Reviewerly/exHarmony huggingface dataset:
Analisa Penggunaan Mesin X-Ray Sebagai Security System Di Bandara Internasional Husein Sastranegara

Security system is a protection has a structured cycle, meaning that it has been systemized according to its application in the environment according to its field. Security system is formed is because of threats, crimes and others. A security system is needed at certain events and in special places such as malls and airports to maintain the continuity of the event and security can be well maintained. Everyone who comes to the location must have their luggage checked to minimize the presence of sharp objects that endanger or threaten security at the location. Inspection of passenger objects at the airport is one way to protect the safety and security of airplane passengers. The procedure for checking objects carried by passengers is still carried out manually by airport security officers. The goal is to check whether you find sharp objects or illegal drugs in the passenger's bag. The results of this study are the authors know the working principle and operation of x-ray machines, as well as the number and types of x-ray machines used by Husein Sastranegara International Airport. Security system is a protection has a structured cycle, meaning that it has been systemized according to its application in the environment according to its field.Security system is formed is because of threats, crimes and others. A security system is needed at certain events and in special places such as malls and airports to maintain the continuity of the event and security can be well maintained. Everyone who comes to the location must have their luggage checked to minimize the presence of sharp objects that endanger or threaten security at the location. Inspection of passenger objects at the airport is one way to protect the safety and security of airplane passengers. The procedure for checking objects carried by passengers is still carried out manually by airport security officers. The goal is to check whether you find sharp objects or illegal drugs in the passenger's bag. The results of this study are the authors know the working principle and operation of x-ray machines, as well as the number and types of x-ray machines used by Husein Sastranegara International Airport.
The Second Version of The Pskov Maid in the Context of Nikolai Rimsky-Korsakov’s Opera Legacy

The article is devoted to the second version of Nikolai Rimsky-Korsakov’s opera “The Pskov Maiden” (1876–1877). This version was written by the composer during the time of his independent studies and beginning of work at the Conservatory and has become the most significant composition of this period. Nonetheless, it became virtually unnoticed in music history – there was no production at the Mariinsky Theater, despite the composer’s wishes, neither the piano-vocal score, nor the full score was published, and as the result this version was preserved only in the form of segmental manuscripts. We have undertaken a textological research of the preserved fragments, for the first time having provided a scholarly generalization of their entire core, and having made a list of all the available materials (according to the sources from the Manuscript Section of the Russian National Library), and the attempt has been made to compare and juxtapose them with the plan of the opera contained in one of the manuscripts, and to present Rimsky-Korsakov’s initial conception most completely. A comparative analysis of all three editions of the opera is made, particularly, their scenario plans – detailed research is made of the differences of dramaturgy and composition of all the opera’s versions; conclusions are made about the integrality and artistic value of the second version. In addition to this, the article examines the reception of the second version, as well as the attitudes towards the entire work, as well as to separate episodes from it, in particular, of the composer himself, his associates from the “Mighty Handful,” the composer’s wife Nadezhda Nikolayevna and the greatest researcher of his works Vassily Yastrebtsev, and subsequently – of the researchers engaged in studying this version of the opera. In conclusion, the hypothesis is brought out, according to which the negative attitude towards the second version of the “Pskov Maid” has gained a foothold in musicology and in publication practice for the most part as the result of the composer’s own opinion about his own composition, without any detailed critical analysis of the musical text. In our opinion, such a position may be reevaluated, after the score is assembled from the existing fragments, which will make it possible to evaluate its position in Rimsky-Korsakov’s musical legacy in a more precise or objective manner. Keywords: Nikolai Rimsky-Korsakov, opera “The Pskov Maid,” second version, textology.
How to Improve Students’ Cognitive Ability?: The Influence of Two Stay Two Stray Type Cooperative Model Assisted by Innovative Module

This study aims to see the effect of using the two stay two stray learning model assisted by an innovative module on the cognitive learning outcomes of students on the topic of simple machines. This quantitative study used a quasi-experimental design with a pretest-posttest control group design. This study consisted of 2 classes, class VIII A as the experimental class and class VIII B as the control class. The results showed that the average of the cognitive test of the experimental class was 80.00 and the control class was 72.67. The results of data processing using the Mann Whitney test with a sig level of 0.05 revealed that the results of sig.2 tailed were 0.000, less than 0.05, which means that there is a difference in the posttest average value of students’ cognitive learning outcomes in the control and experimental classes.
Development and Evaluation of an Augmented Reality Microscope for Science Learning: A Design-Based Research

