Reviewerly/exHarmony huggingface dataset:
Algorithmic Design of Autonomous Housekeeping Robots through Imitation Learning and Model Predictive Control

Intelligent robots are more and more adopted into humans&#x0027; regular life, from work to leisure. For instance, autonomous vehicles are running on public roads for testing, intelligent moving robots are deployed in hotel or museum lobbies to help customers. In this project, we will design algorithmic autonomous housekeeping robots to help people with housework. To enable intelligent and efficient motion planning that allows the robots to execute given tasks without colliding with humans or static obstacles (such as furniture at home), we use a combination of imitation learning and model predictive control (MPC). First, we will use MPC to generate and collect multiple optimal actions for randomly generated initial conditions of the robots, obstacles and target locations. Based on that, we use imitation learning to learn a policy network from the optimal policies generated by MPC. Moreover, we also adopt the concept of data aggregation (DAgger) to further improve the learning performance. The experimental results verify the effectiveness of our algorithms.
An FPGA based Method of Polynomial Multiplication

Polynomial multiplication is widely used in communication, signal and image processing. In practical application, the system often needs to process a large amount of data efficiently and then output the results. However, since polynomials are always multivariable and large-scale, software algorithms alone are not effective to meet the actual requestments. In addition, the key to polynomial multiplication is the operation of matrix multiplication. Nevertheless, due to the increasingly complex structure of matrix multiplication, it is challenging to realize efficient processing and calculation of large matrices. With the development of FPGA, LSI manufacturing, memory interface and EDA tools, it is possible to apply hardware to the polynomial multiplication. To solve the above problems, we have designed and implemented a polynomial multiplication platform based on FPGA, which combines hardware and software to make up for the shortcomings of current software algorithms and at the same time, it can efficiently and effectively operate polynomial multiplication.
Webiplan as a Webinar Management Information System for Optimizing Online Seminars During the COVID-19 Pandemic

Humans are facing multiple disruptions caused by the development of the industrial revolution 4.0 and the covid-19 pandemic. This changes the work pattern of the people who are required to carry out all online-based activities, including the implementation of seminars or conferences. The amount of training and socialization carried out by webinars requires a good webinar management system. The purpose of this paper is to develop a web-based information system for webinar activity management under the name webiplan. The method used in system development is the waterfall method. In the waterfall method, there are 5 stages, including requirements, design, implementation, verification, and maintenance. The subjects of this research are prospective end users of the system, namely lecturers and students who are accustomed to participating in webinars. The design and development stages are tailored to the user's needs obtained in the previous phase and are described as product backlog items. At the verification stage, three stages are used to evaluate the feasibility level of the information system, including: unit testing using whitebox testing, validation testing using the blackbox testing method, and compatibility testing. The results of whitebox testing and blackbox testing showed that every feature and user task could run well so as to provide convenience for the organizers and participants in the implementation of the webinar. This Webiplan is also compatible with various web browsers so as to give users flexibility in using it.
Counting Broiler from Poultry House Video Using Faster Region-based Convolutional Neural Networks

In a poultry house, livestock object detection is essential for farmers to prepare the amount of food. Counting broiler can be done through video monitoring placed at the top. If video processing is done automatically, it will collide with memory, especially with many frames. Image approach can be taken considering the broiler is in a poultry house or closed room, so that one frame per second processing is quite representative. In addition, the observed broilers are considered to have a similar shape, and their identity is ignored. Therefore, this study proposed using the Faster Region-based Convolutional Neural Network (Faster R-CNN) method for broiler livestock. Faster R-CNN is one of the models of object detection that can be used to count the number of objects. In Faster RCNN, choosing the proper architecture and configuration must be considered to find good performance. The experiment showed that using ResNet-101, Faster R-CNN achieves the best accuracy than other famous CNN architecture, such as ResNet-50 and ResNeXt-101, with an average precision of 50% achieving 92.8% and average precision of 75%, achieving 78.1%. The ResNet-101 architecture has more convolution depth. The depth causes ResNet-101 to do better feature extraction even though ResNeXt-101 has more depth convolution, but it made ResNeXt-101 too heavy to do feature extraction, making ResNeXt-101 have the largest size model. However, the Faster R-CNN model using ResNet-101 is better implemented for real-time detection.
Improving the Accuracy of COCOMO II Using Extended Gamma GWO

