Reviewerly/exHarmony huggingface dataset:
Improving Students’ Literation Through The Implementation of Mind Mapping-Based Discovery Learning Model

Abstract: This study aims to apply the discovery learning model based on concept maps to improve student literacy. As many as 25 students of class VIIIF SMPN 16, were chosen as the research sample. Literacy ability is measured by observation using an observation sheet with regard to student attitudes when discovery through reading activities. Students' responses to learning are also measured through focus group interview (FGI). The results showed that the application of this model could improve the literacy culture of students with a score of 74.67 and the average student learning outcomes obtained a score of 81.53 which included good categories. Keywords: discovery learning, mind mapping, literacy. Abstrak: Penelitian ini bertujuan untuk menerapkan model pembelajaran penemuan berbasis peta konsep dalam meningkatkan literasi siswa. Sebanyak 25 orang siswa kelas VIIIF, SMPN 16 dipilih sebagai sampel penelitian. Kemampuan literasi diukur dengan pengamatan menggunakan lembar observasi berkenaan dengan sikap siswa ketika penemuan melalui aktivitas membaca. Respon siswa dalam pembelajaran juga diukur melalui focus group interview (FGI). Hasil penelitian menunjukkan bahwa penerapan model ini dapat meningkatkan budaya literasi siswa dengan skor 74,67 dan rata-rata hasil belajar siswa diperoleh skor 81,53 yang termasuk kategori baik. Kata kunci: pembelajaran penemuan, peta konsep, literasi. DOI:  http://dx.doi.org/10.23960/jpmipa/v20i1.pp12-16
HIBGE: Monitorable and Traceable Anonymous Encryption in Cloud Computing

Privacy problem is an emerging concern when we protect information security in cloud computing. In some application scenarios, the users may require to receive the computing results anonymously from the cloud server. However, complete anonymity brings regulatory issues to practical use. We usually need the encrypted results monitorable by superiors in case of illegal information, and once there are some disputes occurring, a trusted third-party arbitration institution is also required to trace the recipients without any need of decryption. Aiming at this problem, we propose a new cryptographic primitive named Hierarchical Identity-Based Group Encryption (HIBGE). In an HIBGE system, the recipients are organized in a tree-like structure. Recipients with the same function or belonging to the same department make up a group and they are managed by a group manager. When encrypting, the cloud can use the recipient’s identity as the public key, and others only know which group the message is sent to, but cannot know the exact recipient’s identity. Besides, the higher level can monitor the lower’s message, and group manager can trace the recipients’ identity. We then construct a concrete HIBGE scheme, and prove this scheme is semantic secure, anonymous and traceable, with a perfectly zero-knowledge proof. HIBGE scheme can be widely applied in cloud computing.
Leveraging experience in lazy search

Lazy graph search algorithms are efficient at solving motion planning problems where edge evaluation is the computational bottleneck. These algorithms work by lazily computing the shortest potentially feasible path, evaluating edges along that path, and repeating until a feasible path is found. The order in which edges are selected is critical to minimizing the total number of edge evaluations: a good edge selector chooses edges that are not only likely to be invalid, but also eliminates future paths from consideration. We wish to learn such a selector by leveraging prior experience. We formulate this problem as a Markov Decision Process (MDP) on the state of the search problem. While solving this large MDP is generally intractable, we show that we can compute oracular selectors that can solve the MDP during training. With access to such oracles, we use imitation learning to find effective policies. If new search problems are sufficiently similar to problems solved during training, the learned policy will choose a good edge evaluation ordering and solve the motion planning problem quickly. We evaluate our algorithms on a wide range of 2D and 7D problems and show that the learned selector outperforms baseline commonly used heuristics. We further provide a novel theoretical analysis of lazy search in a Bayesian framework as well as regret guarantees on our imitation learning based approach to motion planning.
DAMPAK PENGGUNAAN APLIKASI ONLINE DICTIONARY PADA PEMBELAJARAN BAHASA INGGRIS DI MA AL-HIDAYAH MUARA TELANG

The purpose of the reseach is to find students’ perceptions in using Online Dictionary in learning English. This study uses qualitative studies using those that aim to find the impact using Online Dictionary. The object of this research is the students of MA Alhidayah Muara Telang. The author takes 30 students of grade X science using the online dictionary as a sample. The method used by the author is the questionnaire. There are two impacts in this study, namely positive and negative impacts, the positive impact is that students easily understand the reading text, motivating students to be active in English. While the negative impact is the weak internet connection and the lack of funds in fulfilling the Quota.
Mobile Cloud Computing as Competence Intensifier