This study aims to develop and evaluate an augmented reality microscope, “MicrosAR”, for a middle school Science course, which was aimed for use both in and out of school, and to understand the users’ perceptions about it. The study adopted design-based research to iteratively develop and evaluate the MicrosAR. Learning activities and working handouts in the application were grounded upon inquiry-based learning. The initial prototype was evaluated with 99 middle school students, as well as 18 preservice and six experienced in-service science teachers. The second prototype was then evaluated with 96 different middle school students. Accordingly, design changes were applied to the second prototype to present the final product development. Participants’ experiences and perceptions were gathered through a self-developed, paper-based instrument after they practiced with the MicrosAR. The findings indicated that the MicrosAR was favored by and recognized as an effective and useful tool by the participants. The study highlighted the benefits that augmented reality technology and such an application can offer for learning purposes, and that it can be practiced at any place to deliver a “real” learning experience over virtual platforms, thereby saving costs, enhancing its availability, and improved learner interest.
Statistical Tests to Identify Virtual Concept Drifts

In streaming environments, concept drift is a common problem and identifying whether it is occurring is of utmost importance. Most of the published drift detection methods work based on the results of a base classifier, for example, by using the classification error or the distance between two consecutive errors. But if a change occurs only in the attributes space without changing the boundaries inferred by the learner, drift detection methods may not able to correctly work. This paper proposes VDDM, a drift detection method specially able to identify virtual concept drifts. It works by using a multivariate nonparametric statistical test to identify changes in a window of the most recent instances. Experimental results indicate that the usage of a multivariate nonparametric statistical test presents competitive results specially in the number of detected changes, distances to the drift point, sensitivity and specificity scores, as well as the Matthews Correlation Coefficient and the F1 score.
A Penalized Matrix Normal Mixture Model for Clustering Matrix Data

Along with advances in technology, matrix data, such as medical/industrial images, have emerged in many practical fields. These data usually have high dimensions and are not easy to cluster due to their intrinsic correlated structure among rows and columns. Most approaches convert matrix data to multi dimensional vectors and apply conventional clustering methods to them, and thus, suffer from an extreme high-dimensionality problem as well as a lack of interpretability of the correlated structure among row/column variables. Recently, a regularized model was proposed for clustering matrix-valued data by imposing a sparsity structure for the mean signal of each cluster. We extend their approach by regularizing further on the covariance to cope better with the curse of dimensionality for large size images. A penalized matrix normal mixture model with lasso-type penalty terms in both mean and covariance matrices is proposed, and then an expectation maximization algorithm is developed to estimate the parameters. The proposed method has the competence of both parsimonious modeling and reflecting the proper conditional correlation structure. The estimators are consistent, and their limiting distributions are derived. We applied the proposed method to simulated data as well as real datasets and measured its clustering performance with the clustering accuracy (ACC) and the adjusted rand index (ARI). The experiment results show that the proposed method performed better with higher ACC and ARI than those of conventional methods.
The Use of Indirect Corrective Feedback to Eliminate Grammatical Errors in Students' Writing

This study aimed to find out the effect of using indirect corrective feedback to eliminate grammatical errors in students' writing. This research was conducted at SMKN 1 Padang Panjang because most of students still encounter problems in mastering writing text especially grammatical aspect. Most of the students' writing was founded many of grammatical error. Then, the students still did not get the appropriate feedback of the teacher toward their errors. And, the teacher also did not give the spesific feedback or treatment to help the students in reducing the error. This research used pre-experiment research. The researcher used one-group pretest posttest design. To determine sample, the researcher used purposive sampling technique. The data got from the pre-test and post-test in experiment class. To analyze the data, the resercher used the normality test and homogeneity test by using SPSS Statistics20 and to test the hypothesis the researcher used t-test formula and consulted the result into t-table with level of significant a=0.05. From the result and accounting the post-test of experiment class,it was founded that using indirect corrective feedback in eliminate grammatical error had better result than without indirect coreective feedback. It can be seen by comparing the t-obtained (2) that higher than the t-table (1.706).
Minibatch Recursive Least Squares Q-Learning

