Reviewerly/exHarmony huggingface dataset:
Development of Topic Modeling Framework Using Probabilistic Recurrent Neural Network

A topic model is a probability based model that finds the collection of documents.The basic concept is to treat the documents as combinations of topics in the topic model, and each topic is viewed as a probability distribution of the.In this paper we proposed LDA Algorithm using Probabilistic recurrent neural network (PRORNN) to classify the text documents.Topic modeling refers to the task of Discovering Latent Topics in the text corpus set, where the output is commonly represented as top terms appearing in each topic.This algorithm , probabilistic recurrent neural network (PRORNN) is implemented first with 2 News groups data set and later with 20 News groups dataset and all the results are tabulated.The performance of PRORNN algorithm was compared with the state of art of algorithms for topic classification.
Interpretable Text-to-Image Synthesis with Hierarchical Semantic Layout Generation

Generating images from natural language description has drawn a lot of attention in the research community for its practical usefulness and for understanding the method in which the model relates text with visual concepts by synthesizing them. Deep generative models have been successfully employed to address this task, which formulates the problem as a translation task from text to image. However, learning a direct mapping from text to image is challenging due to the complexity of the mapping and makes it difficult to understand the underlying generation process. To address these issues, we propose a novel hierarchical approach for text-to-image synthesis by inferring a semantic layout. Our algorithm decomposes the generation process into multiple steps. First, it constructs a semantic layout from the text using the layout generator and then converts the layout to an image with the image generator. The proposed layout generator progressively constructs a semantic layout in a coarse-to-fine manner by generating object bounding boxes and refining each box by estimating the object shapes inside the box. The image generator synthesizes an image conditioned on the inferred semantic layout, which provides a useful semantic structure of an image matching the text description. Conditioning the generation with the inferred semantic layout allows our model to generate semantically more meaningful images and provides interpretable representations to allow users to interactively control the generation process by modifying the layout. We demonstrate the capability of the proposed model on the challenging MS-COCO dataset and show that the model can substantially improve the image quality and interpretability of the output and semantic alignment to input text over existing approaches.
Cryptocurrencies for the Payment of Products or Services: Risks, Accounting Practices and Regulations

This paper examines the risks, accounting practices and disclosures of companies who accept cryptocurrency for the payment of products or services. We provide a brief history of cryptocurrency and blockchain technology that allows the reader to deepen their understanding of the subject before moving on to a discussion of how regulatory bodies such as the Financial Accounting Standards Board (FASB), the Securities and Exchange Commission (SEC) and the Internal Revenue Service (IRS) are treating the accounting for cryptocurrency transactions.
Designing and quality testing of “Digichip” virtual simulation software of Android platform for mobile-virtual learning supporting vocational mechatronics engineering

Technology enhancement in any life aspects is undeniably crucial to face the globalization era. A strategic approach to touch any life aspect is gained through education. Supporting this educational long-term goal, this study aims to: (1) produce learning media in the form of a virtual-learning simulation software, which is capable of operation on mobile devices to facilitate mobile, portable, effective, economical, and operationally-safe learning; (2) test the developed software quality in terms of the functional aspects of suitability, maintainability, portability, and usability (ISO/IEC 25010). The subjects of this Research and Development (R&amp;D) study were class XI students of Mechatronics Engineering at SMTI Yogyakarta. The process of designing and testing software uses the V-model type Software Development Life Cycle (SDLC). Unit testing was done through the white-box technique with the base-path test, flowgraph, and independent path. The testing for integration, system, and acceptance used Black-box techniques. The study shows that: (1) V-models were used to design the "DigiChip" virtual simulation software to support virtual learning and mobile learning through the software development stage; (2) The software quality testing based on ISO/IEC 25010 shows that in the functional aspects suitability, all features function properly (very decent), its maintainability is of MI 84 (easy maintenance), it gains 100% portability, or can be operated on all Android OS kernels (very feasible), and its usability is 86.18% (very feasible) with Cronbach's Alpha 0.841 (good). Ninety percent of media experts and 100% of material experts consider that the developed software is very feasible.
Data Placement Strategy in Data Center

Data centers have been prevalent resources for many years in data intensive applications. Typically, there are multiple servers in a data center to store data and perform computing. One of the last steps of provisioning a data center is to populate the servers with data.In this paper, we consider the scenario that data are transferred from external storage devices. We investigate data placement strategy with the consideration of two competing objectives for data transfer, reducing completion time and enhancing spatial locality. We propose a heuristic solution to jointly consider these two objectives and explore their tradeoff. Performance evaluation shows that with an adjustable balance ratio, a flexible data transfer performance can be achieved.
Utilization of social media platforms by librarians for promoting library resources and services in Nigerians’ tertiary institutions in Cross River State

