Reviewerly/exHarmony huggingface dataset:
VR Minecraft for Art

Art museums are becoming very boring to many people especially to the younger generation. The purpose of this project is to try to make a new type of art museum, one that is engaging and interactive. This project aims to answer the research question: Can a VR Minecraft museum enhance the user experience by giving them something that a typical art museum can’t? To answer this question, we’ve create a VR art museum within Minecraft and added features to make it more interactive and interesting. A more engaging environment is a great atmosphere to want to learn more. A scavenger hunt was added to the art museum to give the player a reason to walk around the entire museum. The player can build a sculpture after he/she completes the scavenger hunt which allows the player to be creative and imaginative. The user is then provided with different colored blocks to create a painting of his/her own. In order to answer the research question, we demoed the museum to a few people and then interviewed them. Their answers were very positive towards the VR Minecraft museum which leads me to believe that a VR Minecraft museum can indeed enhance the user experience.
Big Generalizations with Small Data: Exploring the Role of Training Samples in Learning Adjectives of Size

In this paper, we experiment with a recently proposed visual reasoning task dealing with quantities – modeling the multimodal, contextually-dependent meaning of size adjectives (‘big’, ‘small’) – and explore the impact of varying the training data on the learning behavior of a state-of-art system. In previous work, models have been shown to fail in generalizing to unseen adjective-noun combinations. Here, we investigate whether, and to what extent, seeing some of these cases during training helps a model understand the rule subtending the task, i.e., that being big implies being not small, and vice versa. We show that relatively few examples are enough to understand this relationship, and that developing a specific, mutually exclusive representation of size adjectives is beneficial to the task.
Loss-Free Excitonic Quantum Battery

Robust quantum energy storage devices are essential to realize powerful next-generation batteries. Herein, we provide a proof of concept for a loss-free excitonic quantum battery (EQB) by using an open quantum network model that exhibits exchange symmetries linked to its structural topology. By storing electronic excitation energy in a symmetry-protected dark state living in a decoherence-free subspace, one can protect the charged EQB from environment-induced energy losses, thereby making it a promising platform for long-term energy storage. To illustrate the key physical principles and potential functionality of this concept, we consider an open quantum network model of a para-benzene-like structure. We demonstrate through numerical simulations the immunity of the charged EQB to environmentally induced losses and further show how to harness the stored energy by adding a symmetry-breaking perturbation (SBP) to the network. We also investigate the impact of static disorder and temperature fluctuations of the SBP on the performance of the EQB during its storage and discharge phases. Apart from the cases with very strong static disorder, the performance of the EQB is essentially unaltered, thereby demonstrating the robustness of the proposed EQB.
An optimization approach to capacity evaluation and investment decision of hybrid cloud: a corporate customer’s perspective

Abstract While the rapid growth of cloud computing is driven by the surge of big data, the Internet of Things, and social media applications, an evaluation and investment decision for cloud computing has been challenging for corporate managers due to a lack of proper decision models. This paper attempts to identify critical variables for making a cloud capacity decision from a corporate customer’s perspective and develops a base mathematical model to aid in a hybrid cloud investment decision under probabilistic computing demands. The identification of the critical variables provides a means by which a corporate customer can effectively evaluate various cloud capacity investment opportunities. Critical variables included in this model are an actual computing demand, the amount of private cloud capacity purchased, the purchase cost of the private cloud capacity, the price of the public cloud, and the default downtime loss/penalty cost. Extending the base model developed, this paper also takes into consideration the interoperability cost incurred in cloud bursting to the public cloud and derives the optimal investment. The interoperable cloud systems require time and investment by the users and/or cloud providers and there exists a diminishing return on the investment. Hence, the relationship between the interoperable cloud investment and return on investment is also investigated.
An Error Resilience and Concealment Method for Line-based Wavelet Image Compressions

Recently, some wavelet-based line compression methods for low-cost high-resolution image transmission have been studied. With these line compression methods, which are a combination of the multilevel discrete wavelet transform, predictive coding, and entropy coding, communications errors on the transmission channel can cause an image quality–degradation phenomenon of various line shapes. In this paper, we analyze image degradation patterns of wavelet-based hybrid line compression techniques caused by communications errors. Then, we propose an image quality–improvement method including a low-cost resynchronization technique by using a periodic reset to prevent the error propagation phenomenon of predictive coding, as well as an error concealment technique that uses only one additional line memory in the variable reference line–based decoder to improve degraded image quality. Experimental results showed that the proposed method achieved better image quality than the conventional techniques at the small expense of compression ratio while satisfying low-cost design conditions pursued by existing line compression systems.
Semantic Prior Based Generative Adversarial Network for Video Super-Resolution