Most software industries use the Constructive Cost Model (COCOMO) II to estimate the cost of software projects with features that are dependent on the cost drivers. The cost driver’s value can influence the project cost estimate accuracy. Nevertheless, the accuracy of the model could be further improved. The difference between the cost estimated by using COCOMO II and the actual project cost is not adequately close. To enhance the accurateness of COCOMO II, an extended method of Gamma Grey Wolf optimization (GWO) is employed for optimization. In our research, the COCOMO II-Extended Gamma GWO produced a higher MMRE value (51.09%) than in models proposed in previous studies, namely COCOMO II-Bee Colony optimization (12.92%), and COCOMO II-GWO (1.110%). This indicates that the proposed strategy of enhancing the model was not able to improve MMRE than original model. This finding could be caused by the additional population, namely Gamma, it not better than the previous populations, namely Alpha, Beta, Delta.
Moving Image YOLOv3 Process Comparison with Multiple Resolutions and Frames-per-Second

Many city/district governments have installed Closed-Circuit Television (CCTV) on the streets as a form of their smart city. This CCTV installation is usually only useful as a recording of events at a location that needs to be seen at a time. Actually, with the same capital, moving image recordings produced by CCTV can also be used for various things, such as vehicle counters that can show the level of road density. In this paper, the researcher tries to use “You Only Look Once” or YOLO with several configurations of the moving image feature to recognize objects as vehicles. Because one factor that need to be considered in object detection for moving images is the need for large computational resources and long processing time. This study examines the effect of reducing the resolution size feature and the number of frames per second (fps) of moving images in the YOLO process, to obtain optimal use of computing resources and processing time. Reducing the resolution and the number of frames per second does not have much effect on the use of Central Processing Unit (CPU) computing resources used by researchers, which is around 9S.5l% on average (with Xeon 8 cores). However, it can reduce processing time by up to 70%.
Solar power forecasting: Investigating the robustness of feedforward neural networks to adapt to utility-scale PV-system fault conditions

Photovoltaic (PV) power forecast models are an important solution to reduce the uncertainty associated with PV system power supply, which is characteristically intermittent. Traditionally, machine learning forecast models are trained with a perfect-data representation of PV-system behaviour. However, utility-scale power output can change abruptly due to internal PV-system fault conditions (broken modules, failed inverters, etc.) or routine maintenance. Given this context, there is concern regarding the ability of these perfect-data models to adapt forecasts to a sudden change in power output. The popular Feedforward Neural Network (FFNN) model is used to deliver 1 - 6 h ahead forecasts for a 75 MW rated PV system. With power output systematically reduced by introducing fault conditions, the robustness of this widely used ML-based forecasting solution is evaluated. From the overall results, the FFNN model was shown to maintain an impressive degree of robustness for scenarios where less than 10 % of rated PV-system capacity was lost.
Accelerated Adaptive Learning Rate

An artificial neural network (ANN) is a kind of human brain learning method software simulation. ANN is suitable for classification issues and has proven successful in many practical applications. Signal interference, power consumption, and signal noise are the main issues in signal processing research. This paper proposes AALR; this project attempts to develop a neural network demodulator and machine learning technology. As a result of the new concepts, the neural network demodulator processing speed has improved by 88%, increasing output accuracy by 7%.
Modern Package Design Using Digital 3D Image Processing Technique

In the extensive age, dear designate perplexity and relatively supercilious show charge in the traditive parcel extend project composition, the double discriminator GAN is ply to the bale work indicate composition. On the basis of BicycleGAN, a topic discriminator is added, and the analogous privation sine and external province are reformed. In the proof, the input aim is a likeness suit of “margin idol + source cast,” and the product goal is the copy with the top 10 chance of genuineness. The trial arise are appraised from the three aspects of variegation, PSNR importance, and SSIM appraise. The arise shows that the effigy produced by the double discriminator GAN not only has improved variegation but also better peculiarity of show lowdown. Therefore, it is practicable to devote the double discriminator GAN to the look sketch of parcel products. On the one side, it can stipulate designers with sketch breath, and on the other side, it can also rescue manpower and significant cause and disapprove duty ability.
Perencanaan Kebutuhan Air Pada Areal Irigasi Bendung Walahar Karawang, Jawa Barat

&#x0D; Planning for water needs for irrigation areas is carried out to determine the water needs for plants to grow and develop. The source of water obtained for meeting water needs comes from the Walahar Dam. The results of water demand planning can also be used to simulate cropping patterns. Cropping patterns are very helpful for farmers in determining effective and efficient planting times. Calculation of plant water needs is processed from climatological data and rainfall data, the results of which are demand discharge. Based on the results of the calculation of water requirements for plants, the average discharge requirement is 0.65 m3/second – 9.59 m3/second, while the available water or available discharge in Walahar Weir is 183.90 m3/second. So it can be concluded that the available water in Walahar Weir can meet the water needs of plants.&#x0D; Keywords: Discharge, Climatological Data, Rainfall Data, Plant Water Demand Planning&#x0D;
Multiexpert Adversarial Regularization for Robust and Data-Efficient Deep Supervised Learning

