Reviewerly/exHarmony huggingface dataset:
Pengembangan Website Fakultas Teknik Universitas Hamzanwadi Berbasis Progressive WEB APP (PWA)

The Hamzanwadi University Faculty of Engineering website is an online news media that provides comprehensive information about the Hamzanwadi University Faculty of Engineering. However, in an effort to improve the quality of accessing this information, the website needs to be improved. By implementing Progressive Web Apps (PWA) technology, it is hoped that it can be a solution to existing problems. By implementing PWA technology, it will have a significant impact on the Faculty's website, when compared to before. Of course, with the application of PWA technology, the loading process on the website will be much lighter, faster, and more time efficient and open when not connected to the internet or offline.
Adaptation of students to professional-oriented activities based on media technologies

Media technologies make it simple for students to access accurate information wherever and whenever they want at a lower cost, however, many studies show that children do not see a difference between the real world and the fictional media world until they reach the age of 12. This study aims to determine primary school students' professional activity adaptation based on media technologies. The study was conducted in the spring term of 2020 - 2021, with 339 elementary school students voluntarily participating. The "Media technologies" questionnaire developed by the researchers was applied to primary school students. The questionnaire was developed and edited by experts in the field. Collected data were analyzed using an SPSS program. Frequency, percentage, average, standard deviation, minimum and maximum values, One Way Anova, and T-test were applied to analyze the data obtained from the media technologies survey. According to the results of the study, it was concluded that elementary school students have high results in determining media technologies concepts.&#x0D; &#x0D; Keywords: Media technologies, Distance Education, Primary School Students, Performance, Adaptation
ANALYSIS OF STUDENTS’ ERRORS IN WRITING COMPARATIVE AND SUPERLATIVE ADJECTIVE

Abstract-This research was conducted to find out the impact of writing comparative and superlative adjective to analyze students’ errors in writing and to observe the students’ error in writing. The main purpose of the research is to observe if students get the errors in their writing comparative and superlative adjective. Qualitative research is used. The findings indicated that the error of misinformation 43.28%, error of misordering 22,11%, error of addition 18,07%, and error of omission 16,52%. It showed that students encounter problems and difficulties in writing comparative and superlative adjective. The conclusion is that educators should provide more effective ways in teaching comparative and superlative adjective so that the students can be better in using that in writing. Keywords : Student’s error, Writing Skill, Comparative and Superlative Adjective
Modeling Knowledge Acquisition from Multiple Learning Resource Types

Students acquire knowledge as they interact with a variety of learning materials, such as video lectures, problems, and discussions. Modeling student knowledge at each point during their learning period and understanding the contribution of each learning material to student knowledge are essential for detecting students' knowledge gaps and recommending learning materials to them. Current student knowledge modeling techniques mostly rely on one type of learning material, mainly problems, to model student knowledge growth. These approaches ignore the fact that students also learn from other types of material. In this paper, we propose a student knowledge model that can capture knowledge growth as a result of learning from a diverse set of learning resource types while unveiling the association between the learning materials of different types. Our multi-view knowledge model (MVKM) incorporates a flexible knowledge increase objective on top of a multi-view tensor factorization to capture occasional forgetting while representing student knowledge and learning material concepts in a lower-dimensional latent space. We evaluate our model in different experiments toshow that it can accurately predict students' future performance, differentiate between knowledge gain in different student groups and concepts, and unveil hidden similarities across learning materials of different types.
[Detection of white blood cells in microscopic leucorrhea images based on deep active learning].

The number of white blood cells in the leucorrhea microscopic image can indicate the severity of vaginal inflammation. At present, the detection of white blood cells in leucorrhea mainly relies on manual microscopy by medical experts, which is time-consuming, expensive and error-prone. In recent years, some studies have proposed to implement intelligent detection of leucorrhea white blood cells based on deep learning technology. However, such methods usually require manual labeling of a large number of samples as training sets, and the labeling cost is high. Therefore, this study proposes the use of deep active learning algorithms to achieve intelligent detection of white blood cells in leucorrhea microscopic images. In the active learning framework, a small number of labeled samples were firstly used as the basic training set, and a faster region convolutional neural network (Faster R-CNN) training detection model was performed. Then the most valuable samples were automatically selected for manual annotation, and the training set and the corresponding detection model were iteratively updated, which made the performance of the model continue to increase. The experimental results show that the deep active learning technology can obtain higher detection accuracy under less manual labeling samples, and the average precision of white blood cell detection could reach 90.6%, which meets the requirements of clinical routine examination.白带显微图像中白细胞的数量可以提示阴道炎症的严重程度。目前对白带中白细胞的检测主要依靠医学专家们的人工镜检，这种人工检查耗时、昂贵且容易出错。近年来，有研究提出基于深度学习技术对白带白细胞实现智能检测，但是这类方法通常需要人工标注大量的样本作为训练集，标注代价高。因此，本研究提出运用深度主动学习算法来实现对白带显微图像中白细胞的智能检测。在主动学习框架下，首先以少量的标注样本作为基础训练集，采用更快的卷积神经网络（Faster R-CNN）训练检测模型，再自动挑选最有价值的样本进行人工标注，从而迭代更新训练集和相应的检测模型，使模型的性能不断提高。实验结果表明，深度主动学习技术能在较少的人工标注样本下获得较高的检测精度，对白细胞检测的平均精度达到了 90.6%，可以满足临床常规检查要求。.
Risk-Sensitive Reinforcement Learning: Near-Optimal Risk-Sample Tradeoff in Regret

