Reviewerly/exHarmony huggingface dataset:
Generating Permutations Using Hash Tables

Given a set of <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$N$</tex> distinct values, the operation of shuffling those elements and creating a random order is used in a wide range range of applications, including (but not limited to) statistical analysis, machine learning, games, and bootstrapping. The operation of shuffling elements is equivalent to generating a random permutation and applying the permutation. For example, the random permutation of an input allows splitting into two or more subsets without bias. This operation is repeated for machine learning applications when both a train and test data set are needed. In this paper we describe a new method for creating random permutations that is scalable, efficient, and simple. We show that the operation of generating a random permutation shares traits with building a hashtable. Our method uses a fairly new hash table, called HashGraph, to generate the permutation. HashGraph's unique data-structure ensures easy generation and retrieval of the permutation. HashGraph is one of the fastest known hash-tables for the GPU and also outperforms many leading CPU hash-tables. We show the performance of our new permutation generation scheme using both Python and CUDA versions of HashGraph. Our CUDA implementation is roughly 10% faster than our Python implementation. In contrast to the shuffle operation in NVIDIA's Thrust and cuPy frameworks, our new permutation generation algorithm is 2.6 x and 1.73 x faster, respectively and up to 150 x faster than numPy.
Nonparametric Maximum Likelihood Probabilistic Photovoltaic Power Generation Forecasting based on Spatial-Temporal Deep Learning

One major challenge in the development of solar photovoltaic (PV) systems is their inherent intermittency due to high dependability on meteorological and weather conditions. To address this challenge, providing full statistics information for the look-ahead times can be a potential solution. This paper establishes a two-stage probabilistic framework to predict full statistics information for PV power. In the first stage, a combined deep neural network is designed to capture full spatial features by a convolutional neural network (CNN), while temporal features are realized using a gated recurrent unit (GRU). In the second stage, a probability density estimation (PDF) is developed using nonparametric smooth band limit maximum likelihood (NSBML) PDF estimator to extract full information about the predicted PV power generation. The numerical results on a PV power generation dataset with a 5-min time resolution demonstrate the effectiveness and superiority of the proposed framework by comparing with several state-of-the-art models and one PDF estimator (kernel density estimator (KDE)).
Development of an Electric Solar Array Superblock in Open Source Software SCILAB/Xcos

The article presents an electric superblock of a solar array implemented in the Xcos extension of the Scilab computing system. The article shows ways of elimination of errors that have arisen during adaptation of the well-known block model of the solar array implemented in the system MATLAB/Simulink/SimPowerSystems for operation in the system Scilab/Xcos. It was found, that the deviation of short circuit, maximum power and idle points on the I-V and P-V curves characteristics obtained with the Xcos model and SimPowerSystems model was less than 1%. When a direct current (DC) motor is connected to a solar array the results of dynamic Mode simulation are presented. A program that has been proposed in this article builds better quality graphics in the Scilab system than in the Xcos system. In addition, this program prepares data for the construction of the charts in Excel and determines the point of maximum power of solar array.
Accreditation Documents Profiling Based On The Used of English Terms Using Text mining Algorithm : A case study on 24 accreditation document

Until recently, study program accreditation is still the standard for evaluating the feasibility and quality of the education process worldwide. Normally, in the process of accreditation, one specific study program needs to prepare a report document that represents the whole process of education, especially from the standpoint of quality, and then submit it to the National Board of Accreditation. Self-assessment and evaluation are the keys to creating a proper accreditation document. In the case of Indonesia, the accreditation process is run by the National Board of Higher Education Accreditation (called BAN-PT). The study program must prepare and submit a document that represents the overall process of education by following the national standard and format determined by BAN-PT. In this study, we hypothesized that the text mining technique can be used to evaluate the accreditation document by analyzing the English terms that are used in the document. The analysis is done by scoring the number of English terms in one accreditation document and selecting the proper terms that are used. We hypothesize that the higher the score is obtained by the document the higher the national rank of accreditation was obtained by the document. In this study, 24 accreditation documents with approximately every document consisting of 200 pages within similar study programs were analyzed. A text processing technique was implemented to filter and clean the documents from unimportant words. English phrases were detected from each sentence in the document and then continued by extracting English terms further filtering to get the English terms is then performed. The selected English terms are then scored from each accreditation document. The validation process is done by mapping the scoring result with the Accreditation Rank obtained by the document. The result showed that the higher the score based on the English terms obtained by one document. The higher the Nasional Accreditation ranks too. However, two out of 24 documents did not show a similar pattern.
Research on stealthy UAV path planning based on improved genetic algorithm