Semantic information is widely used in the deep learning literature to improve the performance of visual media processing. In this work, we propose a semantic prior based Generative Adversarial Network (GAN) model for video super-resolution. The model fully utilizes various texture styles from different semantic categories of video-frame patches, contributing to more accurate and efficient learning for the generator. Based on the GAN framework, we introduce the semantic prior by making use of the spatial feature transform during the learning process of the generator. The patch-wise semantic prior is extracted on the whole video frame by a semantic segmentation network. A hybrid loss function is designed to guide the learning performance. Experimental results show that our proposed model is advantageous in sharpening video frames, reducing noise and artifacts, and recovering realistic textures.
Research and Capstone Project Electronic Repository

Aims: Higher Education Institutions are challenged to manage research and capstone projects output available through open access is something that is increasingly mandated by funders and universities in many countries.&#x0D; Study Design: This is to widen the dissemination of results to the community which information technology practices and theory can address.&#x0D; Place and Duration of Study: This project tries to investigate the possible outcomes by developing an online repository for research and capstone project in Southern Philippines Agri-Business and Marine and Aquatic School of Technology that can deposit students output, register various accounts, log transactions and an interactive website.&#x0D; Methodology: Innovative research approach is being manifested in the development that uses modified waterfall, white-box testing and survey-type methodologies are being highlighted.&#x0D; Results: The website is successfully developed with specific functionality on referencing, data storage, data security, data extraction and some special functionalities. The fifty evaluators gave very agreeable results to the reliability, functionality and usability of the website.&#x0D; Conclusion: Although, the system is functional and evaluated very agreeable to the respondents the testing is very crucial that proper monitoring should be in place in the entire plan.
Improving Classification by Using MASI Algorithm for Resampling Imbalanced Dataset

At present, financial fraud detection is interested by many machine learning researchers.This is because of existing a big ratio between normal transactions and abnormal ones in data set.Therefore, a good result of prediction rate does not mean that there is a good detection result.This is explained that the experimental result might be effected by the imbalance in the dataset.Resampling a dataset before putting to classification process can be seen as the required task for researching in financial fraud detection area.An algorithm, so-called as MASI, is proposed in this paper in order to improve the classification results.This algorithm breaks the imbalance in the data set by re-labelling the major class samples (normal transactions) to the minor class ones basing the nearest neighbor's samples.This algorithm has been validated with UCI machine learning repository data domain.Then, the algorithm is also used with data domain, which is taken from a Vietnamese financial company.The results show the better in sensitivity, specificity, and G-mean values compared to other publication control methods (Random Oversampling, Random Under-sampling, SMOTE and Borderline SMOTE).The MASI also remains the training dataset whereas other methods do not.Moreover, the classifiers using MASI resampling training dataset have detected better number of abnormal transactions compared to the one using no resampling algorithm (normal training data).
Remuneration Information System Success In State University As Legal Entity (PTN-BH) Using Delone and Mclean Model

This study aimed to determine the success of remuneration information systems in PTN-BH using the DeLone and McLean models. The sampling method used was simple random sampling with 51 of 101 work units at the Indonesia University of Education. The data used were primary data with questionnaires, documentation, and interview techniques. Hypothesis testing was done by using version 3.0 Partial Least Square (PLS) software. The results of hypothesis testing show that the quality of the system influences user satisfaction, the quality of information affects user satisfaction and user satisfaction affects the personal impact.
Outlier Detection Credit Card Transactions Using Local Outlier Factor Algorithm (LOF)

Threats or fraud for credit card owners and banks as service providers have been harmed by the actions of perpetrators of credit card thieves. All transaction data are stored in the bank's database, but are limited in information and cannot be used as a knowledge. Knowledge built with credit card transaction data can be used as an early warning by the bank. The outlier analysis method is used to build the knowledge with a local outlier factor algorithm that has high accuracy, recall, and precision results and can be used in multivariate data. Testing uses a matrix sample and confusion method with attributes date, categories, numbers, and countries. The test results using 1803 transaction data from five customers, indicating that the average value accuracy of LOF algorithms (96%), higher than the average accuracy values of the INFLO and AFV algorithms (84% and 77%).
Development of Diagnosis Determination Model in Nursing Process based on Patient Centered Theory