Since the arrival of mobile devices, such as Personal Digital Assistants (PDA’s), smartphones, tablets etc., and their amalgamation with cloud computing is bringing up and transforming ubiquitous computing into actual existence. This concept of ubiquitous computing straightens out the way to unusual and experimental applications, in which the mobile devices are integrated and provide assistance to the users. This paper discusses about the concept of mobile cloud computing, identify advantages and disadvantages of collaborating mobile applications with cloud and identify benefits of leveraging mobile learning services on cloud. Mobile cloud computing induces innumerable benefits and overcomes the technical limitations of mobile learning.
Above the Clouds: A Brief Survey

Cloud Computing is a versatile technology that can support a broad-spectrum of applications. The low cost of cloud computing and its dynamic scaling renders it an innovation driver for small companies, particularly in the developing world. Cloud deployed enterprise resource planning (ERP), supply chain management applications (SCM), customer relationship management (CRM) applications, medical applications, business applications and mobile applications have potential to reach millions of users. In this paper, we explore the different concepts involved in cloud computing and we also examine clouds from technical aspects. We highlight some of the opportunities in cloud computing underlining the importance of clouds showing why that technology must succeed and we have provided additional cloud computing problems that businesses may need to address. Finally, we discuss some of the issues that this area should deal with.
PENGARUH PENGELOLAAN KEARSIPAN TERHADAP EFISIENSI KERJA DI KANTOR PERPUSTAKAAN DESA WANASARI KECAMATAN MUARA WAHAU

Administrative activities in an office basically also have a result like other units. Results or products from an office are letters, forms, and reports. The management of the letters, forms, and reports produced and received by an office will ultimately relate to archives. So, the administrative activity is basically produce, receive, process, and store various letters, forms, reports and so forth. Wanasari Village Library Office Muara Wahau District is one of the government offices that serve the public interest that will not be separated from archival activities. Office that daily takes care of making library member cards, storing documents or other letters, making the office has a variety of archives. Success in archival management is determined by many things. Filing factors such as storage systems, archiving employees, archival equipment, and of course with the work environment. The management of archives conducted in the Wanasari Village Library Office of Muara Wahau Subdistrict is very influential to the success or failure of the rediscovery of archives.
Summary of Book

This chapter gives concluding remarks of this book with a brief summary of presented audio watermarking. The future research direction in this research area is also discussed in this chapter.
OPTIMAL: A NEW MACHINE LEARNING APPROACH FOR GDGT-BASED PALAEOTHERMOMETRY

Abstract. In the modern oceans, the relative abundances of Glycerol dialkyl glycerol tetraether (GDGTs) compounds produced by marine archaeal communities show a significant dependence on the local sea surface temperature at the site of formation. When preserved in ancient marine sediments, the measured abundances of these fossil lipid biomarkers thus have the potential to provide a geological record of long-term variability in planetary surface temperatures. Several empirical calibrations have been made between observed GDGT relative abundances in late Holocene core top sediments and modern upper ocean temperatures. These calibrations form the basis of the widely used TEX86 palaeothermometer. There are, however, two outstanding problems with this approach, first the appropriate assignment of uncertainty to estimates of ancient sea surface temperatures based on the relationship of the ancient GDGT assemblage to the modern calibration data set; and second, the problem of making temperature estimates beyond the range of the modern empirical calibrations (&gt; 30 ºC). Here we apply modern machine-learning tools, including Gaussian Process Emulators and forward modelling, to develop a new mathematical approach we call OPTiMAL (Optimised Palaeothermometry from Tetraethers via MAchine Learning) to improve temperature estimation and the representation of uncertainty based on the relationship between ancient GDGT assemblage data and the structure of the modern calibration data set. We reduce the root mean square uncertainty on temperature predictions (validated using the modern data set) from ~ ±6 ºC using TEX86 based estimators to ±3.6 ºC using Gaussian Process estimators for temperatures below 30 ºC. We also provide a new but simple quantitative measure of the distance between an ancient GDGT assemblage and the nearest neighbour within the modern calibration dataset, as a test for significant non-analogue behaviour. Finally, we advocate against the use of temperature estimates beyond the range of the modern empirical calibration dataset, given the absence – to date – of a robust predictive biological model or extensive and reproducible mesocosm experimental data in this elevated temperature range.
Development of a Perceptual Distance Monitor (PDM) for client-contractor collaborations

Client-contractor relationships are increasingly important in determining project outcomes. Although clients benefit from good relationships, clients and contractors often have different perceptions regarding relationship issues in their projects. In this study, we apply agency theory and social identity theory to understand why perceptions differ between collaborating partners. Furthermore, we investigate through a literature study on which relationship issues perceptual distance can be expected. For this, we use the input-process-output model. In two phases we gathered quantitative data from both clients and contractors. The first phase serves as a pilot phase in which the questionnaire is tested. The second phase is used to test for perceptual distance and its predictive validity of project outcomes and trust. We found that buyer and supplier representatives showed perceptual distance on multiple relationship issues. Moreover, we found that perceptual distance was mostly negatively but sometimes also positively related with project outcomes and trust, proving perceptual distance an important topic for project managers.
Secure Exchange of Digital Goods in a Decentralized Data Marketplace