The deep Q-network (DQN) is one of the most successful reinforcement learning algorithms, but it has some drawbacks such as slow convergence and instability. In contrast, the traditional reinforcement learning algorithms with linear function approximation usually have faster convergence and better stability, although they easily suffer from the curse of dimensionality. In recent years, many improvements to DQN have been made, but they seldom make use of the advantage of traditional algorithms to improve DQN. In this paper, we propose a novel Q-learning algorithm with linear function approximation, called the minibatch recursive least squares Q-learning (MRLS-Q). Different from the traditional Q-learning algorithm with linear function approximation, the learning mechanism and model structure of MRLS-Q are more similar to those of DQNs with only one input layer and one linear output layer. It uses the experience replay and the minibatch training mode and uses the agent’s states rather than the agent’s state-action pairs as the inputs. As a result, it can be used alone for low-dimensional problems and can be seamlessly integrated into DQN as the last layer for high-dimensional problems as well. In addition, MRLS-Q uses our proposed average RLS optimization technique, so that it can achieve better convergence performance whether it is used alone or integrated with DQN. At the end of this paper, we demonstrate the effectiveness of MRLS-Q on the CartPole problem and four Atari games and investigate the influences of its hyperparameters experimentally.
HIGHER EDUCATION STUDENTS’ EXPERIENCE IN A DIGITAL EDUCATIONAL MODALITY

In 2020, universities worldwide migrated abruptly from face-to-face education to an emergency remote distance format to respond to the SARS-CoV-2 pandemic. The present study aimed to assess this type of learning experience from higher education students' perspectives. A mixed-method design was conducted with 796 students from six different disciplinary schools. The questionnaire included five constructs (Pedagogical, Collaboration, Learning Resources, Time Management, and Functional Features) with 17 quantitative items rated on a 1-5 Likert scale (Cronbach's Alpha = 0.955). Answers from two open questions were coded into two categories: Positive Experiences and Aspects to Improve. The highest constructs were Functional Features (3.60) and Time Management (3.15). The lowest score was Collaboration (2.77). Students appreciated the digital experience using Zoom and Canvas and their flexibility with time and space in their selfmanagement. They recommended improving the interactions among peers and teachers with shorter and more interactive synchronous sessions. Even though there was only one week to prepare for the change from face-to-face to emergency remote distance learning, the students successfully continued their education. An updated distance teaching method was improved and implemented after reviewing the results of the present study. Since August 2020, more than 50 thousand higher education students have been taught via this enhanced digital educational modality. Keywords: Educational Innovation, Higher Education, digital education, flexible education, Zoom, Canvas, SARS-CoV-2
Blind Image Quality Assessment Using a CNN and Edge Distortion

The present paper introduces a Convolutional Neural Network (CNN) for the assessment of image quality without a reference image, which comes under the category of Blind Image Quality Assessment models. Edge distortions in the image are characterized as input feature vectors. This approach is in justification of the fact that subjective assessment focusses on image features that emanate from the edges and the boundaries present in the image. The earlier methods were found to use complex transformations on the image to extract the features before training or as a part of the training. The present work uses Prewitt kernel approach to extract the horizontal and vertical edge maps of the training images. These maps are then input to a simple CNN for extracting higher level features using non-linear transformations. The resultant features are mapped to image quality score by regression. The network uses Spatial Pyramid Pooling (SPP) layer to accommodate input images of varying sizes. The present proposed model was tested on popular datasets used in the domain of Image Quality Assessment (IQA). The experimental results have shown that the model competes with the earlier proposed models with simplicity of feature extraction and involvement of minimal complexity.
Multidimensional linear cryptanalysis with key difference invariant bias for block ciphers

Abstract For block ciphers, Bogdanov et al. found that there are some linear approximations satisfying that their biases are deterministically invariant under key difference. This property is called key difference invariant bias. Based on this property, Bogdanov et al. proposed a related-key statistical distinguisher and turned it into key-recovery attacks on LBlock and TWINE-128. In this paper, we propose a new related-key model by combining multidimensional linear cryptanalysis with key difference invariant bias. The main theoretical advantage is that our new model does not depend on statistical independence of linear approximations. We demonstrate our cryptanalysis technique by performing key recovery attacks on LBlock and TWINE-128. By using the relations of the involved round keys to reduce the number of guessed subkey bits. Moreover, the partial-compression technique is used to reduce the time complexity. We can recover the master key of LBlock up to 25 rounds with about 2 60.4 distinct known plaintexts, 2 78.85 time complexity and 2 61 bytes of memory requirements. Our attack can recover the master key of TWINE-128 up to 28 rounds with about 2 61.5 distinct known plaintexts, 2 126.15 time complexity and 2 61 bytes of memory requirements. The results are the currently best ones on cryptanalysis of LBlock and TWINE-128.
Learning Frequency-Aware Dynamic Network for Efficient Super-Resolution

