Reviewerly/exHarmony huggingface dataset:
Mayfly optimization with chaos-based image encryption technique for cloud computing environment

Cloud computing (CC) technologies become popular due to their characteristics of maximum availability, flexibility, and scalability. Despite the benefits of CC, security becomes a major challenging issue that can be resolved by the use of image encryption techniques. Numerous works have been carried out with several approaches, and effective models need to be designed to enhance secure image encryption algorithms. Recently, chaotic approaches have been available in various domains, such as design of cryptosystems and image encryption. Chaos-based methods utilize random chaos sequences for image encryption, which are highly secure and fast. In this view, this study introduces a unique mayfly optimization with chaos-based image encryption (MFO-CBIE) technique for CC environment. The major intention of the MFO-CBIE model is to accomplish security in the CC environment via encryption and key generation process. The proposed MFO-CBIE model follows a three-stage process, namely image encryption, optimal key generation, and image decryption. In addition, the MFO-CBIE model employs the MFO algorithm for the optimal key generation process of the chaos-based encryption technique. Once the images are encrypted, they are securely transmitted to the cloud server. On the receiver side, the cipher image gets accessed from the cloud server, and then the decryption process is carried out. The experimental evaluation of the MFO-CBIE model is tested using benchmark images, and the results ensured the promising performance of the MFO-CBIE model over recent approaches.
Methodology for Blockwise Behavioral Modeling of ICs for Narrow-Band RF Interference Injection

A behavioral modeling methodology based on echo state networks for time-domain simulations of the conducted immunity of integrated circuits is proposed. The methodology is applied to a buffered voltage reference integrated circuit that is subjected to dc functional signals with superimposed radio-frequency (RF) interference signals. Behavioral models of three analog subcircuits of the buffered voltage reference are built that capture the RF behavior of the voltages and currents observed in the time-domain simulations of the narrow-band RF interference injection. The behavioral models are interchangeable with the transistor-level subcircuits and they ensure the correct nonlinear impedance toward the connected transistor-level circuits. The conditions for the stability of the resulting top-level circuit with different combinations of behavioral model and transistor-level subcircuits are derived, and the accuracy of the presented behavioral models in the circuit simulator is evaluated.
Statement-Level Software Defect Prediction Based on Improved R-Transformer

Engineers use software defect prediction (SDP) to locate vulnerable areas of software. Recently, statement-level SDP has attracted the attention of researchers due to its ability to localize faulty code areas. This paper proposes DP-Tramo, a new model dedicated to improving the state-of-the-art statement-level SDP. We use Clang to extract abstract syntax trees from source code and extract 32 statement-level metrics as static features for each sentence. Then we feed static features and token sequences as inputs to our improved R-Transformer to learn the syntactic and semantic features of the code. Furthermore, we use label smoothing and weighted loss to improve the performance of DP-Tramo. To evaluate DP-Tramo, we perform a 10-fold cross-validation on 119,989 C/C++ programs selected from Code4Bench. Experimental results show that DP-Tramo can classify the dataset with an average performance of 0.949, 0.602, 0.734 and 0.737 regarding the recall, precision, accuracy and F1-measure, respectively. DP-Tramo outperforms the baseline method on F1-measure by 1.2% while maintaining a high recall rate.
MaMiPot: a paradigm shift for the classification of imbalanced data

The most frequently used approach for imbalance learning is resampling. In this technique, the minority class is oversampled. Most of the algorithms used for this purpose are variants of the well-known algorithm named as SMOTE, which is based on creating synthetic samples on the lines connecting selected minority instances. The variants are mainly designed to alleviate the shortcomings of SMOTE where, the idea of generating synthetic samples to avoid the bias on the majority class is preserved. This study aims at following a different path. Instead of balancing, the proposed approach is based on repositioning the samples. In particular, the classifier is enforced to learn the decision regions of the minority class by repositioning the majority class samples. Consequently, the regions in which positive instances exist will not be outnumbered the majority class samples. Hence the classifier labels these regions as the minority class. To tackle the outliers, the minority samples are repositioned in small steps to avoid distorting the original distribution. The potential of the proposed repositioning scheme is also evaluated as a preprocessing algorithm for SMOTE. The experiments that are conducted on 52 datasets from the KEEL repository have shown that the proposed approach is highly effective, when evaluated in terms of F-score, G-mean and AUC.
Qualitative differences between evolutionary strategies and reinforcement learning methods for control of autonomous agents