An improved adaptive genetic algorithm is proposed for the problem of how to plan the assault path of stealth UAVs in a complex battlefield environment. Combined with radar network, terrain obstruction and other threats to simulate the real combat environment, the stealth UAV’s assault path is planned on a three-dimensional map, and in order to improve the real-time and flexibility of the path, the path needs to be re-planned with the addition of emergent obstacles. The improved adaptive genetic algorithm solves the disadvantages of the traditional genetic algorithm such as slow convergence speed and easy to fall into local extremes. The final simulation results show that the improved adaptive genetic algorithm not only converges faster than the standard genetic algorithm and adaptive genetic algorithm, but also the solution results are closer to the optimal solution and the planned paths are more reasonable.
Echo State Network for Turbulence-Induced Fading Channel Prediction in Free-Space Optical Systems

Free-space optical (FSO) communication has established its reputation for delivering ultra-high data-rate services. Over the time-varying turbulence channels, the channel state information (CSI) tends to be outdated due to the feedback delay. This may lead to considerable performance deterioration in FSO systems. This paper studies a prediction scheme for accurately updating CSI over FSO turbulence-induced fading channels. We employ the echo state network (ESN) model, a form of recurrent neural network (RNN) with low computational complexity yet high efficiency, for turbulence channel prediction in FSO systems. We verify the prediction model using measured FSO channel data. Simulation results indicate that the ESN model has excellent prediction performance in FSO systems. Furthermore, we also highlight the effectiveness of the ESN model by comparing it with conventional CSI prediction models in terms of complexity and accuracy performance.
Biomorphic artificial intelligence system for pattern recognition problems with adaptive error correction

In this study, we have developed a biomorphic artificial intelligence system for pattern recognition tasks with adaptive error correction. The system imitates the process of hierarchical information processing inherent in living systems, for example, information processing in the visual cortex of the brain. Also, one of the properties that the developed system has is the correction of errors accumulated in the process of interaction with the outside world.
Automated Suggestions Framework for Processing Hardware Specifications Written in English

Automatic creation of formal models from natural language specifications can help reduce design time and manual errors. However, the accuracy of the translation may not be high due to the ambiguous, incomplete, and inconsistent nature of natural language. Specifications written in a controlled natural language (CNL) can overcome the problems associated with natural language translation and deliver similar benefits in automation. However, a user has to learn to write in a CNL. We propose a suggestions framework that provides automatic feedback to assist users in writing specifications in CNL. Our feedback generates different ways of writing CNL acceptable sentences when the input sentence is not understood. We developed a ranking scheme to ensure the semantics of generated suggestions are closer to the input specification’s intent. We evaluated the framework on 132 erroneous specifications taken from AMBA and memory controller architectures documents. Our system generated suggestions for all the specs. On manual inspection, we found that 87% of these suggestions were semantically closer to the intent of the input specification.
The Implementation of Rilpot Sensor Integration In the Real Blindspot Application as a Media for Millenial Generation Driving Safety Learning In the 4.0 Era

The high rate of traffic accidents in Indonesia in 2020 was reported by Media Indonesia on September 30, 2020, which stated that traffic accidents (Lakalantas) increased by 1.28 percent in the 39th week compared to the previous week, with one of the factors contributing to traffic accidents being blind spots on vehicles, particularly container trucks passing through the port hinterland area. One of them is in Jakarta's northwestern outskirts. As a result, an application that has the potential to minimize the rate of accidents is required. These accidents frequently occur as a result of the driver's inability to detect and know the existence of other vehicles in the vehicle's blind areas. The ADDIE approach was used to conduct the study. This application will provide a warning sign of a vehicle passing on the vehicle's blind spot, also known as a blind spot, which comes from sensors installed on the vehicle's blind spot. It will also provide additional features such as blind spot understanding, blind spot simulators, blind spot detectors, equipment driving, driving procedures, and ethics. Regarding driving regulations, this feature will educate Indonesians, particularly the millennial age, on how to be more self-conscious and aware of the dangers of blind spots while driving, potentially lowering the likelihood of road accidents and assisting in the realization of several crucial aspects. Quality education is one of the Sustainable Development Goals (SDGs).
Anthropometric Measurements with 2D Images

In this work, we designed and provided a proof-of-concept study for a novel system that takes several anthro-pometric measurements (bust, waist, and hip circumferences) simultaneously using only two (frontal and side) 2D images of a human subject. The system has two components: a specific camera setup with lasers and image analysis software. Towards this purpose, we compare body measurements of the proposed system and manual measurements on a limited number of subjects. For automatic measurements, we took one frontal and one side image from each subject. Body segmentation and pose estimation are applied to these images using pre-trained deep neural networks. Using the laser positions on the image, pixel sizes are estimated in terms of physical length (ie. centimeter). Using physical widths of the bust, waist, and hip in the images, their circumferences are estimated automatically. On three subjects, we obtained less than 10% measurement error. We concluded that anthropometric measurements could be obtained using a camera and laser setup. However, the number of subjects should be increased with a more precise laser to determine a better margin for measurement error.
A Clustering-based Approach for Topic Modeling via Word Network Analysis

