Reviewerly/exHarmony huggingface dataset:
Efficient Multi Keyword Search And Retrieve Encrypted Cloud Data Using Iddfs And Fpgrowth Algorithm

Cloud computing is a new technology that is paving the way for a plethora of new technologies and possibilities. The cloud's use has skyrocketed as a result of the unending advantages it provides. This increase in cloud use encouraged end-users to outsource their documents to the cloud, thus reducing financial burdens. Outsourcing sensitive information to a third-party server is often a risk since the data can be accessed by malicious insiders or outsiders in its original form. Researchers have introduced various schemes for searching outsourced data, but there is often a tradeoff between search efficiency and search capabilities of these searchable encryption schemes. To maintain a balance between search efficiency and search capabilities, a hybrid search scheme that can provide relevant results while preserving search efficiency is needed. The hybrid encrypted multi-keyword search scheme was introduced in this paper. The proposed scheme first conducts a conjunctive keyword search, in which documents containing the entire query words are easily retrieved and sorted based on their relevance to the query. If the number of obtained results exceeds the number of desired results, the search is efficient. If, on the other hand, the results fall below the end user-specified threshold, disjunctive searching is used. The records containing the subset of query words are included in the results of disjunctive searching. The obtained results are rated and shared with the end users. The search efficiency, search accuracy (measured in terms of recall and precision), and rank efficiency are all analyzed.
Learning Large DAGs by Combining Continuous Optimization and Feedback Arc Set Heuristics

Bayesian networks represent relations between variables using a directed acyclic graph (DAG). Learning the DAG is an NP-hard problem and exact learning algorithms are feasible only for small sets of variables. We propose two scalable heuristics for learning DAGs in the linear structural equation case. Our methods learn the DAG by alternating between unconstrained gradient descent-based step to optimize an objective function and solving a maximum acyclic subgraph problem to enforce acyclicity. Thanks to this decoupling, our methods scale up beyond thousands of variables.
Audio Visual Reception of American Sign Language for Blind, Deaf and Dumb People

The computer recognition of sign language is an important process for enabling communication with the visually and hearing impaired people. This proposed project introduces an efficient way of computer recognition of sign languageby using a simplified method by the use of an accelerometer sensor which is a three axis sensor and a voice IC. The main objective of our project is to convert the sign language into a voice format and display the corresponding message on the LCD screen. The basic idea of this project is to have accelerometer sensors attached to the gloves worn by the impaired person. When the person flexes his/her hand for the pre-coded commands, the accelerometer sensors senses the change due to the angular movement of fingers and produces a corresponding output voltage. The sensed analog signal is converted to digital signal by ADC and transmits it to the voice IC via a microcontroller. The objective of the microcontroller is to perform the matching of the obtained hex-code with its corresponding pre-coded commands using Keil software. Once the code is matched with its pre-coded commands the output is delivered through a speaker via a voice IC and the command is also displayed in a LCD screen.
The Comparative Economics of Financial Access in Gender Economic Inclusion

The study has investigated the comparative importance of financial access in promoting gender inclusion in African countries. Gender inclusion is proxied by the female labour participation rate while financial channels include: financial system deposits and private domestic credit. The empirical evidence is based on non-contemporary Fixed Effects regressions. In order to provide more implications on comparative relevance, the dataset is categorised into income levels (middle income versus (vs.) low income); legal origins (French civil law vs. English common law); religious domination (Islam vs. Christianity); openness to sea (coastal vs. landlocked); resource-wealth (oil-poor vs. oil-rich) and political stability (stable vs. unstable). Six main hypotheses are tested, notably, that middle income, English common law, Christianity, coastal, oil-rich and stable countries enjoy better levels of “financial access”-induced gender inclusion compared to respectively, low income, French civil law, Islam, landlocked, oil-poor and unstable countries. All six tested hypothesis are validated. This is the first study on the comparative importance of financial access in gender economic participation.
Comprehensive Study of Hyperspectral Signatures, Petrography and Edx Analysis on Gold Bearing Litho Units of Kempinakote, Nuggihalli Schist Belt, Dharwar Craton, Karnataka, India