The study was carried out to determine the utilization of social media platforms for promoting library resources and services in Nigerians' tertiary institutions in Cross River State, Nigeria.Descriptive survey research design method was employed for this study.The sample for this study comprised of 300 librarians in three Nigerian tertiary institutions in Cross River State.The questionnaire was the only instrument used for data collection.Frequency counts and mean were used to analyse the data collected.The study revealed that Facebook and Blogs are the most frequently used social media platforms for promoting library resources and services in the libraries.Findings also showed librarians utilize social media platforms in promoting two-way communication as the highest followed by making connections with library users easily, and provides forum for feedback and increases library users /usage and providing up-to date information in campus as the least.Some challenges librarians encounter was also discovered; hence recommendations were made.These include encouraging more participation on social media platform through seminars and symposiums amongst other recommendations.
Identifying Temporal Trends Based on Perplexity and Clustering: Are We Looking at Language Change?

In this work we propose a data-driven methodology for identifying temporal trends in a corpus of medieval charters.We have used perplexities derived from RNNs as a distance measure between documents and then, performed clustering on those distances.We argue that perplexities calculated by such language models are representative of temporal trends.The clusters produced using the K-Means algorithm give an insight of the differences in language in different time periods at least partly due to language change.We suggest that the temporal distribution of the individual clusters might provide a more nuanced picture of temporal trends compared to discrete bins, thus providing better results when used in a classification task.
Consistent Inverse Probability and Possibility Propagation

Given a probability distribution of an output quantity of a model, it is generally not possible to infer a unique probability distribution of the uncertain input quantity.In this contribution, it is shown that by reverting back to the coarser framework of possibility theory this problem possesses a conceptually straightforward solution with some powerful properties in the view of imprecise probability descriptions.
PENERAPAN METODE SAS (STRUKTURAL ANALITIK SINTETIK) UNTUK MENINGKATKAN KETERAMPILAN MEMBACA SISWA KELAS RENDAH DI SEKOLAH SASAR

Reading is an activity to get meaning or information from a reading. Therefore, reading is a very important thing to learn. But based on the observations of researchers at 016 Marsawa State Elementary School, researchers still found students whose reading abilities were lacking. The purpose of this study is to improve students' reading skills by applying synthetic analytic structural methods (SAS). This study uses classroom action research consisting of two cycles. each cycle consists of 1) planning, 3) implementation, 4) observation, and 5) reflection. The results showed, on the base score, 13 students completed with a percentage of 46.4% while students who did not complete as many as 15 with a percentage of 53.6% with the category of incomplete. In the first cycle, students who completed completed increased to 21 with a percentage of 75%, while students who did not complete as many as 7 people with a percentage of 25% with a complete category. In the second cycle, students who completed again increased to 25 with a percentage of 89.2%, while those who did not complete reduced to 3 people with a percentage of 10.8% complete category. Viewed from the results of students' reading skills; the basic score with an average of 60.07 then increased with an average number of 72.80 in cycle I. In cycle II an increase occurred with an average of 87.25. The increase in reading skills in the basic score to the first cycle was 12.73% while the increase in reading skills of students in the first cycle to the second cycle was 14.45%. From these results it can be concluded that the application of structural analytic synthetic (SAS) methods can improve the reading skills of low grade students in primary schools.
Performance Analysis of DNA Sequencing Using Smith-Waterman Algorithm on FPGA

The Smith-Waterman algorithm is the most accurate and optimal alignment algorithm for DNA sequencing. It is utilized to locate the ideal local alignment between two sequences. While looking sequence databases that may contain billions of sequences, this algorithm turns out to be computationally costly and tedious. Smith-Waterman algorithm needs additional memory space and this constrains the extent of a sequence to be aligned. In this study we will implement the S-W algorithm on a FPGA board which will speed up the performance execution of the program with less utilization and more efficiency. Keywords: Bioinformatics, sequence alignment, dynamic programming, Smith-Waterman algorithm, FPGA Cite this Article Anna Hakim, Anam Kashtwari, Rajinder Tiwari, Jamini Sharma. Performance Analysis of DNA Sequencing Using Smith-Waterman Algorithm on FPGA. Journal of VLSI Design Tools & Technology. 2019; 9(2): 9–13p.
Analysis and applications of the Locally Competitive Algorithm