This paper presents a clustering-based approach to topic modeling via analyzing word networks based on the adaptation of a community detection algorithm. Word networks are constructed with different word representations, and two types of topic assignments are introduced. Topic coherence score and the document clustering results are reported for topic model evaluation. Experimental results showed that it achieved comparable results with the current best. It also showed that the proposed approach produced a higher performance as the number of most relevant words gets larger in <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$C_{cv}$</tex> coherence score.
Robotic Grasping in Simulation Using Deep Reinforcement Learning

In robotics, manipulators are recently becoming one of the prominent fields of interest for different types of applications. One of the usual functionalities performed by manipulators is grasping. Grasping means simply holding an object. In order to perform a grasping task, each manipulator needs a gripper mounted at the end effector of them. In this paper, a method based on deep reinforcement learning is presented to deal with the issue of robotic grasping employing only vision feedback. The combination of deep learning with dueling architecture, a variant of Q-learning, brings the complexity caused by the use of handcrafted features to a humbler state. Our method employs the Dueling Deep Q-learning Network(DDQN) to learn the grasping policy. Our proposed system employs a visual structure that uses a Kinect camera setup that spots the scene that possesses the object of interest. We realized our experiments by utilizing Webots simulator environment. The results show that our proposed dueling architecture enables our Reinforcement Learning(RL) agent to perform well enough to fulfill the grasping task.
One-Class Classification of 3D Point Clouds Using Dynamic Graph CNN

Anomaly detection is defined as a binary classification of normal and abnormal samples in a given data. In this task, anomalous samples are considered very rare and thus they are not used in training the deep learning model. Anomaly detection is thoroughly studied in 2D images, but 3D applications are not common yet. In this work, we combine studies in the field of 3D point cloud and anomaly detection and present a one-class classification method for 3D data. This method uses a pretrained Dynamic Graph Convolutional Neural Network (DG-CNN) to extract features from 3D point cloud data, and then fits a separate multivariate Gaussian distribution for each class. In the test phase, features are extracted from the test samples, and the distances to the distribution obtained in training indicate how anomalous the sample is for that class. Our method achieves 94.2% AUROC accuracy for the given setup. This is the first attempt to handle 3D point cloud classification as an anomaly detection case, and thus our findings and methods serve as a baseline for further studies.
Adaptive Slot-Filling for Turkish Natural Language Understanding

Slot-filling is a key part of natural language under-standing that aims to extract words which hold certain attributes for the dialogue system. Although slot-filling is traditionally considered to be a data demanding and expensive task, advances in transformer models can help to solve this problem via transfer learning. In this paper, we propose an adaptive transfer-learning based slot filling model using BERT and conditional random fields (CRFs). We also introduce and discuss the stemming problem for agglutinative languages in slot-filling, which we define as the ambiguity of meaning between extracting the whole word or extracting a part of the word for the slot. We propose a novel definition of stemming specifically for wordpiece tokenizers used in transformer models and use it to solve the stemming issue. Our experiments with the BERT-CRF model out perform previous models on Turkish slot filling. We also show that under the new definition, wordpiece tokenizers perform on par with current state-of-the-art stemming models. Finally, we contend transformer based models like ours can overcome the stemming issue with the help of labelling.
Brain Computer Interaction and Cybersecurity of the Brain

It is the brain that makes human knowing and supreme being among other living creature. With the mental and cognitive abilities formed as a result of education, training and experience, the brain is accepted as a machine that processes information or a natural computer. Ensuring the security of cyber space created by information communication systems and networks is getting harder day by day. While providing cyber security, necessary precautions should also be taken for the security of data and information in the brains of those who create and use cyberspace.
Hybrid CPU-GPU Acceleration of a Multithreaded Image Stitching Algorithm

Real-time image stitching is critical, especially in un-manned aerial vehicles, and its acceleration has received attention in recent years. This paper describes an image stitching acceleration scheme for heterogeneous (CPU+GPU) devices. Acceleration is attempted with both multithreading and multiprocessing. Most time-critical functions in the algorithm are offloaded on to the GPU. We crafted a 3-buffer ping-pong mechanism for synchro-nization and data transfer among threads/processes in order to maximize CPU utilization. We carried out our experiments on Nvidia Jetson AGX Xavier. Results show that more than 3x acceleration is achieved.
Virtual Prototype based Analysis of Neural Network Cache Behavior for Tiny Edge Device