We study risk-sensitive reinforcement learning in episodic Markov decision processes with unknown transition kernels, where the goal is to optimize the total reward under the risk measure of exponential utility. We propose two provably efficient model-free algorithms, Risk-Sensitive Value Iteration (RSVI) and Risk-Sensitive Q-learning (RSQ). These algorithms implement a form of risk-sensitive optimism in the face of uncertainty, which adapts to both risk-seeking and risk-averse modes of exploration. We prove that RSVI attains an $\tilde{O}\big(λ(|β| H^2) \cdot \sqrt{H^{3} S^{2}AT} \big)$ regret, while RSQ attains an $\tilde{O}\big(λ(|β| H^2) \cdot \sqrt{H^{4} SAT} \big)$ regret, where $λ(u) = (e^{3u}-1)/u$ for $u&gt;0$. In the above, $β$ is the risk parameter of the exponential utility function, $S$ the number of states, $A$ the number of actions, $T$ the total number of timesteps, and $H$ the episode length. On the flip side, we establish a regret lower bound showing that the exponential dependence on $|β|$ and $H$ is unavoidable for any algorithm with an $\tilde{O}(\sqrt{T})$ regret (even when the risk objective is on the same scale as the original reward), thus certifying the near-optimality of the proposed algorithms. Our results demonstrate that incorporating risk awareness into reinforcement learning necessitates an exponential cost in $|β|$ and $H$, which quantifies the fundamental tradeoff between risk sensitivity (related to aleatoric uncertainty) and sample efficiency (related to epistemic uncertainty). To the best of our knowledge, this is the first regret analysis of risk-sensitive reinforcement learning with the exponential utility.
Discrimination of earthquakes and quarries in Kula District (Manisa, Turkey) and its vicinity by using linear discriminate function method and artificial neural networks

In this study, seismic events in Kula district (Manisa, Turkey) and its vicinity have been investigated and then natural and artificial seismic activities are discriminated.Total of 77 digital vertical component velocity seismograms of seismic activities with M L ≤3.5 magnitude from seismic activity catalogs between 2009 to 2014 recorded by Manisa Kula (KULA) broadband station operated by Bogazici University, Kandilli Observatory and Earthquake Resarch Institute Regional Earthquake-Tsunami Monitoring Center (RETMC) were used in this study.The maximum S-wave and maximum P-wave amplitude ratio (Ratio) of vertical component velocity seismograms and power ratio for (1 and 12 sec.)(Complexity-C) and total signal duration (Duration) of the waveform were calculated.The earthquakes and the quarry blasts have been discriminated using linear discriminant function (LDF) and Back Propagation-Feed Forward Neural Networks (BPNNs) that is one of the learning algorithms at the artificial neural networks (ANNs) methods taking correlation between these parameters into consideration.39 (51%) of the 77 seismic activities were identified as quarry blasts and 38 (49%) of them as earthquakes LDF and ANNs methods have been applied together for the first time for Ratio-C, Ratio-logS and Ratio-duration parameter pairs with the data of Manisa and surroundings, and earthquakes and quarry blasts have been distinguished from each other.LDF and ANNs methods were compared for each pair of parameters.Both of two methods are successful but the ANNs method has higher accuracy percentage values than LDF method when there is sufficient number of data.The accuracy percentages are different for a pair of Ratio versus C, for a pair of Ratio versus logS and for a pair of Ratio versus duration, respectively.
Automated Utterance Generation