The Archean Green Stone Belts (AGSB) having enormous metalliferous deposits like gold , copper, silver, iron and other precious gem stones which are also called as schist belts of Dharwar Craton. The study area Kempinakote lies in southern mos part of Nuggihalli Schist Belt of Hassan district. The schist belt consists commonly two rock types Amphibolites and Ultramafics with their variants. Gold is occurring at or near the amphibolite-ultramafic contact. The Study area comes to Hassan district, Random samples were collected such as Gold bearing Amphibolite schist, Amphibolite, Diorite, Gabbro through GTC (Ground Truth Check). The study carried out by Geological, Petrological, Ore microscopic, SEM-EDX and Hyperspectral signatures using advent high-tech tools of Spectro- Radiometer (Spectral Evolution SR-3500) instrument, DARWin SP.V.1.3.0 and GIS software's. The spectral signatures of the collected samples were derived in laboratory environment to achieve better accuracy. Hyperspectral (350-2500nm) were developed as works mainly on physicochemical and optical properties of the litho unit which help in mapping of gold mineralization at lithological contacts and mineralized zones of amphibolites and ultramafic rocks. The final results highlight the gold specks were noticed through ore microscopy and presence of gold is confirmed through SEM-EDX studies and also spectral characters of Gold bearing amphibolites schist and associated rocks fo better mapping of Kempinakote area of Hassan district in Precambrian basement rocks and similar terrains of Dharwarcraton.
Correlation between Students’ Anxiety and Speaking Ability at English Department of IAIN Palangka Raya

This study was focused to find out if there is or not any correlation between students’ anxiety and speaking ability at the 3 rd semester students of English Department IAIN Palangka Raya. The method of this research was quantitative and correlation was the design of the research. The sample of the research was the 3 rd semester students of English Department at IAIN Palangka Raya consisting of 30 students taken by cluster random sampling technique. The instruments were questionnaire and speaking test. The first instrument was FLCAS questionnaire sheet developed by Horwitz et.al (1986), and the second instrument was speaking test. The result of this research showed that the result of r calculation for students’ anxiety and their speaking test is -.269. Based on the table of interpretation of r value, the result of r calculated (-.269) is between 0.200 and 0.400, This value shows that there is a negative correlation in low correlation criteria. From the significance (2 tailed), the writers get the score .151. It means r>0.05 which showed H o cannot be rejected. The result explained that there is a correlation between two variables, students’ anxiety and speaking ability of 3 rd semester students of English Department IAIN Palangka Raya.
ABOUT PROBLEMS AND METHOD OF TEACHING STUDENTS OF SENIOR CLASSES TO THE BASES OF ALGORITHMIZATION AND PROGRAMMING

Due to the high level of continuous development of science and technology, programming languages are constantly updated and changed. Programming has become an important tool for the development of creative abilities, intelligence and thinking of students, as well as the formation of knowledge, skills. The article discusses the problems of teaching the basics of programming, which allows them to organize, structure, systematize information and knowledge, understand the importance of information modeling, ways of presenting information and use it in the process of reviewing and making decisions, mastering modern information technologies . Algorithmic thinking grows throughout life under the influence of external conditions and additional factors. The need to search for new effective ways of forming algorithmic thinking in adolescents shows the connection of the importance of personality with the aim of further self-knowledge in the modern computerized world. The main purpose of this article is to identify some of the problems of teaching the basics of programming languages and ways to solve them. The study of the topic “Algorithmization and programming” revealed the importance of an effective method for the formation of algorithmic thinking in high school students in the development of algorithms and their application in solving problems.
Comparison of JSON and XML Data Formats in Document Stored NoSql Database Replication Processes

The current trend of solutions in storing large amounts of data is using the NoSQL Database. A document stored is one type of NoSQL database that uses the JavaScript Object Notation (JSON) and eXtensible Markup Language (XML) data formats in data storage. High Availability database is significant to support cloud-based applications and services. Replication is one solution to maintain the consistency of source data and target data. This study aims to determine the performance of JSON and XML data formats in the document stored NoSQL Database replication process. In this study, ArangoDB, RethinkDB, and MongoDB were chosen for use in the trial process of replication from master-server to slave-server with two different data formats, JSON and XML. Data transfer, CPU usage, memory usage, and execution time are measured in each trial. Based on research and experiments that have been carried out, the JSON data format consumes bandwidth with an average value smaller than the XML data format; this occurs in MongoDB, CouchDB, and RethinkDB. In CPU usage, JSON data format, on average, consumes less CPU compared to the XML data format. This is the case with MongoDB. While on CouchDB and RethinkDB, the average CPU usage for XML and JSON data formats does not show a significant difference. The average memory usage for the JSON data format is smaller than the XML data format. The average execution time of the XML data format a little faster than the JSON data format.
Changes in extreme surface solar radiation in EURO-CORDEX Regional Climate Models

