Reviewerly/exHarmony huggingface dataset:
APLIKASI PEMBELAJARAN BAHASA LATIN TUMBUH-TUMBUHAN BERBASIS ANDROID

Plants one of the living things that produce their own food, plants can convert sunlight into energy through photosynthesis. Based on research (Nia Kurniawati, 2015) in Indonesia there is a high diversity of flora and fauna, it is estimated that there are 100 to 150 families of diversity of plants and most of them potentially can be utilized. Latin is learned in the eyes of biological studies which are learned during junior high school. In the study of natural science, the names of plants, animals, as well as organs of the human body and animals that are given Latin. Middle school students usually learn Latin using guidebooks, there are no interactive and interesting applications suitable for junior high school students that can be easily understood by them, the teacher considers Latin material difficult for students to understand due to foreign languages ​​and are rarely heard. Android is one of the most widely used operating systems today, Android is free to use, modify, repair, and distribute by software developers, with the Android-based plant learning application for Android makes it easier for students to learn Latin, with voice features , pictures and descriptions make it easy for students to learn Latin for plants
Application of Extreme Learning Machine in Predicting Short- Term Wind Speed

At present, wind energy is the fastest growing power generation sector with its economic advantage of being a rich, clean and environmentally sustainable resources. However, wind does not generally blow consistently which prevents wind turbines from functioning at maximum capacity and capability. However, the solution that had been put forward in this paper to overcome the aforementioned problem is able to be used to make the deterministic predictions study for the wind speed, and of which its model in this paper possesses a significant prediction accuracy and strong stability, which could be useful in predicting the randomness of short term wind speed accurately. Based on the prediction output results, the amount of power required to be generated for load dispatch planning could be calculated and used to produce a scientific basis with the purpose of designing an optimal power grid dispatching design. In this paper, Extreme Learning Machine (ELM) is used for predicting short-term wind speed, and through use of the ELM the prediction accuracy of wind speed was observed to be at 0.93 followed by the root mean square error rate at 1.9. With reference to the prediction results, the developed model is tested to be able to predict wind speed accurately.
Coarse-to-Fine Attention Network via Opinion Approximate Representation for Aspect-Level Sentiment Classification

Aspect-level sentiment classification aims to determine the sentiment polarity of the aspect that occurs in the sentence. Some prior methods regard the aspect as the attention goal and learn the association between aspect and context directly. Although improved results are achieved, the pure representation of aspect can not effectively reflect the differences of multiple sentences and will limit the improvement of sentiment classification. To address this issue, we propose a coarse-to-fine attention network via opinion approximate representation, which first extracts the neighboring words of aspect to approximate the opinion representation that contains the descriptive information of the aspect. Moreover, we design the coarse-to-fine attention module to complete the interaction of useful information from the sentence and word level, the former learns the rough representation of the context, while the latter learns the precise representation of opinion. The experimental results on three public datasets show our model achieves the state-of-the-art performance.
Performance Quantification of Search Operators in Hybrid Harmony Search Algorithms

Meta-heuristic algorithms have been developed to solve various mathematical and engineering optimization problems. However, meta-heuristic algorithms show different performances depending on the characteristics of each problem. Therefore, there have been many kinds of research to decrease the performance gap for the different optimization problems by developing new algorithms, improving the search operators, and considering self-adaptive parameters setting on their algorithms. However, the previous studies only focused on improving the performance of each problem category (e.g., mathematical problem, engineering problem) without the quantitative evaluation for the operator performance. Therefore, this study proposes a framework for the quantitative evaluation to solve the no free lunch problem using the operators of the representative meta-heuristic algorithms (such as genetic algorithm and harmony search algorithm). Moreover, based on the quantitative analysis results for each operator, there are several types of hybrid optimization algorithms, which combined the operator of harmony search algorithm (HSA), genetic algorithm (GA), and particle swarm optimization (PSO). The optimization process to find the optimal solution is divided into five sections based on the number of function evaluations to see the performance of the search operator according to the section. Representative mathematical problems were applied to quantify the performance and operators. None of the five evaluated applied to mathematical benchmark problems were the best algorithms. Hybrid HSAs showed advanced performance for problems where traditional HSA did not show good performance. However, it still has not escaped the No Free Lunch theorem.
Work in Progress: Integrating the Entrepreneurial Mindset into a Software Requirements Course