In this paper we analyze the qualitative differences between evolutionary strategies and reinforcement learning algorithms by focusing on two popular state-of-the-art algorithms: the OpenAI-ES evolutionary strategy and the Proximal Policy Optimization (PPO) reinforcement learning algorithm – the most similar methods of the two families. We analyze how the methods differ with respect to: (i) general efficacy, (ii) ability to cope with rewards which are sparse in time, (iii) propensity/capacity to discover minimal solutions, (iv) dependency on reward shaping, and (v) ability to cope with variations of the environmental conditions. The analysis of the performance and of the behavioral strategies displayed by the agents trained with the two methods on benchmark problems enable us to demonstrate qualitative differences which were not identified in previous studies, to identify the relative weakness of the two methods, and to propose ways to ameliorate some of those weaknesses. We show that the characteristics of the reward function has a strong impact which vary qualitatively not only for the OpenAI-ES evolutionary algorithm and the PPO reinforcement learning algorithm but also for other reinforcement learning algorithms, thus demonstrating the importance of optimizing the characteristic of the reward function to the algorithm used.
Dynamics evolution prediction from time series data with recurrent neural networks in a complex system

Time series data can be used to predict the dynamical behaviors without knowing equation model of a system. In this study, long-short term memory (LSTM) neural network is implemented to construct a complex dynamical system from data series. The network is trained through minimizing the loss function to obtain the optimal weight matrices of LSTM cells. We find that the LSTM network can well ”learn” the information of the complex system. The data series generated from periodic orbits of a nonlinear system can be exactly predicted by comparing the output of neural networks with the real complex system. For the chaotic data series, the time evolution of trajectories could exactly match the actual system in the short-term data. Moreover, the long-term ergodic behavior of the complex system remains in our prediction, although such chaotic data series are quite sensitive to the initial conditions and the ensuing increase in uncertainty.
ANALISIS KEMAMPUAN BELAJAR MATEMATIKA SISWA SEKOLAH DASAR DI MASA TRANSISI PEMBELAJARAN

This research was conducted with the aims to find out how far the mathematics learning ability of elementary school students during the learning transition. The method used in this research is a qualitative descriptive research method, with the samples used as the subject of this research as many as 3 elementary school students and the sampling technique used is purposive sampling technique. Based on the results of the students' mathematics learning ability tests conducted in this research, it was found that the FR subject got a score of 96 in the very good category, the MS subject got a score of 78 in the good category, and the ZV subject got a score of 80 in the good category. The avarage score of the students’ mathematics learning ability test results in this research was 84.6. Based on this, it is known that the mathematics learning ability of elementary school students during the learning transition period is currently in the good category.
Revisiting natural user interaction in virtual world

Natural user interaction in virtual environment is a prominent factor in any mixed reality applications. In this paper, we revisit the assessment of natural user interaction via a case study of a virtual aquarium. Viewers with the wearable headsets are able to interact with virtual objects via head orientation, gaze, gesture, and visual markers. The virtual environment is operated on both Google Cardboard and HoloLens, the two popular wireless head-mounted displays. Evaluation results reveal the preferences of users over different natural user interaction methods.
Ceaseless steganographic approaches in machine learning

Steganography is the showing of disguising a message inside another message or a certifiable article like message, picture, or video is covered inside another record, message, picture, or video. The restricted intel is concealed so to speak that it not unmistakable to the normal eyes. Huge learning progression, which has arisen as a key asset in different applications including picture steganography, has gotten extended thought lately. Where generally single strategy is utilized in encryption which can be decoded effectively so to beat this method in this project three different procedures are utilized, for example, Recurrent Neural Network-based (RNN), which can consequently produce excellent message covers based on a mystery bitstream that should be covered up. Convolutional Neural Network (CNN) are profound learning calculations that are exceptionally strong for the examination of pictures, which is utilized to encode the produced text covers into a little picture. We prepared our model with an enormous number of misleadingly produced tests and acquired a great gauge of the measurable language model and HIGAN, which develops the encoding network made from holding up blocks to mask the combination secret picture into another grouping picture of the more unpretentious size to yield a lower bending and higher visual quality steganographic picture. The continuous endeavor means to use steganography for an image with another image using spatial region methodology.
BQ-Chem: A Quantum Software Program for Chemistry Simulation Based on the Full Quantum Eigensolver Algorithm

We describe a quantum chemistry simulation software program BQ-Chem, which can calculate the low-energy spectrum and potential energy surface of molecules on a quantum computer. BQ-Chem is based on the full quantum eigensolver (FQE), which is implemented with a quantum gradient descent algorithm. Benefiting from FQE, BQ-Chem can perform all the calculations on a quantum computer. Compared with the classical optimization methods which encounter the optimization difficulty of high-dimensional and multivariable functions in dealing with multielectron orbitals of macromolecules, FQE provides an exponential speedup. FQE works fully on a quantum computer; thus, BQ-Chem can be smoothly transited to future large-scale quantum computers.
Corruption, imported innovation, and growth: Evidence using the panel smooth transition regression approach for developing countries

