Reviewerly/exHarmony huggingface dataset:
Application of metaheuristic optimization algorithms for image registration in mobile robot visual control

Visual Servoing (VS) of a mobile robot requires advanced digital image processing, and one of the techniques especially fitting for this complex task is Image Registration (IR). In general, IR involves the geometrical alignment of images, and it can be viewed as an optimization problem. Therefore, we propose Metaheuristic Optimization Algorithms (MOA) for IR in VS of a mobile robot. The comprehensive comparison study of three state-of-the-art MOA, namely the Slime Mould Algorithm (SMA), Harris Hawks Optimizer (HHO), and Whale Optimization Algorithm (WOA) is presented. The previously mentioned MOA used for IR are evaluated on 12 pairs of stereo images obtained by a mobile robot stereo vision system in a laboratory model of a manufacturing environment. The MATLsoftware package is used for the implementation of the considered optimization algorithms. Acquired experimental results show that SMA outperforms HHO and WOA, while all three algorithms perform satisfactory alignment of images captured from various mobile robot poses.
Deep semi-supervised learning with weight map for review helpfulness prediction

Helpful online product reviews, which include massive information, have large impacts on customers? purchasing decisions. In most of e-commerce platforms, the helpfulness of reviews are decided by the votes from other customers. Making full use of these reviews with votes has enormous commercial value, especially in product recommendation. It drives researchers to study the technologies about how to evaluate the review helpfulness automatically. Although Deep Neural Network(DNN), learning from the historical reviews and labels, computed by the votes, has demonstrated effective results, it still has suffered insufficient labeled reviews problem. When the helpfulness of a large number of reviews is unknown for lack of votes, or some useful latest reviews with less votes are submerged by the past reviews, the accuracy of current DNN model decreases quickly. Therefore, we propose an end-to-end deep semi-supervised learning model with weight map, which makes full use of the unlabeled reviews. The training process in this model is divided into three stages:obtaining base classifier by less labeled reviews, iteratively applying weight map strategy on large unlabeled reviews to obtain pseudo-labeled reviews, training on above combined reviews to obtain the re-training classifier. Based on this novel model, we develop an algorithm and conduct a series of experiments, on Amazon Review Dataset, from the aspects of the baseline neural network selection and the strategies comparisons, including two labeling and three weighting strategies. The experimental results demonstrate the effectiveness of our method on utilizing the unlabeled data. And our findings show that the model adopted batch labeling strategy and non-linear weight mapping method has achieved the best performance.
THE INFLUENCE OF PARENT'S ATTENTION, REWARD GIVING, AND LEARNING FACILITIES ON STUDENTS LEARNING MOTIVATION

This study aims to determine how parental attention, reward, and learning facilities influence learning motivation. The method used in this research is using quantitative methods. The population in this study were students of the Faculty of Economics at the Jakarta State University with a sample of 100 respondents. Data collection techniques using a questionnaire or questionnaire. Data were processed and analyzed using IBM SPSS Statistics 23.0. The results showed that : (1) There is a significant effect between Parents' Attention, Reward Giving, and Learning Facilities on Learning Motivation, indicated by the sig value—amounting to F count 46,856 > F table 2.47. (2) There is an influence between Parents' Attention, Giving Rewards, and Learning Facilities on Learning Motivation, as indicated by the adjusted R square value of 58.2%. While the remaining 41.8% is influenced by other variables.
The Rational Thinking of Flipped Classroom Overturns the Traditional Classroom in College English Teaching

NOTICE OF RETRACTION: While investigating potential publication-related misconduct in connection with the ICIMTech 2021 Conference Proceedings, serious concerns were raised that cast doubt on the integrity of the peer-review process and all papers published in the Proceedings of this Conference. The integrity of the entire Conference has been called into question. As a result, of its investigation, ACM has decided to retract the Entire Conference Proceedings and all related papers from the ACM Digital Library.
INVESTIGATING ENVIRONMENTAL RESPONSES TO BANDITRY ACTIVITIES IN BATSARI LOCAL GOVERNMENT AREA KATSINA STATE, NIGERIA

