Reviewerly/exHarmony huggingface dataset:
Promoting Oral-Communication Skill to the students of Seventh Grade on Earth Science Content Using Multimedia Based Integrated Instruction (MBI<sub>2</sub>)

Student should improve their communication skills whereas they are in school and college to enhance their adaption for the 21-century skills requirement. The aim of this study is promoting students' oral-communication skills through Multimedia Based Integrated Instruction (MBI2) on earth-science content. The research method that is utilized in this research is embedded mix methods toward 35 seventh grade students that consist of 17 males and 18 females. Oral-communication skill rubric which has been developed is used to assess students' oral-communication skill. The aspects that assessed are eye contact, intonation of speech, content, enthusiasm, and mannerisms & body language. We assessed each aspect using the rubric and then interpret it into categories which are need an improvement, fair, good and excellent. The result shows that the average score of five oral-communication skill aspects in every meeting are 8.40 (earth structure sub-content) that is categorized as Fair, 13.75 (earthquake sub-content) that categorized as Good, 14.83 (tsunami sub-content) that is categorized as Good and 15.67 (volcano sub-content) that is categorized as Good. In conclusion, MBI2 can promote students' oral-communication skill of seventh grade on earth science content.
Parallel edge-based sampling for static and dynamic graphs

Graph sampling is an important tool to obtain small and manageable subgraphs from large real-world graphs. Prior research has shown that Induced Edge Sampling (IES) outperforms other sampling methods in terms of the quality of subgraph obtained. Even though fast sampling is crucial for several workflows, there has been little work on parallel sampling algorithms in the past.
Intelligent clustering analysis model for mining area mineral resource prediction

Based on the cluster analysis technique, the prediction technology of mineral resources in the mining area is studied and the collected geochemical data are sorted out. Thirty-nine chemical elements of the data are clustered and six relatively stable regions are obtained. Correlation analysis of t he geochemical data in four regions is carried out. Through the correlation between the elements and the characteristic elements of each deposit, predicting the possible resources and types in each region, a line chart of the content of 39 elements at different sampling points in each region is drawn. The results of the preliminary prediction and the results of the relevant analysis are combined to obtain the possible resources and resource types of the four regions. Finally, by combining the occurrence environment of each deposit with the geological data of each area, the types of mineral resources and resources in the area are further determined more accurately.
Performance Evaluation of Enhanced RC6 Permutation-Diffusion Operation in Securing Images

Several modifications and analysis on encryption performance and security were studied and evaluated by several researchers to measure the efficiency and quality of the proposed enhancement in the encryption method. In this paper, the authors evaluated the performance of the image encryption using the enhanced RC6 permutation-diffusion operation integrating the concept of cyclic-shift permutation. Statistical analysis, key space analysis, and runtime execution analysis were conducted based on considered metrics such as key sensitiveness, histogram and maximum deviation, mean squared error, mean value analysis and the speed measurement of encryption and decryption process. Based on the numerical and visual results, the good qualities of encryption using this method were achieved.
Haze removal method based on a variation function and colour attenuation prior for UAV remote-sensing images

Aiming at the problem of low visibility of images obtained by UAV in hazy weather, this paper proposes an image dehazing algorithm based on variation function and colour attention prior. A large number of experiments have proved that the sky or other bright regions could affect the estimation of atmospheric light and transmittance. In the experiment, our proposed algorithm divides the images into sky and dark regions and uses the pixels of the dark region to solve the atmospheric light value. According to the region where the pixels are located, the transmittances of the pixels in the sky and non-sky regions are separately estimated and adjusted. The experiment’s results show that the restored image visibility, information entropy and colour saturation are significantly improved, and the algorithm’s computational efficiency is high.
A classifier for multi-dimensional datasets based on Bayesian multiple kernel grouping learning

This paper proposes an algorithm for the classification of multi-dimensional datasets based on the conjugate Bayesian Multiple Kernel Grouping Learning (BMKGL). Using conjugate Bayesian framework improves the computation efficiency. Multiple kernels instead of a single kernel avoid the kernel selection problem which is also a computationally expensive work. Through grouping parameter learning, BMKGL can simultaneously integrate information from different dimensions and find the dimensions which contribute more to the variations of the outcome for the purpose of interpretable property. Meanwhile, BMKGL can select the most suitable combination of kernels for different dimensions so as to extract the most appropriate measure for each dimension and improve the accuracy of classification results. The simulation results illustrate that our learning process has better performance in prediction results and stability compared to some popular classifiers, such as k-nearest neighbours algorithm, support vector machine algorithm and naive Bayes classifier. BMKGL also outperforms previous methods in terms of accuracy and interpretation for the heart disease and EEG datasets.
Datacentre TCP Protocol of Centralized Window Control