One of the most challenging aspects of software engineering is teaching students requirements elicitation.Software requirements elicitation requires complex thinking and a thorough understanding of the customer and business needs.Traditionally, requirements elicitation courses have focused on pure documentation of requirements, the focus being on drafting unambiguous statements properly formatted to follow an IEEE standard.However, the challenge of requirements elicitation is often not in the documentation of requirements, but rather in understanding the needs of a customer.This work in progress paper intended to provide a case study of a novel approach to integrating the entrepreneurial mindset into a software requirements course.Working in teams, students are given an extensive scenario related to a real-world medical issue introduced by a brief video.Through the remainder of the course, students interview other students, real world practitioners, and others to understand the value of the product and the needs of potential clients before drafting a final requirements document which then could be used to develop the project.Through this approach, engineers communicate with nurses, athletes, pharmacists, and other non-engineers, learning the skills of teamwork, the perspectives of non-engineers, the limitations of technology, and in some cases, learn that a project that seems advantageous may actually not be successful.The paper will describe the project, the materials created for the project, and provide student observations on the success of this approach.
Enhancing Decision Making with Deep Reinforcement Learning in a Context of Novel Coronavirus Outbreak

Physicians in hospitals are expected to improve treatment outcome and reduce health care costs. Information systems have been widely adopted in hospitals but not been properly integrated to provide information for decision support. The objective of this research is trying to validate the feasibility of enhancing hospital resource planning system in decision support by utilizing data stored in multiple systems in the hospital with a deep reinforcement learning approach to assist medical practitioner making a more accurate and efficient decision. Following the Design Science Research Method, this research is going to build an artefact to utilize data from electronic health record (EHR) and hospital resource planning (HRP) to provide medical decision support in the emergency department setting.
Utilising mobile game based learning methods effectively to support education

This paper is a response concerning the implications of scholarship of the article entitled "A critical review of 13 years of mobile game-based learning" by Giannakas et al. (in Educ Technol Res Dev 66:341-384, 2018). This response focuses on the key ideas presented in Giannakas et al. (2018) from a practical perspective, highlighting how practitioners, scholars and designers can use their findings to effectively utilise mobile Game Based Learning (mBGL) approaches to support and enhance learning. This response paper is discussing the value, impact and practical implications of Giannakas, et al. (2018) and possible future directions relevant to the use of smartphone enabled virtual reality to support mGBL.
Strong Stimulation with Virtual Reality Treatment for Acrophobia and Its Evaluation

This manuscript discusses an acrophobia treatment procedure using virtual reality of VR technology and conducts a pilot experiment on feasibility. The core of our treatment produce is a exposure therapy with a strong shock. In our treatment produce, patients wear a head-mounted display or HMD and they are left on the top of a mountain in a virtual space. They are given five minutes to find a way down the mountain and only a way down is to jump off. There are some artifacts for them to do that by themselves. A strong stimulation is given to the patients at the moment of jumping. The experiment results show that our treatment procedure demonstrates the potential for alleviating acrophobia and a stronger stimulation is acceptable for alleviating acrophobia.
DEVELOPMENT OF A DATABASE FOR HOLDING SCIENTIFIC EVENTS OF THE UNIVERSITY

The article discusses the implementation of the server side of the information platform for organizing scientific and methodological events, which includes the creation of a database of events and a system for registering participants for methodological events. The implementation of the server part will help, in this case, automate the system of collecting data about participants for the organizers, as well as allow participants to quickly monitor the status of their submitted applications and articles. The server part of the development of an information platform for organizing scientific and methodological events belongs to the direction of backend programming and includes the study of the scripting language PHP, the free relational database management system MySQL, the content management system (CMS) Drupal, as well as the technologies for interaction between the listed systems and programming languages.
Construction of Modern Information Education Platform Based on Embedded Resource Scheduling

In order to improve the ability of modern information education, this paper puts forward the construction model of modern information education platform based on embedded resource scheduling. The overall design description and functional modularization analysis of modern information education platform are carried out. The bus automatic monitoring design of bus control program control method of modern information education platform is carried out, and the underlying database is established. The system design includes modern information education resource retrieval module, modern information education digital information resource integration processing module, bus control module, resource information fusion module, output interface control module, etc., Big data mining of modern information education is carried out, the identification model of digital information fusion of modern information education is constructed, and the software development and design of modern information education platform are realized by combining the digital integration algorithm and resource scheduling design of modern information education, and the integrated design of modern information education platform is realized under MVB bus control protocol and embedded environment. The simulation results show that the designed modern information education platform has good intelligence and strong control ability of information output, which improves the level of modern information education and management.
Before You Start, Know the Industry