Author(s): Paiton, Dylan M | Advisor(s): Olshausen, Bruno | Abstract: The Locally Competitive Algorithm (LCA) is a recurrent neural network for performing sparse coding and dictionary learning of natural signals. The network itself lacks much of the anatomical structure we see in real brains, but it does include lateral connectivity and iterative, or recurrent, computation. These together produce population nonlinearities that facilitate desirable coding properties, including improved robustness, selectivity, and efficiency when compared to more traditional architectures. When trained on images of natural scenes, the network also has many overlapping response properties with those of biological neurons. This has encouraged scientists to use it as a means to conceptualize theories of neural coding and explore their consequences. Probing the network enables us to observe and test theories that account for neurophysiological phenomena. In this thesis, we will provide motivation for investigating the model, a detailed derivation of the model, an analysis of its response properties, and offer some extensions. The core computational principles in the LCA are distinct from those used in most production artificial neural networks, and we will argue that there is much to be gained from incorporating LCA-like computations in exchange for feed-forward, pointwise nonlinear model neurons.
Lattice-Based Lightly-Supervised Acoustic Model Training

In the broadcast domain there is an abundance of related text data and partial transcriptions, such as closed captions and subtitles.This text data can be used for lightly supervised training, in which text matching the audio is selected using an existing speech recognition model.Current approaches to light supervision typically filter the data based on matching error rates between the transcriptions and biased decoding hypotheses.In contrast, semi-supervised training does not require matching text data, instead generating a hypothesis using a background language model.State-of-the-art semi-supervised training uses lattice-based supervision with the lattice-free MMI (LF-MMI) objective function.We propose a technique to combine inaccurate transcriptions with the lattices generated for semisupervised training, thus preserving uncertainty in the lattice where appropriate.We demonstrate that this combined approach reduces the expected error rates over the lattices, and reduces the word error rate (WER) on a broadcast task.
PENGEMBANGAN MEDIA PEMBELAJARAN BERBASIS MULTIMEDIA INTERAKTIF DENGAN PENDEKATAN SAINTIFIK PADA TEMA INDAHNYA KERAGAMAN DI NEGERIKU DI KELAS IV SD NEGERI 101969 TANJUNG PURBA

The purpose of the study was to determine the validity of the learning media, to find out the practicality of learning media developed on the theme of the beauty of diversity in my country. The type of research is development research (R &amp; D) using a 4-D model consisting of four stages, namely Define, Design, Develop, and Disseminate. The subjects in this study were grade IV students of Tanjung Purba State Elementary School 101969 which numbered 23 students. The data collection technique in this study was to use the results of assessments on aspects of media feasibility and material feasibility, the results of teacher responses to the practicality of learning media and the results of student responses after product trials. Based on the results of the study obtained the results of the feasibility of learning media by media experts by 92%, the results of material feasibility by material experts I obtained a percentage of 83%, the results of material feasibility by material experts II obtained a percentage of 86% and the results of assessment of teacher responses to the practicality 92%. From learning activities and data analysis research conducted by researchers, it can be concluded that the Development of Learning Media Based on Interactive Multimedia with a Scientific Approach in the Theme of Beautiful Diversity in My Country is valid and practical for use in the teaching-learning process and in accordance with the characteristics of students in class IV Tanjung Purba State Elementary School 101969. Keywords: Learning Media, Interactive Multimedia, Scientific Approach.
Twitter Sentiment Analysis in Under-Resourced Languages using Byte-Level Recurrent Neural Model

Sentiment analysis in non-English language can be more challenging than the English language because of the scarcity of publicly available resources to build the prediction model with high accuracy.To alleviate this under-resourced problem, this article introduces the leverage of byte-level recurrent neural model to generate text representation for twitter sentiment analysis in the Indonesian language.As the main part of the proposed model training is unsupervised and does not require much-labeled data, this approach can be scalable by using a huge amount of unlabeled data that is easily gathered on the Internet, without much dependencies on humangenerated resources.This paper also introduces an Indonesian dataset for general sentiment analysis.It consists of 10,806 twitter data (tweets) selected from a total of 454,559 gathered tweets which taken directly from twitter using twitter API.The 10,806 tweets are then classified into 3 categories, positive, negative, and neutral.This Indonesian dataset could help the development of Indonesian sentiment analysis especially general sentiment analysis and encouraged others to start publishing similar dataset in the future.
Survey on Deep Learning for Human Action Recognition

Human action recognition has gained popularity because of its worldwide applications such as video surveillance, video retrieval and human–computer interaction. This paper provides a comprehensive overview of notable advances made by deep neural networks in this field. Firstly, the basic conception of action recognition and its common applications were introduced. Secondly, action recognition was categorized as action classification and action detection according to its respective research goals. And various deep learning frameworks for recognition tasks were discussed in detail and the most challenging datasets and taxonomies were briefly reviewed. Finally, the limitations of the state-of-the-art and promising directions of the research were briefly outlined.
Assessment of power generation performance characteristics using different solar photovoltaic technologies