There is an important class of applications in data centre called online dataintensive (OLDI) application, such as search engine, electronic shopping, and advertising, which has low latency, high throughput, and high burstiness. For better user experience, the query response delay in the data centre is controlled under 300 milliseconds. DCTCP can better meet the requirements of OLDI, but all the switches in the data centre are required to support RED. For data centre that does not fully support RED switch, TCP protocol can only be used. TCP Incast will appear in the application, which is far from the requirements of OLDI application. This paper proposes the TCP protocol of data centre based on window control. It is in existing switches and network devices,and centralized control makes the send window match with the switch queue cache. In the case of ensuring low latency, it keeps the switch queue in a small range of fluctuation, enabling the data centre to meet low latency, high throughput and high burstiness application requirements. Simulation experiments verify that WCTCP congestion control method can effectively solve the above problems. It is 5 times lower than TCPNewReno packet loss rate, 6 times higher than the network utilization rate, and the network performance is close to the DCTCP protocol.
Explaining Actual Causation in Terms of Possible Causal Processes

We point to several kinds of knowledge that play an important role in controversial examples of actual causation. One is knowledge about the causal mechanisms in the domain and the causal processes that result from them. Another is knowledge of what conditions trigger such mechanisms and what conditions can make them fail. We argue that to solve questions of actual causation, such knowledge needs to be made explicit. To this end, we develop a new language in the family of CP-logic, in which causal mechanisms and causal processes are formal objects. We then build a framework for actual causation in which various “production” notions of actual causation are defined. Contrary to counterfactual definitions, these notions are defined directly in terms of the (formal) causal process that causes the possible world.
Determination of energy levels, probabilities, and expectation values of particles in the three-dimensional box at quantum numbers ≤5

Static potential boxes represent an area that is limited by an infinite potential wall, where there is no external potential in it. The solution of the Schrodinger equation gives a wave function that can explain the energy level and determine the probability of finding a particle at any point. The main focus of the research in this paperwas to solve the solution to the energy levels, probabilities, and expectation values of a particle in a three-dimensional box, where control limits are given for the potential box width, namely and L for each situation. In this research, the results showed that probability meetings depend on changes in box width and quantum number of particles, while particle energy levels depend on the length of the box and the quantum number of particles. Based on the relation between probabilities and expectation values, the most stable size of the box to find the particles in ground state to the fourth excitation state is at the width of and L.
Design of Ontology-based Question Answering System for Incompleted Sentence Problem

Ontology-based Question Answering System (QAS) has advantage on representing semantic knowledge concept of an object, property, and relation between objects on a particular domain. Ontology-based QAS requires a proper definition of objects, properties, and their relationships in order to work correctly. It is very challenging topic for Bahasa Indonesia, especially when dealing with incomplete question sentences. For example: the sentence with no question word or no question object, or no obscure adverb question. This study designed an Ontology-based QAS that capable for dealing incomplete question sentence. Two approach method has been done to deal with the problem: First, designed an ontology that used a keyword property to find the question objective; Second, designed an architecture that transform the question in natural language into instruction that can be understood by computer. Experiment results shows that the ontology design plays an important role for Ontology-based QAS as well as system design.
The Importance of Safe Coding Practices and Possible Impacts on the Lack of Their Application

A significant change in the way organizations and companies use software has been brought by the evolution of the Internet over the time, resulting in an increasing presence of Information Systems in Web environments and, consequently, an increase in security vulnerabilities and threats. In this context, secure application development has become a crucial component for information systems in the market. This paper aims to contribute to a greater awareness of the importance of secure software coding by exposing possible impacts examples resulting from the lack of safe coding practices, as well as to present some existing recommendations from specialized agencies to avoid or treat occurrences of exploitation of vulnerabilities by malicious agents.
Registration of works as a tool to protect copyright in cross-border relations (Internet)

The paper analyzes copyright registration systems in the Russian Federation and abroad. The fact of creation of a work gives rise to the author a number of rights and obligations without performing any formalities, including registration of the work. There are three systems in which registration of a work creates for the copyright holder additional protection of the rights of the author, does not create negative consequences for non-compliance with the registration procedure or is provided by law only for specific copyright objects. Deposit is considered as an element of the procedure for registration of works, including as an indirect confirmation of the fact of the creation of a digital work for the lex loci originis collision binding states (the law of the state of origin of the work), and for the lex loci protectionis targeting states (the law of the state where protection is claimed), the deposit does not confirm the fact of the creation of the work, but helps to preserve the copyright object.
Rendering differential performance preference through intelligent network edge in cloud data centers