Deep neural networks (DNNs) can achieve high accuracy when there is abundant training data that has the same distribution as the test data. In practical applications, data deficiency is often a concern. For classification tasks, the lack of enough labeled images in the training set often results in overfitting. Another issue is the mismatch between the training and the test domains, which results in poor model performance. This calls for the need to have robust and data efficient deep learning models. In this work, we propose a deep learning approach called Multi-Expert Adversarial Regularization learning (MEAR) with limited computational overhead to improve the generalization and robustness of deep supervised learning models. The MEAR framework appends multiple classifier heads (experts) to the feature extractor of the legacy model. MEAR aims to learn the feature extractor in an adversarial fashion by leveraging complementary information from the individual experts as well as the ensemble of the experts to be more robust for an unseen test domain. We train state-of-the-art networks with MEAR for two important computer vision tasks, image classification and semantic segmentation. We compare MEAR to a variety of baselines on multiple benchmarks. We show that MEAR is competitive with other methods and more successful at learning robust features.
Voice analysis for personal identification using FFT, machine learning and AI techniques

In this paper a hybrid approach for spectral analysis and voice profiles recognition by techniques on the base of Machine Learning and Artificial Intelligence (AI) have been proposed. For voice-processing procedures was applied the algorithm of Fast Fourier Transformation (FFT) with several different window types, respectively Hamming, 4 Term BHarris, Flat Top and Hanning. The spectral feature extraction is used for pre-processing training sets about k-Nearest Neighbours (k-NN) classification models and Feed-Forward Neural Networks (FFNN) for individual’s personal identity about target group of people. Euclidean, Cityblock, Minkowski and Chebychev metric distances were applied in k-NN model creation. The design models are evaluated through resubstitution and cross-validation techniques. Levenberg- Marquardt learning algorithm was used to FFNN architectures with Linear and Tangent Sigmoid activation functionsin network outputs. High quality k-NN and FFNN models in regard to personal voice identification with level of accuracy achieved 97.68 % and 100.0 % were synthesized.
Applying Knowledge-Driven Architecture Composition with Gabble

Service interoperability for embedded devices is a mandatory feature for dynamically changing Internet-of-Things and Industry 4.0 software platforms. Service interoperability is achieved on a technical, syntactic, and semantic level. If service interoperability is achieved on all levels, plug-and-play functionality known from USB storage sticks or printer drivers becomes feasible. This reduces the manual effort for system integration for home automation systems and, in the case of the producing industry, allows for micro-batch size production, individualized automation solution, or job order production. However, interoperability at the semantic level is still a problem for the maturing class of IoT systems. In this work, we present a software engineering tool in detail that allows storing, sharing, and reusing integration knowledge between software interfaces incrementally by looking at integration cases instead of domain models.
Identifying Errors in Russian Web Corpora

Abstract The explosion of the Web leads to the production of large amounts of texts and inevitably influences their quality. Errors that tend to occur more often can distort results, especially when texts are used for scientific purposes, in language teaching or learning. Hence, there is a need to examine the existing corpora based on web texts and to clean up the data, which may contain such “noisy” fragments. In our study, we deal with the problem of errors and analyze the Aranea Russicum Maximum corpus. Among such errors, we can name, above all, encoding errors, incorrect font types, as well as segments written in other languages. These phenomena result in incorrect morphological analysis and lemmatization, frequency distortion, as well as the fact that lexical units cannot be found and therefore displayed to corpus users. The paper focuses on the errors, describes their types and outlines possible ways to eliminate them.
Research on Iterative Decoupling Algorithm in Color Fringe Projection Profilometry

An iterative decoupling algorithm (IDA) is proposed to improve the performance of a color fringe projection measurement system that suffers from the complicated color coupling effect. Firstly, we analyzed the effect of projection intensity and the measured object on color coupling, and the mechanism of coupling; then, get the coupling coefficients by three red, green, and blue uniform intensity images; finally, use the proposed IDA method to correct the captured color fringe pattern for phase recovery. The advantage of the IDA method is that only three uniform color images are needed to pre-calibrate the coupling coefficients and are therefore easy to implement, highly robust, and widely used. Simulation and experimental results present that the proposed algorithm can significantly reduce the interference of color coupling and then improve the measurement accuracy of the color-channel-based phase-shifting profilometry.
Deep Learning-Enabled Spatial Phase Unwrapping for 3d Measurement