Banditry remains a major security challenge that has created instability, debilitated governance and exposed the environment to numerous threats, thus hindering socio-economic development. This research investigated the environmental reaction to the surging activities of banditry. A mixed research approach was used in which case quantitative data was obtained through a well-structured questionnaire admitted to systematically-selected 300 respondents from two districts in Batsari. Qualitative data was acquired through purposively-selected ten (10) knowledgeable informants; five from each district. The collected data was analyzed through descriptive statistics. The results showed that banditry is in earnest and has hampered human activities that indirectly have the environment captivated, farmlands are left barren, infrastructures devastated, forests razed and fear has been incited to the inhabitants. All these collectively affect the environment as socio-economic development is almost not possible. Further results reveal that the inhabitants are collaborating with security agents and the government to avert banditry so as to save their environment. This research recommends that the government should put in more effort to save the Batsari Environment from the effects of Banditry activities as a long –term consequence will be vile and catastrophic
Improving Document-Level Sentiment Classification with User-Product Gated Network

Document-level sentiment classification is a fundamental task in Natural Language Processing (NLP). Previous studies have demonstrated the importance of personalized sentiment classification by taking user preference and product characteristics on the sentiment ratings into consideration. The state-of-the-art approaches incorporate such information via attention mechanism, where the attention weights are calculated after the texts are encoded into the low-dimensional vectors with LSTM-based models. However, user and product information may be discarded in the process of generating the semantic representations. In this paper, we propose a novel User-Product gated LSTM network (UP-LSTM), which incorporates user and product information into LSTM cells at the same time of generating text representations. Therefore, UP-LSTM can dynamically produce user- and product-aware contextual representations of texts. Moreover, we devise another version of it to improve the training efficiency. We conduct a comprehensive evaluation with three real world datasets. Experimental results show that our model outperforms previous approaches by an obvious margin.
A Critique of the Smooth Inverse Frequency Sentence Embeddings

We critically review the smooth inverse frequency sentence embedding method of Arora, Liang, and Ma (2017), and show inconsistencies in its setup, derivation, and evaluation.
Sistem Pakar Deteksi Keaslian Toner HN Dengan Menggunakan Metode Certainty Factor

An expert system is defined as a branch of artificial intelligence that uses special knowledge possessed by an expert to solve a particular problem quickly. This study aims to design an expert system to detect the authenticity of HN toner using the certainty factor method. So that with this system, women know the difference between genuine and unoriginal HN toners and how to deal with them before consulting or buying drugs to a beauty doctor.
Testing the Augmented Reality Functional Suitability of Wood as Raw Materials for Typical Crafts of East Borneo

Borneo wood which has good quality wood for building basic materials and other purposes, has been widely known and sold. There are more than 120 types of wood, some of them are from Kalimantan. Cempaka wood, Putat wood, Mentibu wood, Meranti wood, Merbabu wood, Ironwood, Teak wood, and Sengon wood are included in various types, both endemic and from Borneo. This research is a continuation of previous research that has been running on the initial development of AR Wood, Raw Materials for Typical Crafts of East Borneo. This research aims to test forestry applications for information or socialization of the classification and utilization of East Borneo typical wood. This research objective can be achieved by using Augmented Reality (AR) technology which can incorporate almost all levels of virtual object society into the real environment and also use Android mobile technology. Therefore, functional adequacy testing is needed, namely black box testing and usability to find out how much the system can meet the needs if it is used as a raw material for East Borneo handicrafts, where the results of black box testing are good in test results for six features and standards. When using wood as a raw material, tests are carried out. With good classification results, usability reached 71.6%.
GLMs for Count Data

GLMs using the Poisson distribution are a good starting place when dealing with integer count data. The default log link function prevents the prediction of negative counts and the Poisson distribution models the variance (approximately equal to the mean).
NFC Enabled Wearable Device Connected With Android Smart Phone For Virtual Assistant Applications

Near Field Communication (NFC) as promising short range remotecorrespondence innovation encourages versatile use of billions of individuals all throughthe earth that differing administrations going from installment and devotion applicationsto get to keys for workplaces and houses. Our point is to style the keen Gravity Watchwhich can be a wearable detecting, notice, and processing stage incorporated with a wristwatch. NFC correspondence gives a remote connect to a phone or fixed PC. Gravity Watchprovides tactile and visual notification. The framework gives plentiful preparing capacitiesmulti day battery life empowering reasonable client studies. The proposition of anincorporated android application upheld area data. Making and altering occasion contentframework to transfer area data, search work utilizing area data of the client and showingindexed lists on the screen. This system also remind the regular water consumptionnotification period of your time. Smartphone applications could help with this cycle bysupporting propensity development. Present plan rules for mediations that may upholdpropensity arrangement through relevant signs and execution goals.
Territorial features of the development of higher education in Russia