Determination of nursing diagnosis based on the theory used, one of them is the theory of Carpenito, namely the patient response. The theory is not in line with the current situation. If the diagnosis is based on the patient's response, this theory cannot answer the patient's needs because patients who are hospitalized do not always respond or complain. The purpose of this study is to develop a model for determining the diagnosis in the nursing process based on the Patient-Centered Theory. The results of this study indicate that some nurses in establishing nursing diagnoses were still based on client responses so that they were not in line with medical diagnosis. Based on the findings, the determination of the Standard for Nursing Diagnosis must refer to the client's physiological disorders that are in accordance with the theory of Abdella Fayella. In Indonesia, in determining nursing diagnosis it is very important for nurses to carry out their practice in all spheres of nursing services, because nursing diagnoses are part of nursing care, so that the standard diagnostic is original Indonesian nurses are expected to improve standardized nursing services.
IDSH: An Improved Deep Supervised Hashing Method for Image Retrieval

Image retrieval has become more and more important because of the explosive growth of images on the Internet. Traditional image retrieval methods have limited image retrieval performance due to the poor image expression abh... | Find, read and cite all the research you need on Tech Science Press
Towards Automated Emotional Conversation Generation with Implicit and Explicit Affective Strategy

Building an empathic conversation machine in open-domain is a promising research topic in natural language processing. However, most current approaches rely on designated emotions to conduct generating responses and lack the ability to decide the appropriate emotion strategy. In this paper, we propose a dialogue model of jointly predicting and generating emotions called DRCVAE, which stands for Decoupled Representations of Conditional Variational Autoencoders.
Biometric Presentation Attack Detection Using Stimulated Pupillary Movements

Biometric systems can be subverted using presentation attack artefacts. This work presents a way to deal with the vulnerability to such spoofing attacks. In this work we propose the use of pupillary movements to detect such presentation attacks. The pupillary movements were stimulated by presentation of a moving visual challenge to ensure that some pupillary motion can be captured. Photo, 2D mask and 3D mask attack artefacts were evaluated based on data captured from 80 volunteers performing genuine attempts and spoofing attempts. The results indicate the effectiveness of the proposed pupillary movement feature to stop presentation attacks.
Graph Searches and Their End Vertices.

Graph search, the process of visiting vertices in a graph in a specific order, has demonstrated magical powers in many important algorithms. But a systematic study was only initiated by Corneil et al. a decade ago, and only by then we started to realize how little we understand it. Even the apparently naive question vertex can be the last visited by a graph search algorithm, known as the end vertex problem, turns out to be quite elusive. We give a full picture of all maximum cardinality searches on chordal graphs, which implies a polynomial-time algorithm for the end vertex problem of maximum cardinality search. It is complemented by a proof of NP-completeness of the same problem on weakly chordal graphs. We also show linear-time algorithms for deciding end vertices of breadth-first searches on interval graphs, and end vertices of lexicographic depth-first searches on chordal graphs. Finally, we present 2^n * n^O(1)-time algorithms for deciding the end vertices of breadth-first searches, depth-first searches, and maximum cardinality searches on general graphs.
Breaking the Secrets behind the Polyglots: How Do They Acquire Many Languages?

In this study, participants acquired three types of language, those are regional, national and foreign ones. The purpose of this research was to explore how Indonesian polyglots acquire several different languages. We collected data through demographic questionnaires and semi-structured interviews obtained from nine participants. Four salient themes and sub-themes that emerged in this research were (a) mastering languages through instructed learning (learning in formal educational institution and learning in informal educational institution), (b) gaining extra amounts of languages input beyond the classroom (getting more access to a national language environment and getting more access to foreign languages environment), (c) learning languages autonomously (self-directed learning through printed resources and self-directed learning through media and technology), and (d) acculturating with a new language group in society. The findings indicated that the polyglots acquired several different languages through a natural process, a learning process, and a mixture of both naturalistic as well as instructed experiences.
Benchmarking Deep and Non-deep Reinforcement Learning Algorithms for Discrete Environments

Given the plethora of Reinforcement Learning algorithms available in the literature, it can prove challenging to decide on the most appropriate one to use in order to solve a given Reinforcement Learning task. This work presents a benchmark study on the performance of several Reinforcement Learning algorithms for discrete learning environments. The study includes several deep as well as non-deep learning algorithms, with special focus on the Deep Q-Network algorithm and its variants. Neural Fitted Q-Iteration, the predecessor of Deep Q-Network as well as Vanilla Policy Gradient and a planner were also included in this assessment in order to provide a wider range of comparison between different approaches and paradigms. Three learning environments were used in order to carry out the tests, including a 2D maze and two OpenAI Gym environments, namely a custom-built Foraging/Tagging environment and the CartPole environment.
Peningkatan Keamanan Website Menggunakan Metode XML dengan Framework Codeigniter