Conversational AI assistants are becoming popular and question-answering is an important part of any conversational assistant. Using relevant utterances as features in question-answering has shown to improve both the precision and recall for retrieving the right answer by a conversational assistant. Hence, utterance generation has become an important problem with the goal of generating relevant utterances (sentences or phrases) from a knowledge base article that consists of a title and a description. However, generating good utterances usually requires a lot of manual effort, creating the need for an automated utterance generation. In this paper, we propose an utterance generation system which 1) uses extractive summarization to extract important sentences from the description, 2) uses multiple paraphrasing techniques to generate a diverse set of paraphrases of the title and summary sentences, and 3) selects good candidate paraphrases with the help of a novel candidate selection algorithm.
A Bipartite Graph-Based Recommender for Crowdfunding with Sparse Data

It is a common problem facing recommender to sparse data dealing, especially for crowdfunding recommendations. The collaborative filtering (CF) tends to recommend a user those items only connecting to similar users directly but fails to recommend the items with indirect actions to similar users. Therefore, CF performs poorly in the case of sparse data like Kickstarter. We propose a method of enabling indirect crowdfunding campaign recommendation based on bipartite graph. PersonalRank is applicable to calculate global similarity; as opposed to local similarity, for any node of the network, we use PersonalRank in an iterative manner to produce recommendation list where CF is invalid. Furthermore, we propose a bipartite graph-based CF model by combining CF and PersonalRank. The new model classifies nodes into one of the following two types: user nodes and campaign nodes. For any two types of nodes, the global similarity between them is calculated by PersonalRank. Finally, a recommendation list is generated for any node through CF algorithm. Experimental results show that the bipartite graph-based CF achieves better performance in recommendation for the extremely sparse data from crowdfunding campaigns.
Towards Segmentation and Spatial Alignment of the Human Embryonic Brain Using Deep Learning for Atlas-Based Registration

We propose an unsupervised deep learning method for atlas-based registration to achieve segmentation and spatial alignment of the embryonic brain in a single framework. Our approach consists of two sequential networks with a specifically designed loss function to address the challenges in 3D first trimester ultrasound. The first part learns the affine transformation and the second part learns the voxelwise nonrigid deformation between the target image and the atlas. We trained this network end-to-end and validated it against a ground truth on synthetic datasets designed to resemble the challenges present in 3D first trimester ultrasound. The method was tested on a dataset of human embryonic ultrasound volumes acquired at 9 weeks gestational age, which showed alignment of the brain in some cases and gave insight in open challenges for the proposed method. We conclude that our method is a promising approach towards fully automated spatial alignment and segmentation of embryonic brains in 3D ultrasound.
The CASPER user-centric approach for advanced service provisioning in mobile networks

This paper presents an overview of the project CASPER,1 a 4-year Marie Curie Research and Innovation Staff Exchange (RISE) project running between 2016 and 2020, describing its objectives, approach, architecture, tools and key achievements. CASPER combines academic and industrial forces towards leveraging the expected benefits of Quality of Experience (QoE) exploitation in future networks. In order to achieve that, a QoE orchestrator has been proposed which implements the basic functionalities of QoE monitoring, estimation and management. With means of simulation and testbed emulation, CASPER has managed to develop a proprietary SDN Controller, which implements QoE-based traffic rerouting for the challenging scenario of HTTP adaptive video streaming, leading to more stable and higher QoE scores compared to a state-of-the-art SDN Controller implementation.
Securing Vulnerabilities in Docker Images

Docker is an alternative application development and publishing infrastructure tool to various virtualization environments such as Virtual box and the like. The most popular containerization platform is Docker which is the area where Docker images are run. Container is a lightweight contrasting option to full machine virtualization that includes exemplifying an application in a container with its own working condition. These two concepts, virtualization and containerization are competing in the cloud-based environments. When virtualization became the mainstream, VM security concerns was common. IT Security experts are discussing the potential weaknesses of a virtualized environment for a long time. In this paper, focusing on Docker container, its vulnerabilities and possible measurements against security concerns, we have provided information about assessment of risks and vulnerabilities of containerization and the main differences between these two concepts via vulnerability analysis.
Fuzzy Digital Filter Device Study for the Robot Manipulator

