may be observed for such demanding images. Hence, in this paper, we present the applicability analysis of the previously proposed preprocessing methods based on the use of the GGD and GMM in combination with various image binarization algorithms for this purpose. The obtained results are comparable with the state-of-the-art methods, including the top algorithms submitted to the recent DIBCO series contest.
Library Research Guides: Locating and Using Permalinks for Articles in Library Databases: ProQuest | ABI/Inform

Defines and discusses the use of persistent links to articles found in IU Libraries electronic resources. how to locate the permalink in ProQuest
Drought Index Mapping in Java Island Using Sentinel-3 SLSTR

As the most populous island globally, Java Island has various vulnerabilities to disasters ranging from geological to hydro-meteorological. One of the most common hydro-meteorological disasters is the drought that occurs every year in the dry season. This disaster causes crop failure, land and forest fires, and clean water shortages. In this study, the Sea and Land Surface Temperature Radiometer (SLSTR) instrument onboard the Sentinel-3 platform was used to map drought using the Vegetation Temperature Condition Index (VTCI) algorithm based on the scattering space technique of Land Surface Temperature (LST) and Normalized Different Vegetation Index (NDVI). During 2018, the highest and lowest LST occurred on September 28 (309 oK), and February 1 (278 oK); the highest and lowest NDVI occurred on July 1 (0.466) and November 13 (0.221). In comparison, the driest conditions indicated the lowest VTCI (0.162) on July 17 and the wettest on August 28 (0.508). The driest and wettest situation co-occurred: at the end of the dry session and the rainy session, it shows that the rain greatly contributes to high VTCI. A regular drought mapping needs to be performed as an effort to disaster risk reduction. Drought maps are then used as a spatial recommendation in reforestation intervention to reduce drought in the future.
Improving the trustworthiness of image classification models by utilizing bounding-box annotations

We study utilizing auxiliary information in training data to improve the trustworthiness of machine learning models. Specifically, in the context of image classification, we propose to optimize a training objective that incorporates bounding box information, which is available in many image classification datasets. Preliminary experimental results show that the proposed algorithm achieves better performance in accuracy, robustness, and interpretability compared with baselines.
Online learning by using WhatsApp to improve students’ writing achievement in recount rext at the first grade of SMAN 7 Bandar Lampung

The objectives of this research are to find out whether there is any significant improvement on students’ writing achievement by using online learning activity by using WhatsApp and to investigate students’ perception of it. This is a quantitative research. The population of the sample was thirty two students of the X MIA 6 of SMAN 7 Bandar Lampung in the academic year 2020/2021. The data were collected by using writing test and questionnaire.  The data were analyzed by using Paired Samples T-Test and descriptive statistics.  It is  shown that there was statistically significant improvement of the students’ writing achievement after they were taught through online learning by WhatsApp group chat .  Moreover, it could be seen from the computation of Paired Samples T-Test where the T-value (17,576) was higher than t-table with the significance level was 0.00  < 0.05.   The result of this research indicates that there is significant improvement  in all aspects of  students’ writing achievement . This suggests that online learning activity by using WhatsApp can be used as a strategy to improve the students’ writing achievement.  Keywords:  W hatsapp, online learning, writing .
Diaspora-Homeland Connection in Bharati Gautam’s Vigata ra Baduli [Past and Hiccups]

This article aims to explore the way Bharati Gautam’s memoir Vigata ra Baduli [Past and Hiccups] (2020) connects the writer with her homeland. Home and homeland are out of some major loci of diasporic life and the discourse. Diasporic writings deal with homeland both as a real place to return and an imaginary reality for those transnational migrants who have no chance of physical return to the place left back. To study the writer’s homeland connection as expressed in the book, this study uses qualitative methodology with its interpretative approach for analysis. The theoretical input is the diasporic discourse related to home and homeland. For the diasporans, homeland is the root of their life, culture, language and in total the life they live in the hostland. The time a diaspora loses its physical, imaginary or emotional connection with the homeland, it stops being a diaspora. Thus, every diasporic writing has some kind of homeland connection. The study finds that Gautam’s memoirs deal with her love and respect for the root. These feelings are expressed through her nostalgia, symbols and culture she follows in the USA. Similarly, her own and her children’s critical thoughts on Nepal and Nepali socio-cultural praxis also highlight their connection with the homeland. It is hoped that this study is useful to find how Nepali Diaspora connects itself with Nepal. It may encourage the researchers to work in this field.
Adaptive Gentic Optimization Algorithm For Dynamic Resource Allocation In Cloud Computing