The demand for AI and specifically machine learning functionality on edge devices (TinyML) is growing. TinyML faces several unique challenges, one of them being the requirement of having a lower memory footprint for storage and inference of neural networks.In this paper, we propose and evaluate an approach to make Convolutional Neural Networks (CNNs) with higher memory footprint executable on edge devices. The idea is to combine flash memory with a cached access for the inference of CNNs. In order to evaluate the effectiveness of our proposed memory architecture by measuring the cache hitrate at the system level, we build a Virtual Prototype (VP) with a dedicated flash device and an exclusive cache. We are using Tensorflow Lite Micro (TFLM) for the network inference and mapping all model data and runtime buffers into the cache enhanced flash memory. Multiple experiments with several cache configurations show that a small cache of around 1 KB is able to achieve very high hitrates over 99%. Additionally, the experimental results show that the memory planning of TFLM supports the usage of caching because most memory accesses are adjacent.
Mixed-Method Quantization of Convolutional Neural Networks on an Adaptive FPGA Accelerator

Research on quantization of Convolutional Neural Networks (ConNN) has gained attention recently due to the increasing demand for using ConNN models on embedded devices. The quantization method involves compressing the ConNN model to simplify complex computing and reduce resource requirements. Despite this, simply mapping a 32-bit ConNN model to a lower bit can have a negative impact on accuracy. One of the limitations of the quantization is the diversity of parameters in the ConNN model. Different layers have different structures. Thus, using the same quantization method for all layers in the ConNN model can lead to sub-optimal performance. Therefore, we propose a mixed-method quantization, a compression technique that uses different quantization approaches for a single ConNN model. We also propose an adaptive accelerator for quantized ConNN where its architecture is reconfigured during runtime using partial reconfiguration capability. The experimental results show that the proposed design achieves accuracy close to 32-bit models when quantizing ConNN models to 4 bits without retraining. In addition, the adaptive accelerator can achieve the highest resource efficiency of 1.11 GOP/s/DSP and 1.49 GOP/s/kLUT.
Temporal Derivatives Enabled by Neural Network-based Multilayered Metamaterial Designs

In recent years, the arbitrary control of electromagnetic waves enabled by metamaterials and metasurfaces has opened interesting avenues to explore alternative wave based computing paradigms. Notably, analog optical computing is becoming a powerful tool for fast and energy efficient processing of mathematical operations. In this work, we will present our recent progress in the neural networks design of multilayered metamaterials capable of taking the temporal derivative of normally incident modulated temporal signals.
A Study on the Effect of a nuclear Worker"s Turnover Intention - Focused on Government Trust, Job Satisfaction, and Policy Acceptance for Nuclear Power Plant Policy -

본 연구는 전 세계적으로 각광받는 원자력 산업의 국가적 재산인 산업인력유출을 방지하고자 원자력 관련 기관 근로자들의 이직의도에 미치는 영향요인을 규명하고 이들을 포용하고자 하는 정책적 함의를 제시하는 것이 목적이다. 이를 위해 직무특성, 고용불안, 나이가 직무만족을 매개로 이직의도에 미치는 영향에 대하여 분석하고, 정부신뢰가 탈원전 정책에 대한 정책수용을 매개로하여 이직의도에 미치는 영향에 대한 분석을 실시한다. 가설검증을 위해 빈도분석 및 기술통계, 요인분석, 신뢰도분석, 공분산구조분석 등을 실시하였다. 분석결과, 직무특성은 직무만족에 정(+)의 영향을, 고용불안은 직무만족에 부(-)의 영향을 미치며, 직무만족은 이직의도에 부(-)의 영향을 미치는 것으로 나타났다. 정부신뢰는 정책수용에 정(+)의 영향을 미쳤으나 정책수용과 이직의도 간 관계는 유의하지 않은 것으로 나타났다. 직무만족과 이직의도와의 기존 선행연구와 유사한 결과를 나타냈으나, 탈산업 정책에 대한 정책수용이 반드시 해당 산업 근로자의 이직의도로 이어지지 않는다는 결과가 확인되었다. The purpose of this study is to identify factors influencing the turnover intention of workers in nuclear-related institutions and to present policy implications to embrace them in order to prevent the outflow of industrial manpower, a national property of the nuclear energy industry that is in the spotlight around the world. To this end, the effect of job characteristics, anxiety about dismissal, and age on turnover intention through job satisfaction is analyzed, and the effect of government trust on turnover intention through policy acceptance for nuclear power plant policy is analyzed. Frequency analysis, descriptive statistics, factor analysis, reliability analysis, and covariance structure analysis were conducted to verify the hypothesis. As a result of the analysis, it was found that job characteristics had a positive (+) effect on job satisfaction, employment anxiety had a negative (-) effect on job satisfaction, and job satisfaction had a negative (-) effect on turnover intention. Government trust had a positive (+) effect on policy acceptance, but the relationship between policy acceptance and turnover intention was not significant. The results were similar to previous studies on job satisfaction and turnover intention, but it was confirmed that the policy acceptance of deindustrial policies did not necessarily lead to the turnover intention of the industrial worker.