Deep learning based methods, especially convolutional neural networks (CNNs) have been successfully applied in the field of single image super-resolution (SISR). To obtain better fidelity and visual quality, most of existing networks are of heavy design with massive computation. However, the computation resources of modern mobile devices are limited, which cannot easily support the expensive cost. To this end, this paper explores a novel frequency-aware dynamic network for dividing the input into multiple parts according to its coefficients in the discrete cosine transform (DCT) domain. In practice, the high-frequency part will be processed using expensive operations and the lower-frequency part is assigned with cheap operations to relieve the computation burden. Since pixels or image patches belong to low-frequency areas contain relatively few textural details, this dynamic network will not affect the quality of resulting super-resolution images. In addition, we embed predictors into the proposed dynamic network to end-to-end fine-tune the handcrafted frequency-aware masks. Extensive experiments conducted on benchmark SISR models and datasets show that the frequency-aware dynamic network can be employed for various SISR neural architectures to obtain the better tradeoff between visual quality and computational complexity. For instance, we can reduce the FLOPs of SR models by approximate 50% while preserving state-of-the-art SISR performance.
Semi-Supervised Domain Adaptation for Holistic Counting under Label Gap

This paper proposes a novel approach for semi-supervised domain adaptation for holistic regression tasks, where a DNN predicts a continuous value y∈R given an input image x. The current literature generally lacks specific domain adaptation approaches for this task, as most of them mostly focus on classification. In the context of holistic regression, most of the real-world datasets not only exhibit a covariate (or domain) shift, but also a label gap-the target dataset may contain labels not included in the source dataset (and vice versa). We propose an approach tackling both covariate and label gap in a unified training framework. Specifically, a Generative Adversarial Network (GAN) is used to reduce covariate shift, and label gap is mitigated via label normalisation. To avoid overfitting, we propose a stopping criterion that simultaneously takes advantage of the Maximum Mean Discrepancy and the GAN Global Optimality condition. To restore the original label range-that was previously normalised-a handful of annotated images from the target domain are used. Our experimental results, run on 3 different datasets, demonstrate that our approach drastically outperforms the state-of-the-art across the board. Specifically, for the cell counting problem, the mean squared error (MSE) is reduced from 759 to 5.62; in the case of the pedestrian dataset, our approach lowered the MSE from 131 to 1.47. For the last experimental setup, we borrowed a task from plant biology, i.e., counting the number of leaves in a plant, and we ran two series of experiments, showing the MSE is reduced from 2.36 to 0.88 (intra-species), and from 1.48 to 0.6 (inter-species).
Virtual Machine Customization Using Resource Using Prediction for Efficient Utilization of Resources in IaaS Public Clouds

The main cause of energy wastage in cloud data centres is the low level of server utilization. Low server utilization is a consequence of allocating more resources than required for running applications. For instance, in Infrastructure as a Service (IaaS) public clouds, cloud service providers (CSPs) deliver computing resources in the form of virtual machines (VMs) templates, which the cloud users have to choose from. More often, inexperienced cloud users tend to choose bigger VMs than their application requirements. To address the problem of inefficient resources utilization, the existing approaches focus on VM allocation and migration, which only leads to physical machine (PM) level optimization. Other approaches use horizontal auto-scaling, which is not a visible solution in the case of IaaS public cloud. In this paper, we propose an approach of customizing user VMâ€™s size to match the resources requirements of their application workloads based on an analysis of real backend traces collected from a VM in a production data centre. In this approach, a VM is given fixed size resources that match applications workload demands and any demand that exceeds the fixed resource allocation is predicted and handled through vertical VM auto-scaling. In this approach, energy consumption by PMs is reduced through efficient resource utilization. Experimental results obtained from a simulation on CloudSim Plus using GWA-T-13 Materna real backend traces shows that data center energy consumption can be reduced via efficient resource utilization
Using Calculus to Plan an Open-Air Concert

A concert is a mass entertainment event held indoors, at concert halls, or outdoors (open-air festivals). These two formats differ greatly from each other. However, the goal of both events remains the same – to allow the audience to enjoy the musical performance. Indoor halls are designed for the best acoustics of sound. They are often circular and let sound waves travel around the inside of the building, like an echo bouncing back and forth. This makes the audience feel like they are surrounded by sound. Such places already have outlined spots for mounting music equipment with the highest efficiency. Thus, thanks to the fact that the building is engineered exactly for the purpose of conserving the sound, concert holders are not obligated with extra calculations and planning. On the other hand, open-air concerts can range from small, acoustic gatherings at municipal parks, to large music festivals that take place over several days and feature a large number of different artists. Such concerts take great amounts of planning to make sure that the stage is visible and, more importantly, audible from the entire seating area. In this paper, we investigate the physics of sound events of both formats and derive a basic formula for calculating the coverage of a sound system.