Рurpose of research . Situations that reduce the accuracy of links positioning often arise due to the drab of contacts during the operation of robot manipulator. One of this problem solutions is signal filtering. Modern digital filtering algorithms have a rather complex mathematical structure. Data processing in such filters takes long time. So the purpose of this work is to develop fast-acting fuzzy digital filter device capable to eliminate the problem of contact scrapping when positioning the links of robot manipulator. Methods . Generalized mathematical model was used in the development of digital filter device. This model is implemented by using fuzzy logic theory. Generalized mathematical model of the fuzzy digital filter includes a model determining a support of fuzzy output variable, a model for determining coefficients of fuzzy digital filter and a model for converting the output voltage into a rotation angle of robot manipulator servo drive. This mathematical model makes it possible to recalculate the voltage supplied to the input of fuzzy digital filter using two control coefficients thereby improving the positioning accuracy of robot manipulator links. Results . During experimental studies, comparative analysis of developed fast-acting device of fuzzy digital filter with Kalman filter was carried out. The RMSE standard error for two filters was calculated. The average RMSE value of the fuzzy digital filter was 0.0185 of Kallman's filter was 0.0193. Conclusion . Generalized mathematical model of fuzzy digital filter device consisting of three models is described. Device implementing determination of caliper of fuzzy output variable, experimental model of robot manipulator, experimental research and proposed mathematical model are presented.
On the 3D Track Planning for Electric Power Inspection Based on the Improved Ant Colony Optimization and <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="M1"><mml:msup><mml:mrow><mml:mi>A</mml:mi></mml:mrow><mml:mrow><mml:mi>∗</mml:mi></mml:mrow></mml:msup></mml:math> Algorithm

At present, multirotor drones are restricted by the control accuracy and cannot position accurately according to the accuracy of point cloud data. Also, track planning in three-dimensional space is much more complicated than that in two-dimensional space, which means that existing track planning methods cannot achieve fast planning. Meanwhile, most existing researches were implemented in quasi-three-dimensional space with the shortest route length as the objective function and omitted environmental impacts. To overcome these, this paper uses the grid method to segment point cloud data of the flying space via ArcGIS software according to the drone’s controlling accuracy. It also extracts the grid coordinate information and maps it to a three-dimensional matrix to build the model accurately. This paper sets the minimal energy consumption as the objective function and builds a track planning model based on the drone’s performance and natural wind constraints. The improved ant colony optimization and <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="M2"><mml:msup><mml:mrow><mml:mi>A</mml:mi></mml:mrow><mml:mrow><mml:mi>∗</mml:mi></mml:mrow></mml:msup></mml:math> (ACO-<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="M3"><mml:msup><mml:mrow><mml:mi>A</mml:mi></mml:mrow><mml:mrow><mml:mi>∗</mml:mi></mml:mrow></mml:msup></mml:math>) algorithm are utilized to design this algorithm for a faster solution. That is, we use the improved ant colony optimization to quickly find a near-optimal track covering all viewpoints with the minimal energy consumption. The improved <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="M4"><mml:msup><mml:mrow><mml:mi>A</mml:mi></mml:mrow><mml:mrow><mml:mi>∗</mml:mi></mml:mrow></mml:msup></mml:math> algorithm will be used for local planning for adjacent tracks passing through obstacles. In the designed simulation environment, the simulation results show that, to ensure that the same components are shot, the improved algorithm in this paper can save 62.88% energy compared to that of the Shooting Manual of Drone Inspection Images for Overhead Transmission Lines. Also, it can save 9.33% energy compared to a track with the shortest route length. Besides, the ACO-<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="M5"><mml:msup><mml:mrow><mml:mi>A</mml:mi></mml:mrow><mml:mrow><mml:mi>∗</mml:mi></mml:mrow></mml:msup></mml:math> algorithm saves 96.6% time than the <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="M6"><mml:msup><mml:mrow><mml:mi>A</mml:mi></mml:mrow><mml:mrow><mml:mi>∗</mml:mi></mml:mrow></mml:msup></mml:math> algorithm.
Energy Efficient Load Balancing Aware Task Scheduling in Cloud Computing using MultiObjective Chaotic Darwinian Chicken Swarm Optimization

Scheduling of tasks in a cloud environment has larger influence on time and energy depletion.Different heuristic models were developed to solve the NP-hard task scheduling problem based on time.However, ideal task scheduling algorithms must also maximize energy efficiency with good load balancing and ensure better Quality-of-Service (QoS).An innovative multi-objective Chaotic Darwinian Chicken Swarm Optimization (CDCSO) system is suggested in this article to provide energy efficient QoS and load balancing aware task scheduling.The multi-objective CDCSO algorithm incorporates the chaotic and Darwinian Theory to the standard Chicken Swarm Optimization to increase its global exploration and maximize the convergence rate.This performance enhanced CDCSO algorithm models the cloud task scheduling problem as NP-hard and utilizes the optimization principles to solve them based on multiple objective parameters.The multi-objective fitness function used in CDCSO is modelled based on the objective parameters namely energy, cost, task completion time, response time, throughput and load balancing index.Based on this multi-objective function, the CDCSO effectively allocates the tasks to the suitable energy efficient, cost and time minimized Virtual machines (VMs) which are also optimally load balanced.CloudSim simulations were conducted and the obtained results illustrated that the proposed multi-objective CDCSO has provided better task scheduling with minimized energy, cost, time and optimal load balancing.