&amp;lt;p&amp;gt;As solar energy share is showing a significant growth in the European electricity generation system, assessments regarding long-term variation of this variable related to climate change are becoming more and more relevant for this sector. Several studies analysed the impact of climate change on the solar energy sector in Europe (Jerez et al, 2015) finding light impact (-14%; +2%) in terms of mean surface solar radiation. The present study focuses on extreme values, namely on the distribution of low surface solar radiation (overcast situation) and high surface solar radiation (clear sky situation), since the frequencies of these situations have high impact on electricity generation.&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;The study considers 11 high-resolution (0.11 deg) bias-corrected climate projections from the EURO-CORDEX ensemble with 5 Global Climate Models (GCMs) downscaled by 6 Regional Climate Models (RCMs).&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;Changes in extreme surface solar radiation frequencies show different regional patterns over Europe.&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;The study also includes a case study determining the changes in solar power generation induced by the extreme situations.&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;&amp;amp;#160;&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;&amp;amp;#160;&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;Jerez et al (2015): The impact of climate change on photovoltaic power generation in Europe, Nature Communications 6(1):10014, 10.1038/ncomms10014&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;&amp;amp;#160;&amp;lt;/p&amp;gt;
PERANCANGAN SISTEM INFORMASI AKADEMIK JURUSAN ELEKTORNIKA

In this modern era today, information technology had become a thing where the society have to be cultivated. The development of information technology was currently running very fast and played an important role in various ways. Computers were one of the important parts in improving information technology. This research aimed to design a tool to be able to assist students in obtaining information in an accurate and effective manner. The design of this information system used the System Development Life Cycle (SDLC) method which was a common methodology in system development and design. System Development Life Cycle (SDLC) had six phases, are namely: identification, initiation and project design, analysis, design, implementation and maintenance. The design carried out were the design and normalization of the database, Entity Relations Diagram (ERD), and system design. The system design had Data Flow Diagram (DFD), Use Case Diagram, Activity Diagram, Sequence Diagram and Class Diagram.Keywords: Information System, System Development Life Cycle, Electonic
Multi-level spatial attention network for image data segmentation

Deep learning models for semantic image segmentation are limited in their hierarchical architectures to extract features, which results in losing contextual and spatial information.In this paper, a new attention-based network, MSANet, which applies an encoder-decoder structure, is proposed for image data segmentation to aggregate contextual features from different levels and reconstruct spatial characteristics efficiently.To model long-range spatial dependencies among features, the multi-level spatial attention module (MSAM) is presented to process multi-level features in the encoder network and capture global contextual information.In this way, our model learns multi-level spatial dependencies between features by the MSAM and hierarchical representations of the input image by the stacked convolutional layers, which means the model is more capable of producing accurate segmentation results.The proposed network is evaluated on the PASCAL VOC 2012 and Cityscapes datasets.Results show that our model achieves excellent performance compared with U-net, FCNs, and DeepLabv3.
IMPROVED BIG DATA SECURITY IN CLOUD INFRASTRUCTURE USING AUTHENTICATION BASED ENCRYPTION MODEL

With rapid increase in accumulation of data sources, the security over big data storage is a challenging task. Various issues arise with big data security that includes data privacy, data integrity, and security over infrastructure. Various cryptographic algorithms are developed to secure the big data in cloud, however most algorithm often poses leakage of data. In this paper, a novel solution is developed using secured authentication of bigdata in cloud. It involves authentication of input data in the cloud and security of data using an encryption algorithm in the form of blocks. The validation is conducted on various performance metrics over different attacks to check the efficacy of authentication based security. The simulation results show that the proposed model achieves higher security level than other state-of-art models.
Learning Outside Classroom: Challenges And Opportunities Of Remote Learning During Remote Working In An Indian Commercial Vehicle Organization

Brick and mortar commercial vehicle organizations are adapting to work from home or remote working style. Organizations for a long time have been used only to a physical nature of work and not remote working especially in the commercial vehicle space in India. While this transition is a big change, there are definitive challenges in proving Learn from Home initiatives considering volatility, ambiguity, uncertainty and complexity of the of the environment. While organization who were already in the digital learning space are transitioning to the Learn from Home way, it has been hard for organizations who don’t have learning management systems and digital collaboration tools. Anxiety management of executives and providing digital awareness for successfully taking up learning remotely are the key during difficult times. It’s also very important to sustain the learning agenda keeping in mind various restrictions like social distancing norms and operating in frugal budgets. Learning during this time apart from being a capability building tool is also thought of as an engagement tool.
Block chain: IoT security, privacy and resource challenges