Website security is needed in the dissemination of every information and data in cyberspace now. On the previous website the security used was not optimal in securing the website so we needed a reliable website security. XML (eXtensible Markup Language) is a markup language that is designed to store and deliver data by forming its own tags as needed. Codeigniter Framework is a php framework that is open source and uses the MVC method (Model, View, Controller). codeigniter is free or not paid if you use it. Codeigniter framework is made with the same purpose as other frameworks, namely to facilitate developers or programmers in building a web-based application without having to create it from scratch. The results of testing of this method are able to help and minimize the entry of hackers and secure data that will later become information. Using this method of combining can safeguard the entry of hackers and help secure the data and information on the website.
Rekomendasi Pemilihan Produk Simpanan Tabungan Bank Menggunakan Metode Weighted Product (WP)

Many banks in Indonesia, both public and private banks, offer different advantages to the customers. As a result, it is often difficult for the public to decide where to save their money. For this reason, the writer propose a decision support system to help recommend a bank as a smart solution for saving.&#x0D; This study employed the Weighted Product (WP) with seven criteria, namely initial deposit, subsequent deposits, administrative costs, depositional balances, interest rates, age range, and facilities. This system can help the prospective customers determine the ideal savings place for the future needs. This study shows that the highest final value after calculation is 0.0320 for Batara of BTN bank, and the lowest value is 0.0132 for Tapres of BCA bank.
Dynamic Searchable Encryption with Access Control

We present a searchable encryption scheme for dynamic document collections in a multi-user scenario. Our scheme features fine-grained access control to search results, as well as access control to operations such as adding documents to the document collection, or changing individual documents. The scheme features verifiability of search results. Our scheme also satisfies the forward privacy notion crucial for the security of dynamic searchable encryption schemes.
N-Gain Algorithm for Analysis of Basic Reading

The background of this research is to observe the effectivenes of the Basic Reading textbook as one of learning materials for reading subject.It is intended to discover the implementation of the Basic Reading textbook in one of the private universities in West Sumatera.The method of this research is descriptive research which quantitatively approaches.The second-year students of English Department at one of the private universities in West Sumatera were taken as the sample for this research.Data collection techniques were used tests; reading tests and observation; to identify students' soft skills.The researchers used one group pretest-posttest design which is calculated by using N-Gain Normalization.The data showed that there is significantly different between the result of the test before and after using the Basic Reading textbook.The result of N-Gain calculation shows 0.43 which is categorized as moderate which means the book improves students' cognitive learning outcomes in the reading subject.Thus, students also instill core ethical values in daily living.It can be concluded that the textbook used increased students' hard and soft skills.
Low-Power Pedestrian Detection System on FPGA

Pedestrian detection is one of the key problems in the emerging self-driving car industry. In addition, the Histogram of Gradients (HOG) algorithm proved to provide good accuracy for pedestrian detection. Many research works focused on accelerating HOG algorithm on FPGA (Field-Programmable Gate Array) due to its low-power and high-throughput characteristics. In this paper, we present an energy-efficient HOG-based implementation for pedestrian detection system on a low-cost FPGA system-on-chip platform. The hardware accelerator implements the HOG computation and the Support Vector Machine classifier, the rest of the algorithm is mapped to software in the embedded processor. The hardware runs at 50 Mhz (lower frequency than previous works), thus achieving the best pixels processed per clock and the lower power design.
SSSNet: Small-Scale-Aware Siamese Network for Gastric Cancer Detection

In recent years, deep neural networks have become the most powerful supervised learning method. Several advanced neural networks, such as AlexNet, ZFNet, Inception, ResNet, and DenseNet, have achieved excellent performance on image recognition tasks. However, deep neural networks rely heavily on huge training sets to obtain good performance. Many applications, such as medical image analysis, do not allow for such large training sets, and it is difficult to train such networks on small-scale training sets. Magnifying narrow band imaging (M-NBI) is widely used to assist doctors in diagnosing gastric cancer, but relatively few of these images are available, compared with the number of general images. In this paper, we propose to use a Siamese network architecture to learn discriminative feature representations based on pairs of images. Then, we use a micro neural network to recognize these features and classify the input images. Our experimental results show that the proposed network can effectively learn discriminative features from a limited number of training images, and also that it can successfully recognize gastric cancer in M-NBI images.