Cloud Computing is regarded as one among eminent technology which isgreatly necessitated for various applications which demands proficient dynamic resourceallocation to meet customer satisfaction through consistent and assured services. Theessential services are offered on the needy basis of users and hence cloud systems aretermed as ‘on demand’ technology. Optimal resource allocation is the most thoughtprovokingconcern in the prevailing system. A novel meta heuristic algorithm is presents toalleviate the above-mentioned issue. Adaptive Genetic Optimization Algorithm (AGOA) isgreatly exploited for extemporize proficient resource allotment process in cloud. Thedynamic resource allotment is attained through task scattering or requests in VirtualMachines (VMs). The optimized searching space which is acquired through AGOA areinstructed for searching direction adjustment in automatic manner with the help ofoptimization method of probability. There will be improved individual performance whenthere exists better fitness. The fitness function value is an important criterion in AGOA.for finding out the individuals either to survive or die. Also, it is validated thatrecommended AGOA offers improved performance while comparing with the prevailingapproaches.
MENINGKATKAN KREATIVITAS MINAT BACA PADA ANAK DALAM SITUASI PANDEMI COVID-19

This service activity was carried out in Hamlet III, Rawang Baru Village, Rawang Panca Arga District, Asahan Regency. The problem that occurs in partners is the lack of reading ability of children at the elementary school level, so that the children's reading interest is very low. There are even some children who are already in elementary school grade 3 while still reading spelling. Children prefer to play with their friends, or even choose to play gedjet. With the low interest they have in reading, it has a huge impact on their ability to learn. The solution given by the implementer to partners is to provide reading books with images in order to attract children's attention and invite them to read in turns. This activity is expected to increase children's reading interest and skills. These activities include 1) providing basic knowledge about the importance of reading skills; 2) giving pictorial reading books to encourage interest and desire to read 3) giving tricks and tips so that a sense of desire to read arises; 4) conducting competition activities to motivate children and improve children's skills in the community. The target output is one article on; activity videos; and increasing the empowerment and knowledge of partners according to the problems at hand. An additional output is the publication of an accredited local journal under the name Anadara journal. Keywords : Increase Reading Interest
Multi-Scale Model Driven Single Image Dehazing

Model driven single image dehazing was widely studied due to its broad applications. It is challenging to prevent noise from being amplified in sky region for the model driven dehazing algorithms. In this paper, a new multi-scale hazy image model is first built up by using the Laplacian pyramid of hazy image and the Gaussian pyramid of transmission map. A novel multi-scale dehazing algorithm is then proposed on top of the model to address the problem. Different haze removal and noise reduction approaches are applied to restore the scene radiance at different levels of the pyramid. The resultant pyramid is collapsed to restore a haze-free image. Experiment results demonstrate that the proposed algorithm outperform state of the art dehazing algorithms and the noise is indeed prevented from being amplified in the sky region.
Domestic vs Foreign debts, A viable option for Africa’s debt crisis: Covid-19 implications

The outbreak of the corona virus (Covid-19) global pandemic brought major disruptions to many Africa economies and the rest of the world. This paper builds on the notion of how Covid-19 has influenced structural changes to the supply of domestic and foreign public debts; and how they have implicated African countries. It follows claims presented by the Organization for Economic Cooperation and Development, OECD (2019), which projected that external private finance inflows to developing economies could drop by USD 700 billion in 2020 compared to 2019 levels, which shows a 60% increase compared to the major disruption caused by the 2008 Global Financial Crisis. Hence, this stirred analytical debates to whether there is an alternative for developing economies to seek foreign or external debts to thrive. In addition, this paper explored dynamics around local borrowing or debts as a suitable alternative option in building sustainable economies compared to foreign borrowing.
Image Compression Techniques Using Linear Algebra with SVD Algorithm

In recent days, the data are transformed in the form of multimedia data such as images, graphics, audio and video. Multimedia data require a huge amount of storage capacity and transmission bandwidth. Consequently, data compression is used for reducing the data redundancy and serves more storage of data. In this paper, addresses the problem (demerits) of the lossy compression of images. This proposed method is deals on SVD Power Method that overcomes the demerits of Python SVD function. In our experimental result shows superiority of proposed compression method over those of Python SVD function and some various compression techniques. In addition, the proposed method also provides different degrees of error flexibility, which give minimum of execution of time and a better image compression.
Library Research Guides: Locating and Using Permalinks for Articles in Library Databases: EBSCO | Business Source Complete

Defines and discusses the use of persistent links to articles found in IU Libraries electronic resources. how to locate the permalink in EBSCO
LibGuides @ URI: The First Next Step: Research and Writing for Librarians: Home

Links to resources mentioned in The First Next Step: Research and Writing for Librarians presentation at the Rhode Island Library Association 2021 Annual Conference, June 4, 2021
PERANCANGAN E-COMMERCE TOKO BLORA MENGGUNAKAN METODE PIECES

This study aims to design e-commerce Toko Blora using the PIECES method. E-commerce is needed to provide wider sales services so that it is expected to increase Blora store turnover. The research method used is a field study with data collection carried out through interviews with parties related to sales at Blora stores and direct observations of the sales process. The results of the research based on the PIECES analysis there are still some problems in the sales process including the slow processing of data and the provision of reports. To solve existing problems, a proposed new system based on e-commerce is developing as a solution to the problems encountered today.