In terms of 3D imaging speed and system cost, the single-camera system projecting single-frequency patterns is the ideal option among all proposed Fringe Projection Profilometry (FPP) systems. This system necessitates a robust spatial phase unwrapping (SPU) algorithm. However, robust SPU remains a challenge in complex scenes. Quality-guided SPU algorithms need more efficient ways to identify the unreliable points in phase maps before unwrapping. End-to-end deep learning SPU methods face generality and interpretability problems. This paper proposes a hybrid method combining deep learning and traditional path-following for robust SPU in FPP. This hybrid SPU scheme demonstrates better robustness than traditional quality-guided SPU methods, better interpretability than end-to-end deep learning scheme, and generality on unseen data. Experiments on the real dataset of multiple illumination conditions and multiple FPP systems differing in image resolution, fringe number, fringe direction, and optics wavelength verify the effectiveness of the proposed method.
On the use of generic types for smart contracts

Abstract This paper shows that generic types ( generics ) are useful for writing more abstract and more general smart contracts, but this comes with some security risks, reporting a concrete security issue found while using generics for writing smart contracts that implement shared entities for the Hotmoka blockchain. That issue can be used to steal the remuneration of validator nodes. This paper proposes a patch based on appropriate code rewriting. Namely, smart contracts are pieces of code that are deployed and executed in the context of a blockchain infrastructure in order to automatically enforce some effects when particular events occur. The writing of smart contracts is a complex and critical activity that can benefit from the use of high-level features of programming languages, and generics is one of them. In many programming languages, such as Java, generics are implemented by erasure , i.e. replaced by their upper bound type during compilation into bytecode. This is safe at source level, since the compiler takes care of checking that types are correct, before erasure. However, the erased types of the generated bytecode are consequently weaker. In a permissionless blockchain, where every user can call the bytecode of smart contracts installed by other users, these weaker types pose a risk of attack.
Effectiveness of Network Classroom Teaching Based on Genetic Algorithm

In online classroom teaching, the function of teaching system can play an important role in the effectiveness of classroom teaching. How to use genetic algorithm to optimize online classroom teaching system has become a research hotspot. Based on genetic algorithm, this paper proposes an adaptive genetic algorithm model based on the traditional algorithm. After setting the appropriate mutation probability, the model can improve the convergence speed. Moreover, based on adaptive genetic algorithm, combined with the direct value method and BT neural network theory, this paper constructs the online classroom teaching quality evaluation model and the teaching system test paper data model, and optimizes adaptive mutation genetic algorithm and BP neural network to evaluate the teaching effectiveness. Simulation experiments are carried out based on the algorithm model, and the visual parameter values are obtained. After experimental comparison, the initial value of the mutation rate is set between 0.002 and 0.004. For the network classroom teaching system, this paper introduces the system demand analysis, function module design, and database design in detail. Finally, through the questionnaire survey, this paper understands the network situation of students in class and the use of online classroom teaching platform in detail, analyzes the problems and influencing factors of online teaching, and finally puts forward the strategies to improve the effectiveness of online classroom teaching.
A Reinforcement Learning-Based Basketball Player Activity Recognition Method Using Multisensors

It is an effective means to use a computer auxiliary system to assist athletes in training. In this paper, we design a technical activity recognition system for basketball players. The system uses the sensing module bound to the basketball player to collect the activity data and uses the proposed Multilayer Parallel Long Short Term Memory (MP-LSTM) algorithm to recognize the activity. Moreover, in order to extend the working time of the system and reduce the energy consumption of the sensing module, we also utilize the classical reinforcement learning algorithm DQN to adaptively control the sampling frequency of the sensing module for making a trade-off between recognition accuracy and energy consumption. Experiment results show that the recognition accuracy of the proposed MP-LSTM algorithm reaches 94%, while the recognition accuracy of the system remains at about 90% after applying the DQN algorithm, and the energy consumption is reduced by 76%.
The Bravo of Human Capital Development and Empolyee Productivity in the Ministry of Information, Awka (2015 -2018)

The role played by human capital development cannot he over emphasized as many a/me to recognized that training offers a way to developing skill, enhancing productivity, guaranteeing of work and building workers loyalty to the organizations management. This research work focused on the imperative :> l Human Capital Development and Employee Productivity in the Ministry of Information, Awka (2015-2018). The main objective of I lie study is to examine the impact of human capital development and employee productivity. Specific objectives sought to find out if human capital development has increased workersâ€™ commitment in achieving high productivity, examine the impacts of human capital development in ministry of information Awka and identify factors militating against human capita/ development on ministry of information Awka. Scientific management theory by Fredrick Taylor was adopted in this research. The research made use of survey design and data was sowed through primary and secondary data. The finding from this research shows that human capital and developments are positively significant to employee productivity. The research work recommends amongst others Ilia! I he investment profile of the studied organization being tied to human capital development element like training and retraining Ion the jot} and o/J the job) of her human capitals should not be undermined, more proactive performance management system should be adopted and Systematic training should be carried out in the ministry professionally.