We are tackling the problem of trading real-world private information using only cryptographic protocols and a public blockchain to guarantee honest transactions. In this project, we consider three types of agents --buyers, sellers and notaries-- interacting in a decentralized privacy-preserving data marketplace (dPDM) such as the Wibson data marketplace. This framework offers infrastructure and financial incentives for individuals to securely sell personal information while preserving personal privacy. Here we provide an efficient cryptographic primitive for the secure exchange of data in a dPDM, which occurs as an atomic operation wherein the data buyer gets access to the data and the data seller gets paid simultaneously.
Methods of Countering Cyber Recruitment (Iraq Model)

Connecting to the electronic information network (internet) became the most characteristic that distinguish this era However , the long hours which young men daily spend on the internet On the other hand ,there are many people who are waiting for the chance to talk and convince them with their views This will lead the young people to be part in the project of the “cyber armies “that involved with states and terrorist organizations&#x0D; This project has been able to recruitment hundreds of people every day to work in its rank . It is very difficult to control these websites because we can see the terrorist presence in all its forms in the internet In addition there are many incubation environments that feed in particular the young people minds &#x0D; Because they are suffering from the lack of social justice Also the unemployment, deprivation , social and political repression So , that terrorist organizations can attract young people through the internet by convincing them to their views and ideas . So these organizations will enable to be more stronger.
Imbalance-XGBoost: Leveraging Weighted and Focal Losses for Binary Label-Imbalanced Classification with XGBoost

The paper presents Imbalance-XGBoost, a Python package that combines the powerful XGBoost software with weighted and focal losses to tackle binary label-imbalanced classification tasks. Though a small-scale program in terms of size, the package is, to the best of the authors' knowledge, the first of its kind which provides an integrated implementation for the two losses on XGBoost and brings a general-purpose extension on XGBoost for label-imbalanced scenarios. In this paper, the design and usage of the package are described with exemplar code listings, and its convenience to be integrated into Python-driven Machine Learning projects is illustrated. Furthermore, as the first- and second-order derivatives of the loss functions are essential for the implementations, the algebraic derivation is discussed and it can be deemed as a separate algorithmic contribution. The performances of the algorithms implemented in the package are empirically evaluated on Parkinson's disease classification data set, and multiple state-of-the-art performances have been observed. Given the scalable nature of XGBoost, the package has great potentials to be applied to real-life binary classification tasks, which are usually of large-scale and label-imbalanced.
LibGuides: Library Contact: Contact the Library

Connect with Link Library. This guide contains contact information, hours of operation, holiday schedule, and other general information about visiting or getting in touch General contact information and schedule
Generalized Quantum Correlations in Continuous Variable Systems

In this chapter we focus on extending our view point to the case of continuous variable systems. We do this by studying a nonlocal intermediate model of quantum computation. Although many of the required tools for a discrete-continuous variable unification are still missing from the theory of coherence, we argue that quantum correlations in both streams have equivalent structures.
Bias Disparity in Collaborative Recommendation: Algorithmic Evaluation and Comparison

Research on fairness in machine learning has been recently extended to recommender systems. One of the factors that may impact fairness is bias disparity, the degree to which a group's preferences on various item categories fail to be reflected in the recommendations they receive. In some cases biases in the original data may be amplified or reversed by the underlying recommendation algorithm. In this paper, we explore how different recommendation algorithms reflect the tradeoff between ranking quality and bias disparity. Our experiments include neighborhood-based, model-based, and trust-aware recommendation algorithms.
AN APPRAISAL OF THE COMPUTING KNOWLEDGE AND SKILLS OF STUDENTS WITH DISABILITIES IN THE UNIVERSITY OF EDUCATION, WINNEBA, GHANA

The study examined the knowledge and skills in using computers by students with disabilities at the University of Education, Winneba (UEW). The study employed the descriptive survey design which used a questionnaire to solicit information from forty-six (46) students who were randomly sampled. The study found out that majority of the students had some knowledge and skills in the use of computers. However, it was established from the results, that many of the students’ knowledge and skills about computers were inadequate for higher education level academic work. Finally, the study found out that the type of disability a person has, does not have any significant influence on their knowledge and skills for computer usage. It was recommended that students with disabilities should have access to specialized Information and Communication Technology (ICT) centre where adaptive services would be organized and delivered to enhance their skills. This ICT centre should be manned by competent ICT personnel who understand the needs of students with disabilities, and whose main responsibilities should be to attend to the technological needs of students with disabilities. Article visualizations:
STUDI KASUS : GAMBARAN DIRI PASIEN AMPUTASI COPART PROSTHESIS AKIBAT TRAUMA KECELAKAN UNTUK PASIEN AMPUTASI ANKLE