Summary Sharing the network infrastructure, the performance of emerging distributed applications and services in data centers is directly impacted by the network. As these applications are becoming more and more demanding, it is challenging to satisfy their requirements of low latency, high throughput, and low packet loss rate simultaneously. Prior approaches typically resort to flow control or scheduling mechanisms, prioritizing flows according to their demands. However, none of the methods can solely satisfy the various demands of data center applications. Addressing this challenge, we propose tasch , a preference aware flow scheduling mechanism equipped in the software network edge (ie, end‐host networking). This mechanism utilizes multiple separate queues for flows with different preferences, which guarantees low packet delay for latency‐sensitive flows and provides bandwidth guarantees for throughput‐sensitive flows. A coordinating algorithm is presented to share the network resource among multiple queues with pareto‐optimality. tasch is implemented as a thin and plugable kernel module in Linux based hypervisors, which lies between the complicated physical network and tenants VMs. Subsequently, based on the flow traces of real‐world applications, extensive experiments were conducted to verify the effectiveness of network management mechanism.
Implementation and Performance Analysis of Private Cloud Using Openstack Swift and Rclone

Many companies need a storage system that can be accessed in real time by all parts of the company. Most digital data storage methods today still use conventional methods where data is stored on an external hard disk or public cloud. Storage with external hard disk media makes accessing data difficult and has the risk of data loss when storage media is damaged. On the other hand, the storage method using public cloud requires an internet connection with large bandwidth requirements and the company still has to spend a budget on renting it. This study aims to design digital data storage methods using a private cloud that can be accessed in real time by all parts of the company without having to spend a budget on hiring a storage media and renting an internet connection with large bandwidth requirements. A private cloud was built using OpenStack Swift as an object storage service provider and Rclone as a cross-platform computer data management application. The results of this study are the creation of a private cloud that runs object storage services using Swift storage objects with relatively light and high scalability to meet the needs of storing data effectively and efficiently. A private cloud-based data storage media with relatively light and high scalability to meet data storage needs. Data storage media that can be accessed easily without having to use an internet connection with large bandwidth requirements.
On-demand track-to-track fusion using local IMM inside information

The fusion of state estimates from Interacting Multiple Model (IMM) estimators using inside information (mixture estimates and probabilities) is described in this paper. Fusion is performed on-demand, i.e., without conditioning on past track information. The local trackers run IMM estimators to track a target and transmit mode-conditioned estimates and mode probabilities to a Fusion Center. The fused state posterior probability density is a Gaussian mixture whose parameters can be computed recursively. The likelihood functions of the state and mode are derived, yielding consistent data fusion. Simulations show that this method outperforms the fusion of the local IMM estimator outputs both in terms of error during target maneuvers and in the consistency of the mean-squared error.
Sparse discriminant analysis based on estimation of posterior probabilities

Fisher's linear discriminant analysis (FLDA) is known as a method to find a discriminative feature space for multi-class classification. As a theory of extending FLDA to an ultimate nonlinear form, optimal nonlinear discriminant analysis (ONDA) has been proposed. ONDA indicates that the best theoretical nonlinear map for maximizing the Fisher's discriminant criterion is formulated by using the Bayesian a posterior probabilities. In addition, the theory proves that FLDA is equivalent to ONDA when the Bayesian a posterior probabilities are approximated by linear regression (LR). Due to some limitations of the linear model, there is room to modify FLDA by using stronger approximation/estimation methods. For the purpose of probability estimation, multi-nominal logistic regression (MLR) is more suitable than LR. Along this line, in this paper, we develop a nonlinear discriminant analysis (NDA) in which the posterior probabilities in ONDA are estimated by MLR. In addition, in this paper, we develop a way to introduce sparseness into discriminant analysis. By applying L1 or L2 regularization to LR or MLR, we can incorporate sparseness in FLDA and our NDA to increase generalization performance. The performance of these methods is evaluated by benchmark experiments using last_exam17 standard datasets and a face classification experiment.
Efficient methods to set decay factor of time decay model over data streams