Abstract This article investigates the nonlinear relationship between corruption, imported innovation (imports of high technology), and economic growth in developing countries from 2009 to 2018. We used a sample of 38 countries divided into two subsamples. The first covers 21 African countries, and the second covers 17 non‐African countries. The empirical results from the panel smooth transition regression model indicate that there is a threshold effect in the two relationships: corruption–imported innovation and corruption–growth. Specifically, we found that corruption exerts a significant effect only for non‐African countries. However, no significant effect was found for the whole sample and African countries. Furthermore, we found that below certain thresholds, corruption significantly decreases the level of growth for both the whole sample and the two subsamples. Above these thresholds, the effect of corruption becomes positive and significant.
Perancangan Sistem Produksi Lampu dengan Metode Perancangan Berorientasi Objek di PT. Cosmo Technology Indonesia Kabupaten Sukabumi

The development of information technology (IT) so rapidly, including for the field of ERP (Enterprice Resources Planning). To design and build an ERP system, it is necessary to first analyze the system to be built, especially for the design of Rekayas Software (RPL). The results of this study are expected to find the results of good and correct analysis so as to be able to implement the system into a program using JAVA.&#x0D; &#x0D; This research consists of several parts analysis. The core of this research is to review the problems that exist in the company, in determining user requirements or user needs, then generate design in the form of diagrams used as a system design tool. The point explanation that will be done is Business Overview, Organizational Structure, Department Description, User Requirement. While the design of the system using UML consisting of several diagrams are Activity Diagram, Use Case Diagram, Sequence Diagram, Class Diagrams, Entity Diagrams, Communication diagrams, State Machine Diagrams, Component Diagrams, Deployment Diagrams, Composite Structure Diagrams, Interaction Overview Diagrams, Object Diagrams , Package Diagram, Timing Diagram,&#x0D; &#x0D; The author takes the object case that is PT. COSMO TECHNOLOGY which is located at Jl. Raya Segog Km.14 Batununggal Village Kec. Cibadak, Sukabumi-West Java. Is a company engaged in decorative lights and is developing a business in the field of toys.&#x0D; &#x0D; Keywords: ERP, UML&#x0D; &#x0D; &#x0D;
PREP: A Post-Requirements Effort Estimation Method in Scrum's Sprint Grooming

Effort estimation in the software development process using Scrum is performed to determine the time the development team needs to complete user stories and the sprint's complexity level. Inaccurate effort estimation can result in user stories from the previous sprint having to be carried over to the next sprint, budget overruns, project delivery delays, and incorrect schedule estimates. The inaccurate estimation occurs in one product development in company XYZ, with average estimation performance for only 57.12%. This performance is less than expected, 75%. This study designs a method that the development team can use to improve the accuracy of the effort estimation, focusing on improving the quality of user stories and the estimation process in an e-commerce company in Indonesia, XYZ. The effort estimation method design developed, PREP (Post-Requirements Estimation Procedure), is then evaluated by looking at the reduction in the number of bugs and calculating the estimation accuracy with Balanced Relative Error bias (BREbias). The PREP method experiment in product development in XYZ shows bug reduction from 46.7 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">%</sup> to 15.9% and increases the accuracy of the estimates by 23.63%. The effort estimation method validation shows a positive trend for delight, effort, and functionality aspects but requires a high level of understanding and more time to implement it.
Human Upper Limb Motion Recognition Using IMU sensors and Artificial Neural Networks

Computer vision researchers have been tasked with developing applications to recognize the human body's structure, movements, and tracking. Recognizing human upper limb movements has brought benefits in physical therapy, virtual reality, human-robot systems, sports. This paper presents a multi-layer perceptron (MLP) artificial neural network (ANN) designed to recognize the upper limb movements performance in daily life tasks. For this, inertial measurement units (IMU) were employed to acquire information regarding of the upper limb movement. The performance of the model was assessed through a confusion matrix and the receiver operator characteristic (ROC) curves. Recognition accuracy obtained for the ANN model was 97.39%, the mean area under the curve (AUC) of the ROC curves was 0.973. According to the results, the proposed ANN could recognize upper limb movements in daily life tasks. Further research is suggested to test the model with fine movements.
FPGA implementation of HDR synthesis processing with image compression techniques