In this chapter, we’ll look at the landscape of apps for kids. We’ll try to keep a broad view to get a grip on the general basic knowledge you should have before even starting to ideate a product.
Machine Learning Based Bug Prediction Engine For Smart Contracts

As blockchain solutions become widespread, identifying potential bugs in smart contracts written in Solidity language will be important for these solutions to work correctly. To accurately detect these bugs, the developer must use several state-of-the-art bug detection tools and investigate the potential bugs they report. In this study, we first show that one tool is not enough to detect all the bugs as our Static Analysis for Solidity tool (SA-Solidity) and the known SmartCheck and Securify tools identify different bugs in SmartEmbed's experimental set of smart contracts. Then, we develop Machine Learning-based Bug Predictor for Solidity (MLBP-Solidity) which predicts files that would be reported by all the previous bug detection tools. MLBP-Solidity eases the burden on the developer by allowing him/her to focus on a subset of files that are most probably buggy. Our experimental results show that MLBP-Solidity achieves 91-99% accuracy, depending on the type of predicted bug.
Distant Supervision Relation Extraction Model Based on Feature-recalibration Networks

Relation extraction (RE) is a crucial ingredient in numerous natural language processing tasks for mining structured facts from heterogeneous texts. This paper presents a novel neural network architecture called Feature- recalibration (FR) Networks, that adaptively recalibrates channel-wise feature responses by explicitly modelling interdependencies between channels. This architecture enables end to end learning from task-specific labeled data, forgoing the need for external knowledge such as explicit dependency structures and entity description information. Experimental results on the New York Times dataset demonstrate the effectiveness of our proposed model.
Quantitative imaging of the resonator shape in the bundengan musical instrument

The bundengan is an endangered musical instrument from Indonesia. To support its conservation, a non-contact method is needed to characterize the shape of its resonator. Here we report a low-cost and portable setup, which uses only one camera and one grid ruler, that allows for quantitative imaging of the resonator's shape directly in the remote rural areas where the musical instrument can be found. To perform the measurement, the bundengan needs to be positioned such that the camera can see the whole grid of its woven bamboo lattice. Two digital images are then taken from two different positions. Using a mathematical model of the setup, we determine the three-dimensional positions of all the points in the bamboo grid. These points are then used to estimate the resonator's shape. With this setup, we have been able to characterize the resonator's shape from one bundengan.
A technique of deviant behaviour detection in video surveillance systems based on complex behaviour analysis

This paper deals with deviant behavior detection using video surveillance systems. Deviant behaviour detection is a complex problem and includes not only the recognition of the abnormal patterns of individual's behaviour, but also the problem of general methodology development, which consists of deviant behaviour markers and techniques to identify them. A new complex approach to the object behaviour analysis is presented in this paper as well as the integrated distributed classifier generic architecture is described. The novelty of this research is the consequent subject analysis based on a complex of potentially deviant and socially dangerous behaviour attributes.
Research on Coordinate Conversion and Error Analysis of BLSS-PE

Abstract In this paper, a 3D laser scanner for mining named BLSS-PE is proposed, based on a commercial rangefinder with two servo motors. The coordinate transformation model in different coordinate systems is put forward and the model solving strategy is given. By establishing the error model of BLSS-PE and comparing the measurement data, the results show that the accuracy of BLSS-PE data can be improved after the external factors are included in the system error model, which verifies the feasibility and correctness of the optimization model.
Fashion image analysis using single-stage detector

Clothing detection and landmark detection are important techniques in fashion image analysis. The availability of large annotated fashion datasets has made fashion image analysis a hot research topic. This paper proposes a single-stage detector that performs bounding box detection, fashion landmark detection, and can also predict end-to-end clothing category classification. This parallel processing provides improved time efficiency than the later technique that performs regional proposals first and then prediction module. The proposed network is designed with the revision of the EfficientDet model announced by Google Brain. The proposed approach can also be used within a real application because it can operate efficiently and quickly from the inference latency perspective.
Action-Centered Information Retrieval