The purpose of the paper is to provide a vague overview of the latest blockchain use cases in the information technology industry, in particular, the Internet of Things (IoT). Respective use cases have been discussed in current research articles, Master Theses, business white papers, and industry expert blogs. The paper also provides an overview of the technical aspects of the blockchain and IoT. This paper offers a comprehensive overview of the history and current situation of IoT security, privacy, and resource challenges centred on blockchain technology. It helps understand the need for a comprehensive blockchain feasibility model to be created.
IMPROVEMENT OF DEMONSTRATION EXPERIMENTS OF THE PHYSICS COURSE BASED ON THE USE OF DIGITAL TECHNOLOGIES

The most characteristic problem of the current stage of modernization of higher education is the training of future physics teachers who are ready to adapt to the conditions of a rapidly changing digital society and are able to independently set and solve professional problems. The purpose of the article is to identify the features of the use of digital technologies in improving the demonstration experiments of the physics course. The leading method to study this problem is the modeling method, the study and analysis of psychological, pedagogical, scientific, technical and methodological literature. The use of computer models of various physical processes and phenomena contributes to the effectiveness of teaching students the physics course, allows them to understand the mechanisms of physical phenomena and processes. In this regard, at present, the Abai Kazakh National pedagogical University pays great attention to the development of a technical base for the use of digital technologies in the training of future physics teachers, and electronic resources are being developed for use in teaching physics. By using computer models of experiments and demonstrations, it is possible to compensate for the lack of equipment in the physical laboratory and thus teach students to independently acquire physical knowledge during a physical experiment on virtual models, that is, there is a real opportunity to form students ' creativity and increase cognitive activity in physics.
Certain Exploration of Code Smell Empathy and Refinement Employing Random Decision Forest Classifier

All through the changes in the source code there might be anmanifestation of smell. Fowler calls as a Bad Smell which is the symbols of potential problem in the code that may necessitate a refactoring. Inexperienced software felt challenging to resolve the bad smells even with the help of refactoring tools. The intention is that the developers don’t know where to invoke the refactoring tools and how to choose the refactoring tools for identifying various code smells. For this projected a framework to perform refactoring promptly. By this the developer may refactor the bad smells and resolves them promptly. Refactoring is a well-established practice that aims at improving the internal structure of a software system without changing its external behavior. Existing literature provides evidence of how and why developers perform refactoring in practice. In this proposed work to continue on this line of research by support the MICA-GRDFC technique is more capable and operative than state of the art refactoring techniques practices in 200 open source systems..
한국어 교재의 읽기 과목에 대한 텍스트기호학적 분석

This study aims to examine the reading subjects of Korean textbooks from the viewpoint of text-semiotics. In this study, we propose a two-step analysis procedure. The first step is to identify the characteristics of the communicators and to understand how the text is accepted by the communicators. Here, classroom situation, class situation, teacher and learner situations are analyzed. The second step is to analyze the text structure. In this step, the partial text is determined, and the communication function of the partial text is analyzed. Next, we will look at how language symbols and visual symbols are used, and how language symbols and visual symbols are related to each other. Finally, this paper analyzes the function of partial text in each class stage and attempts to understand what it means in teaching and learning. The advantages of text analysis using text-semiotics are as follows. First, the relationship between the language symbol and the visual symbol of the textbook can be grasped systematically. Second, textbooks can be analyzed in terms of ‘production’ and ‘acceptance’. Third, the problems of ‘input’ and ‘output’ in Korean textbooks can be grasped. Fourth, it can be judged whether the design of the textbook is appropriate.
Robots that Help Humans Build Better Mental Models of Robots

Interactive Task Learning (ITL) is an approach to teaching robots new tasks through language and demonstration. It relies on the fact that people have experience teaching each other. However, this can be challenging if the human instructor does not have an accurate mental model of a robot. This mental model consists of the robot’s knowledge, capabilities, shortcomings, goals, and intentions. The research question that I investigate is “How can the robot help the human build a better mental model of the robot?”
FAIR Geospatial Data

Geospatial data are a valuable product of research with potential for sharing and reuse that can be especially powerful for maximizing resources and accelerating the pace of scientific discovery. However, the complex structures, myriad formats, and unique dependencies of GIS technologies, data, and metadata can create barriers to making them findable, accessible, interoperable, and reusable (FAIR). In this webinar, presenters will discuss how geospatial data can be aligned with FAIR principles and how librarians can support this effort in concert with researchers.