Abstract Solar energy is a relatively free renewable, clean, green, and environmentally friendly energy resource produced from the sun, using different technologies like solar thermal and photovoltaic (PV) modules to generate heat and electricity, respectively. This paper aims to assess and compare the power generation performance characteristics of different solar PV module technologies by simulation, deploying identical input temperature and irradiance parameters. The solar PV designs were simulated using PVsyst Version 6.73 for 26.0 kW annual power and 42.9 MWh/year annual yields using the climate data of Sydney, Australia. The results show that monocrystalline solar technologies needed an area of 163m2, while polycrystalline and thin films needed areas of 173m2 and 260m2 respectively to generate 42.9MWh annual yield. The monocrystalline PV modules are more efficient at solar energy conversion than polycrystalline and thin film technologies, respectively (94.2% and 62.7%). The symmetric semi-toroid dome of sun heights against azimuthal angles show 13 bimodal pyriforms, that cut the azimuths at least twice on each of the optimisation lines. These optimal solution points were visible for every sunlight hour of between 6 and 18 hours (inclusive). They show convex sets of global optima (or local optima) with one minimum in the interval of convexity. Also, each of the minimum points for each of the 13 directed fish-like bimodal pyriforms was on ±60° azimuth. However, the assessment of respective PV module characteristics with respect to variation of irradiation and temperature, show that the general results from simulation displayed disparate changes in the voltages and currents of the generated power with respect to different solar PV modules. The proposed semi-toroid model indicates that many optimisation solutions for easier, cheaper, quicker and more efficient power generation are possible with appropriate design. It is recommended that the installation of a single-axis solar tracker or maximum power point tracker could overcome the azimuth angle effect.
Attractiveness Analysis of Quiz Games

Quiz games are played on platforms such as television game shows, radio game shows, and recently, on mobile apps.In this study, HQ Trivia and SongPop 2 were chosen as the benchmark.Each game data have been collected for the analysis and the game refinement measure was employed for the assessment that focuses on different elimination tournament system for each sample.The results show that games such as HQ Trivia, which applies single-round elimination tournament, has a lower value of game refinement, in which the game is highly skillfull.Meanwhile, games that apply a round-robin system, such as SongPop 2 have a higher value of game refinement, in which the game is very stochastic.SongPop 2 and HQ Trivia both have more than 5 million downloads in Google Play Store.It is concluded that different types of quiz games which apply different kinds of tournament style have different game refinement value.
Study on Data Fields Grading Category Labeling for ERP Practical Skills Intelligent Assessment System

The paper mainly discusses about the labeling method for data fields grading category, which aims at "setting scoring standard" part in ERP practical skills intelligent assessment system. According to the different importance of data fields when scoring, the paper divided them into different categories and determined classification rules based on field attributes. Using the computer programming language, the paper realizes automatic labeling the grading category of data fields.
Arabic Lexicon Learning to Analyze Sentiment in Microblogs

The study and classifying of opinions distilled from social media is called sentiment analysis.The goal of this study is to build an adaptive sentiment lexicon for Arabic language.Based on those lexicons the sentiments polarity classification can be improved.The classification problem will be stated as a mathematical programming problem.In this problem, we search a lexicon that optimizes the classification accuracy.A genetic algorithm is presented to solve the optimization problem.A meta-level feature is generated based on the adaptive lexicons provided by the genetic algorithm.The algorithm performance is supported by using it alongside n-gram features and Bing liu's lexicon.In this work, lexicon-based and corpora-based approaches are integrated, and the lexicons are produced from the corpus.Five data sets are tested through experiments.The sentiments in all data sets are classified based on five polarity levels.A better understanding of words sentiment orientation, social media users' culture and Arabic language can be achieved based on the lexicons generated by the proposed algorithm.Since stop words can contribute and add to the sentiment polarity, stop words will be considered and will not deleted.The results show that the F-measure is greater than 80 % in three data sets and the accuracy is greater than 80 % for all data sets.The proposed method out-performs the current methods in the literature in two of the datasets.Finally, in terms of F-measure, the proposed methods achieved better results for three datasets.
Digital Virtual Simulation Experiment Design of Consumer Behavior in Smart Classroom Scenario

This work provides a specific experimental teaching paradigm for the establishment and application of the social context aware system under the smart classroom.The paper studies the physical context aware system based on the smart classroom, and generates the original big data by constructing the Consumer Behavior Cyber-physical system.And then, the dimensionality reduction of the data is processed to form a reliable data source for deep learning, and the TRIZ theory method is used to develop the virtual simulation experiment design of the specific consumption scene.The research has a positive exploration of the virtual simulation experiment of economic and management behaviors, and also verifies the practical application value of virtual simulation frontier technology in experimental teaching.