The purpose of our study was to evaluate the indicators characterizing the presence of higher education organizations in the regions of Russia, as well as the proportion of students in the total population of each of the regions. The study used as initial information official statistical data on the development of higher education in 82 regions of Russia in 2020, as well as information on the population in each of the regions. We have developed econometric models that describe the distribution of access to higher education in the regions. The study allowed us to estimate the number of higher education organizations per one million residents and the share of students in the population of the regions. The regions with the maximum and minimum values of the considered indicators were identified. It is shown that higher education has received significant development in Russia.
Scientometric Analysis of Gravity Research Output: A Global Perspective

The main objective of this investigation is to know the applicability of Bradford's law in gravity research. A total of 75342 records has been retrieved from the Web of Science citation database for a period of 10 years i.e. from 2010 to 2019. The study examined the year-wise research output, communication channels preferred by the researchers, and most productive journals in gravity literature. The analysis of the study revealed that there is an increasing trend in terms of research productivity during the period. The maximum number of research papers are published in the year 2018 with 9407 records followed by 2019 with 9239 records and very less i.e. 5855 papers are recorded in the year 2010. The majority of the researchers prefer to publish their research articles in the form of journal articles. The rank list of most productive journals shows that Physical Review D is in 1st rank by contributing 5814 papers. The application of the Bradford law of scattering does not fit into the Bradford zones.
Sistem Rekomendasi Berbasis Collaborative Filtering untuk Toko Kosmetik Beauty Gallery

Beauty Gallery is a business that focuses on selling cosmetics. After running the business, the process of recording and processing data is still done using paper media. Because the process has not been computerized, problems often occur in the sales process because the data is not appropriate. This study aims to produce a web-based application that can record all sales, purchase, and stock data at the Beauty Gallery using a database. This application is also made with an attractive and easy to use web display. This website also uses a collaborative filtering recommendation system that will help customers to choose goods. The data source used in making this application is through question and answer with the Beauty Gallery and needed literature. With this application, it is expected that Beauty Gallery data recording can be more organized and can advance the Beauty Gallery.
Interactive analysis of large code bases (invited talk)

Current static source code analyzers can be slow, hard to use correctly, and expensive. If not properly configured, they can also generate large amounts of output, even for well-written code. To fix this, we developed a new tool called Cobra. The Cobra tool can be used interactively even on very large code bases, which means that it is very fast. It is also designed to be easy to use and free. The tool comes with a library of predefined queries to catch standard coding issues, including cyber-security related risks from the CVE database. The query libraries can be used as provided, but the best part is that you can also easily modify and extend those queries, or add your own. The Cobra tool is language neutral: you can use it to check C or C++ code, or Java, Ada, Python, or even English prose. I'll show how the tool works, what makes it fast, and how you can write powerful queries in a couple of different ways.
ALTERNATIVE LEARNING APPROACHES IN A PANDEMIC CONTEXT: FROM THE COLLABORATIVE OUTDOOR EDUPARK GAME TO A VIRTUAL VISIT

Over the last years, there has been much interest in mobile technologies for teaching and learning.Despite the focus on these emerging and innovative tools, mobile learning (mLearning) is facing many challenges.In response to the spread of the COVID-19 pandemic, many schools implemented eLearning or mLearning during school closures.However, mobile activities that involve collaboration of students in outdoor environments need to be rethought, as social distance is advised.This paper provides a pragmatic example of how to respond in the uncertain present, and plan systematically for an unpredictable, post-pandemic future.It presents an alternative learning approach in a pandemic context, as the EduPARK can be explored in a virtual visit, reinforcing the connection between physical and virtual learning spaces.This way learning can happen without face-to-face interactions for those who are avoiding physical contact, for those who are far away from the location, or even for students with motor difficulties.As a reflection, the authors examine major educational changes that have recently been imposed upon teachers and educational researchers, as well as key aspects of the current emergency response in education, and resultant implications for educational technology and mLearning.
CLOUD IDENTITY AND ACCESS MANAGEMENT – A MODEL PROPOSAL