This paper presents an FPGA implementation of real-time high dynamic range (HDR) synthesis, which expresses a wide dynamic range by combining multiple images with different exposures using image pyramids. We have implemented a pipeline that performs streaming processing on images without using external memory. However, implementation for high-resolution images has been difficult due to large memory usage for line buffers. Therefore, we propose an image compression algorithm based on adaptive differential pulse code modulation (ADPCM). Compression modules based on the algorithm can be easily integrated into the pipeline. When the image resolution is 4K and the pyramid depth is 7, memory usage can be halved from 168.48 % to 84.32 % by introducing the compression modules, resulting in better quality.
Autonomous driving system with feature extraction using a binarized autoencoder

In this study, we present an autonomous driving sys-tem that utilizes a binarized autoencoder implemented on a Field Programmable Gate Array (FPGA). The binarized autoencoder compresses the image into optimal features in this system. The recurrent neural network then determines the following control based on the feature values extracted from the autoencoder and the rotation speed of the motor. We reduced the model size by binarizing the autoencoder because of the limited on-chip memory of the FPGA. We implemented the system on an Ultra96-V2, a board with a programmable logic and processing system. The robot employing our implemented system exhibits robust control by recognizing the entire road marking and road edge line as a feature and drives autonomously along the specified route.
Measuring the Effect on Prediction Accuracy When Data Transformation is Overlooked by Predicting the Development Effort of Software Projects

The software effort prediction spent for teams of developers is a main activity of the software planning. When a new prediction model is proposed, a common guideline is to transform data when there exists skewness, heteroscedasticity, or outliers. However, its performance could result awful if actual data is used for a software manager. Thus, in the present study, we empirically measured that impact on the prediction accuracy when nontransformed data value is used in the explanatory variable. Since statistical regression equation has been one of the models mostly used in the mentioned activity, in the present study, eight of them are generated from regression analysis. They were generated involving 1,500 software projects selected from an international public repository (i.e., ISBSG) and applying a hold-out cross validation method. The selection of each the eight data sets observed the guidelines suggested for the ISBSG. The Magnitude of Relative Error (MRE) and Pred <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$(l)$</tex> were used as prediction accuracy measures. We can conclude that, although predictions can seem trustworthy, a software manager should be careful about using nontransformed data in prediction models when these have been generated from transformed data.
HPIPE NX: Boosting CNN Inference Acceleration Performance with AI-Optimized FPGAs

With the ever-increasing compute demands of artificial intelligence (AI) workloads, there is extensive interest in leveraging field-programmable gate-arrays (FPGAs) to quickly deploy hardware accelerators for the latest convolutional neural networks (CNNs). Recent FPGA architectures are also evolving to better serve the needs of AI, but accelerators need extensive re-design to leverage these new features. The Stratix 10 NX chip by Intel is a new FPGA that replaces traditional DSP blocks with in-fabric AI tensor blocks that provide 15x more multipliers and up to 143 TOPS of performance, at the cost of lower precision (INT8) and significant restrictions on how many operands can be fed to the multipliers from the programmable routing. In this paper, we explore different CNN accelerator structures to leverage the tensor blocks, considering the various tensor block modes, operand bandwidth restrictions, and on-chip memory restrictions. We incorporate the most performant techniques into HPIPE, a layer-pipelined and sparse-aware CNN accelerator for FPGAs. We enhance HPIPE's software compiler to restructure the CNN computations and on-chip memory layout to take advantage of the additional multipliers offered by the new tensor block architecture, while also avoiding stalls due to data loading restrictions. We achieve cycle-by-cycle speedups in tensor mode of up to <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathbf{8}.\mathbf{3}\mathbf{x}$</tex> for Mobilenet-v1 versus the original HPIPE design using conventional DSPs. On the FPGA, we achieve a throughput of 28,541 and 29,429 images/s on Mobilenet-v1 and Mobilenet-v2 respectively, outperforming all previous FPGA accelerators by at least 4.0x, including one on an AI-optimized Xilinx chip. We also outperform NVIDIA's V100 GPU, a machine learning targeted GPU on a similar process node with a <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathbf{1}.\mathbf{7}\mathbf{x}$</tex> larger die size, by up to 17x with a batch size of one and 1.3x with NVIDIA's largest reported batch size of 128.
Hardware SAT Solver-based Area-efficient Accelerator for Autonomous Driving

Today's embedded systems applications consisting of a variety of tasks are becoming larger and more complex. Hence, when multiple tasks need to be accelerated, designing a dedicated accelerator for each task would be difficult on small devices due to large area overhead. In this study, we propose an efficient accelerator for autonomous driving, which is a theme of a design competition held at International Conference on Field Programmable Technology. Focusing on two key tasks (path planning and object detection), we formulate each of them as a satisfiability problem (SAT) and use a hardware SAT solver as a common accelerator for these tasks. We present efficient problem formulation methods for solving these tasks on a small FPGA. Experimental results show the effectiveness of our work for these tasks.