Time decay model (TDM) is frequently used for mining frequent patterns on data streams, because the information embedded in the data from the new transactions is particularly valuable. However, some existing methods on designing decay factor of TDM are random, so their results are unsteady. Some o ther methods focus on only 100% recall or 100% precision of algorithm, but the corresponding high precision or high recall is ignored. In order to balance high recall and high precision of algorithm, meanwhile, ensure the stability of the result, a novel average decay factor is designed. In addition, to further increase the weights of the latest transactions and reduce the weights of historical transactions, another novel Gaussian decay factor is proposed. Hence, based on an analysis of existing decay factors, this paper aims to design two novel decay factors and two novel TDMs. Algorithms based on these two TDMs are designed to discover frequent patterns over data streams. The methods of mining frequent patterns on high density or low density data streams are evaluated via experiments. This paper’s research findings show that the application of average time decay factor can balance the high recall and high precision of algorithm. And Gaussian decay factor can produce better performance than existing algorithms.
Model of an Intellectual Information System for Recognizing Users of a Social Network Using Bioinspired Methods

The analysis of social networks is one of the actively developing areas of research for today. In addition to methods of modeling social relations, the interest for researchers is also the methods of obtaining initial data. The analysis carried out by the authors showed that, as initial data, analysts use profile information, various types of activities. Consideration of photo albums and video files as a source of data on users of social networks was not considered. At the same time, by identifying the user’s face and further searching it in photographs and video files in other profiles, an expansion of the list of basic data available to the analyst is achieved. The problems associated with the recognition and identification of the person in the photo and video are well known and not fully resolved. The greatest difficulty here is the problem of scene variation, since the angle and location of the faces in the photo and video cannot be known in advance. The article presents the results of solving the problem of scene variation, presented in the form of a model of an information system using bionspirited methods. This system and the method incorporated in it are intended for preliminary image processing in order to broaden the range of permissible scene variations.
Coordinate-guided U-Net for automated breast segmentation on MRI images

Magnetic resonance imaging (MRI) plays an important role in breast cancer detection and diagnosis. Breast region segmentation on MRI images is an essential step for many analysis tasks such as the assessment of background parenchymal enhancement (BPE), the analysis of non-mass enhancement (NME) and investigation of the tumor characteristics. However, automated breast segmentation remains a challenge because the noisy background and the blurred boundary between breast and pectoral muscle on MRI images. In this study, we developed a coordinate-guided U-Net (CU-Net) to identify the boundary of the breast on MRI images. The CU-Net applied the breast position information to the segmentation of breast by adding the X coordinate channel and Y coordinate channel to the input image. A dataset of 80 3D bilateral breast MRI scans including 64 cases in training/validation set and 16 cases in test set were collected. All the input images were resized to 256×256 pixels and the intensity are cropped and rescaled according to the percentile of cumulative histogram to normalize the image. To add X and Y coordinate channels, the middle point of the breast-air boundary was detected as the origin of coordinates. The position of each pixel in the coordinates was then encoded into the input of the network. The CU-Net was evaluated with three measures: the dice coefficient (Dice), the intersection over union (IoU) and the hausdorff distance (Hdist). The results of CU-Net were compared to those obtained with U-Net method. In the test set including 16 cases, the mean Dice, the mean IoU, and the mean Hdist of CUNet were 94.1&plusmn;1.1%, 92.5&plusmn;1.3%, and 6.17&plusmn;1.4 mm, while the corresponding measures by U-Net were 91.4&plusmn;2.4%, 89.7&plusmn;1.7%, and 6.98&plusmn;1.5 mm, respectively. The results showed that our CU-Net was able to segment breast on MRI images reliably and was more accurate comparing to the U-Net.
Using KCF and face recognition for outdoor target tracking UAV

Outdoor target tracking UAV (Unmanned Aerial Vehicle), which is a research hotspot in the field of computer vision and unmanned aerial system, needs robust target-tracking algorithms with good real-time performance, accurate position estimator of UAV and the corresponding control strategy of the system. In this paper, we designed an outdoor drone tracking system using PCA (Principal Component Analysis) face recognition algorithm and KCF (Kernel Correlation Filter) target tracking algorithm. Firstly, an image acquisition unit is constructed by using an on-board pan-and-tilt camera to capture an outdoor monitored area. Secondly, the PCA algorithm is used for face matching, then the tracking mode is automatically transferred when the expected face target is recognized. Finally, the target tracking is performed by the KCF algorithm. After that, the position error is calculated and sent to the flight control system through the MavLink protocol, thereby performing posture adjustment and completing the tracking and monitoring task. Experimental results show that the performance of outdoor target tracking flight robot is stable and reliable, which meets the requirements of outdoor target tracking and has a good application prospect.