Direct accidents are accidents that result in defects or damage to limbs that lead to amputation. While indirect accidents are accidents which cause one aspect of life to be disturbed, especially on economic and social aspects. Individuals who experience a traumatic event will experience different reactions whether they become worse with the conditions they experience or the individual is able to adjust to the conditions they face and rise from adversity or become strong in the face of these traumatic events. Adjustments that can make individuals able to return to normal life or become better. Copart Amputation, Copart Prosthesis, Polycentric Ankle Joint. These operating methods and techniques have not changed and are still widely used today. Copart prosthesis is an artificial foot used for amputation of the ankle. the process of making copart prosthesis includes assessment, measurement, casting, fabrication, fitting, finishing. Of all the components for copart patients, prosthetic is the most important component that can have an effect on the stability of the road pattern. Options for panel opening type design made from polypropylene or most often GRF laminated (glas reinforeced plastic). the ancient type of postesis is made of sponges and skin.
KENDALA PENGGUNAAN IT SEBAGAI MEDIA BELAJAR DI SMP NEGERI 4 GELUMBANG

This research is a qualitative descriptive study that aims to provide an explanation of the constraints of using technology as a medium of learning in Gelumbang 4 Middle School. The informants in this study included teachers of Gelumbang 4 Middle School, headmaster or vice principal and several students through observation techniques, and interviews. The results of this study indicate that the constraints in the use of learning media technology in Gelumbang Public Middle School are as follows: (1) the lack of media available in school; (2) teacher limitations in using technology as a learning medium; (3) unavailability of time for making learning media; and (4) There are some teachers who do not understand the function of media learning.
Analysis and Design of HF Radio Noise Measurement Antennas considering Directivity and Polarization

The HF radio communication receivers are inevitably affected by radio noise everywhere. Quantitative evaluation of the effects is needed. This article discusses the sources and characteristics of HF radio noise, based on which measurement antennas are analyzed and designed considering directivity and polarization.
Energy Efficient Resource Management Techniques in Cloud Environment for Web Based Community by Machine Learning: A Survey

Cloud computing is a platform where the services of information technology are delivered by retrieval of resource from the internet through web applications and web tools instead of using direct server. In order to offer better quality of service to the larger number of web-based community users, many companies are adopting cloud computing. A dynamic number of web-based community user are interacting with each other via cloud computing. The number of online users is not just limited to manual counting figure so it is extremely important to manage each and every resource efficiently by minimising the energy consumption. To increase the performance of the system, the concepts of machine learning can be used to solve the challenges occur in cloud computing while managing resources for online communities. In this paper, we discuss the application of machine learning in cloud environment and its effect on web-based community.
A close-up comparison of the misclassification error distance and the adjusted Rand index for external clustering evaluation

The misclassification error distance and the adjusted Rand index are two of the most commonly used criteria to evaluate the performance of clustering algorithms. This paper provides an in-depth comparison of the two criteria, aimed to better understand exactly what they measure, their properties and their differences. Starting from their population origins, the investigation includes many data analysis examples and the study of particular cases in great detail. An exhaustive simulation study allows inspecting the criteria distributions and reveals some previous misconceptions.
Feedback Control based Spatial Filter Window Size Selection for Image Processing

Window-based spatial filtering is usually the first pre-processing operation performed, primarily for noise removal, in most image processing applications. In most filter designs, the window size (WS) is selected after performing a search that minimizes some criterion. Such a procedure is dependent on the choice of the initial WS as well as the search domain. In this work, an integral control based algorithm is proposed, where the WS is posed as the control variable that is designed so that the Power Spectral Density (PSD) of the unfiltered image-its output, tracks a reference PSD. The reference PSD is specified such that it consists of only the high energy components. It will be shown that the proposed algorithm is immune to the initial WS value, type of noise, and can be implemented for the design of several types of spatial filters.
A Reputation-based Carbon Emissions Trading Scheme Enabled by Block Chain

The carbon emissions trading scheme plays a significant role in promoting carbon emissions reduction. In this paper, the blockchain technology is applied to the carbon emission trading scheme, and the incentive mechanism of the reputation is added. A reputation-based carbon emission trading scheme (BCR-CETS) enabled by block chain is proposed. Compared with the traditional carbon emission trading scheme, it has the advantages of being safer and more efficient. In addition, case studies prove that BCR-CETS is more conducive to long-term carbon emission reduction.