Abstract Information retrieval (IR) aims at retrieving documents that are most relevant to a query provided by a user. Traditional techniques rely mostly on syntactic methods. In some cases, however, links at a deeper semantic level must be considered. In this paper, we explore a type of IR task in which documents describe sequences of events, and queries are about the state of the world after such events. In this context, successfully matching documents and query requires considering the events’ possibly implicit uncertain effects and side effects. We begin by analyzing the problem, then propose an action language-based formalization, and finally automate the corresponding IR task using answer set programming.
Photonic Long-Short Term Memory Neural Networks with Analog Memory

A Photonic implementation is proposed for the Long-Short Term Memory neural network, offering fundamental speed and bandwidth advantages over digital electronic implementations. Integrated analog memory for photonics is designed as a component of this network.
SJSU Go

The Davidson College of Engineering at San Jose State University (SJSU), San Jose, California, offers a rewards program for students to get points for attending professional and academic events called GO. Currently students submit events for the program through a Google form with many limitations. In this project we have developed mobile applications (for both iOS and Android systems) and a website to submit events, to view points and prizes, and to navigate the system. The goal is to increase student participation in these events and ease of use of the system.
OpenCLTM Implementation of Fast Frequency Domain Image Denoisification Kernels for Parallel Environments

From the last couple of decades, image denoisification is one of the challenging areas in the image processing and computer vision domains that adds clarity to images by removing noise and makes them suitable for further processing. Images or videos captured by various visual sensors are degenerated during various circumstances such as sensor temperature, light intensity, target environment, erroneous electric instruments, and interference in the communication channels and required to be enhanced by various filtering techniques. Most of the real-world applications like student monitoring, vigilance system, traffic analysis, target detection, and classification use image smoothing to reduce artifacts present in the captured images and additionally demand its quicker execution. In this paper, we have explained different types of noise models normally degrade the quality of an image and the three most popular frequency domain smoothing filters (ideal, Gaussian, and Butterworth) are available to remove those noises. In addition to that, OpenCL kernels of those filters have been implemented for rapid denoisification and can be easily portable on wide verities of parallel devices. At last, performance evaluation has been carried out for both the CPU and GPU implementations to seek out their productiveness on those noise models in terms of time and precision. Various performance assessment metrics like entropy, root-mean-squared error, peak signal-to-noise ratio, and mean absolute error are applied to determine the rightness of the above filters.
Learning Discrete Sentence Representations via Construction &amp; Decomposition

In this paper, we address the problem of learning low-dimensional, discrete representations of real-valued vectors. We propose a new algorithm called similarity matrix construction and decomposition (C&D). In the preparation phase, we constructively generate a set of consistent, unbiased and comprehensive anchor vectors, and obtain their low-dimensional forms with PCA. The C&D algorithm learns the discrete representations of vectors in batches. For a batch of input vectors, we first construct a similarity matrix between them and the anchor vectors, and then learn their discrete representations from the similarity matrix decomposition, where the low-dimensional forms of the anchor vectors are regarded as a fixed factor of the similarity matrix. The matrix decomposition is a mixed-integer optimization problem. We obtain the optimal solution for each bit with mathematical derivation, and then use the discrete coordinate descent method to solve it. The C&D algorithm does not learn directly discrete representations from the input vectors, which distinguishes it from other discrete learning algorithms. We evaluate the C&D algorithm on sentence embedding compression tasks. Extensively experimental results reveal the C&D algorithm outperforms the latest 4 methods and reaches state-of-the-art. Detailed analysis and ablation study further validate the rationality of the C&D algorithm.
Adaptive Average Exploration in Multi-Agent Reinforcement Learning

The objective of this research project was to improve Multi-Agent Reinforcement Learning performance in the StarCraft II environment with respect to faster training times, greater stability, and higher win ratios by 1) creating an adaptive action selector we call Adaptive Average Exploration, 2) using experiences previously learned by a neural network via Transfer Learning, and 3) updating the network simultaneously with its random action selector epsilon. We describe how agents interact with the StarCraft II environment and the QMIX algorithm used to test our approaches. We compare our AAE action selection approach with the default epsilon greedy method used by QMIX. These approaches are used to train Transfer Learning (TL) agents under a variety of test cases. We evaluate our TL agents using a predefined set of metrics. Finally, we demonstrate the effects of updating the neural networks and epsilon together more frequently on network performance.