This paper discusses the application of cloud identity and access management in digital transformation and cloud adoption. Cloud computing refers to a fusion of various technologies, including grid computing and distributed computing, that makes use of the Internet as a service delivery network [1]. Organizations need the ability to choose the services and pricing models that best meet their needs, and budgetary constraints. It is the cloud service providers that set the price model for their cloud services, considering factors like instance size, utilization size (per hour), users’ size (per user), infrastructure size (per hour), and service size (per service). The majority of businesses are hosting or implementing web services in a cloud infrastructure for the convenience of administration and increased availability [1]. Multi-tenant setups are also utilized in cloud-based services to reduce the cost considerations associated with the services. To accomplish multi-tenancy in the cloud, virtual environments are utilized. A vulnerability in virtual machines poses a direct danger to the privacy and security of the people who are using them [2]. Security problems in cloud web services are found in particular areas such as authentication, authorization, data protection, and accountability, among other things. Cloud security is critical to business and technology sustainability. Even though the boundary is largely static and monitored and managed by the IT unit in a centralized company where functionality is being implemented within the perimeter of the company, the trust limit becomes dynamic and moves beyond IT command with the adoption of cloud computing [3]. A company's servers, systems, and applications boundaries will expand into the service provider domains. In addition, this loss of control poses a threat to the existing trusted governance and management paradigm, and it has the potential to impede the adoption of cloud services inside an organization if it is not handled correctly. Modern organizations understand the significance of protecting identities in the era of remote work and cloud hosting inside the zero boundaries, Zero Trust. However, as the quantity, kinds, and interrelationships of identities continue to grow exponentially across cloud settings, it is clear that this is a far easier said than done task [3]. Cloud security is widely acknowledged to be a shared duty between cloud service providers and their customers, at least in theory. In the process of determining the best Identity and Access Management (IAM) policies for these settings, many companies are confronted with issues such as: When are cloud-native technologies adequate, and when should we explore specialist solutions from other vendors? With all of the IT security jargon flying about, it may be difficult to discern the difference.
The Effect of Hiragana Memory Hint Application to Increase Motivation and Interest in Learning Hiragana Among University Students

This study aims to determine the effect of using Hiragana Memory Hint on motivation and interest in learning hiragana among students in the 2020/2021 academic year. The study participants consisted of 50 first-year students taking university courses in Japanese. They were divided into two groups. The control group consisted of 25 students who were taught the traditional way. In comparison, the experimental group consisted of 25 students who were taught using a combination of Hiragana Memory Hint and traditional learning. The results showed that there were differences in students motivation and interest in the experimental group and the control group. Furthermore, the difference between the two groups showed that the Hiragana Memory Hint application in learning hiragana could increase the motivation and interest of the experimental group students.
Processing Natural Language Queries in Semantic Web Applications

SPARQL is a powerful query language for an ever-growing number of Semantic Web applications.Using it, however, requires familiarity with the language which is not to be expected from the general web user.This drawback has led to the development of Question-Answering (QA) systems that enable users to express their information needs in natural language.This paper presents a novel dependency-based framework for translating natural language queries into SPARQL queries, which is based on the idea of syntactic parsing.The translation process involves the following five steps: extraction of the entities, extraction of the predicate, categorization of the query's type, resolution of lexical and semantic gaps between user query and domain ontology vocabularies; and construction of the SPARQL query.The proposed framework was tested on our closed-domain student advisory application intended to provide students with advice and recommendations about curriculum and scheduling matters.The advantage of our approach is that it requires neither any laborious feature engineering, nor complex model mapping of a query expressed in natural language to a SPARQL query template, and thus it can be easily adapted to a variety of domains.
Multi-level Attention Feature Network for Few-shot Learning

Existing few-shot methods have problems that feature extraction scale is single, the learned class representations are inaccurate, the similarity calculation still relies on standard metrics. In order to solve the above problems, multi-level attention feature network is proposed. Firstly, the multiple scale images are obtained by scale processing, the features of multiple scale images are extracted and the image-level attention features are obtained by the image-level attention mechanism to fusion them. Then, class-level attention features are learned by using the class-level attention mechanism. Finally, the classification is performed by using the network to compute the similarity scores between features. The proposed method is evaluated on the Omniglot dataset and the MiniImagenet dataset. The experimental results show that multi-level attention feature network can further improve the classification accuracy under small sample conditions compared to the single-scale image features and average prototypes.
A Very Short Introduction to R

One of the challenges of learning analysis is that it means getting to grips with both the statistics and the software being used to implement it. The book has tried to strike a balance that keeps the R code relatively short and simple, especially at the start of the book. This chapter gives only a very brief introduction to R tailored to its use in this book. This very short introduction aims to be enough to allow offline readers to understand the basics of R so that they can focus on the applied details of the statistical analysis that are the real focus of the book.