Reviewerly/exHarmony huggingface dataset:
Enhancing Reusability: An Integrated Framework for Software Requirements Classification and Prioritization

Requirements Prioritization (RP) is the process of planning, identifying, rating and organizing requirements to avoid breach of trust, contract or agreement. The need of RP is inevitable if the concerns, cost and budget estimation of the stakeholders are considered as primary parameters in a software project. In order to ensure the reusability of existing modules, the new requirements must be classified and coordinated with the current ones in the repository, which guarantees the monetary use of assets. In this research, we have suggested a new framework for software requirements classification and prioritization. This framework is called the Software Requirements Classification and Prioritization Framework (SCPF). It will help the software project managers in managing the requirements at the initial stage of software development. We have identified the key factors and developed a taxonomy in order to classify these factors. This study likewise devised a new RP algorithm, which is based on the normalized aggregation values of the identified factors, and also contributes for the requirements classification by using [Formula: see text]-Nearest-Neighbor (KNN) classifier based on the well-known Normalized Compression Distance (NCD) similarity measure. In addition to this, we have implemented the SCPF by developing a Web-based application which provides the computation features for requirements classification and prioritization. Furthermore, we have conducted a case study and an industry survey to evaluate the results of the SCPF. A comparative study is also presented to show the effectiveness of SCPF with respect to three different state-of-the-art RP approaches. The computed SCPF results are very promising and showed significant improvement in the quality of requirements classification and prioritization.
The Effectiveness of Online Learning on Elementary School Student Learning Results

The research entitled the effectiveness of online learning on the learning outcomes of elementary school students, while the purpose of this study was to obtain information about the effectiveness of online learning on student achievement, especially in grades IV and V. This research was conducted at Al Ikhlas Islamic Elementary School. South Jakarta. Indonesia. The method used in this study is a quantitative descriptive approach, the instrument used as an indicator to explore the effectiveness of online learning by using a questionnaire as an instrument. The population this study were students in grades IV A, IV B, V A, VB and VC who were taken randomly, totaling 94 respondents using random sampling. Data analysis used a simple linear regression test with a significant level of 0.05 (5%). Based on the results of the calculation of a significant value of 0.000&lt;0.05, it can be concluded that the effectiveness of online learning on student achievement has a regression equation, this is proven by the results of statistical tests using the Ttest and the calculation results obtained are 11,064&gt;1,661, with significance level 0.000&lt;0.05.While the effectiveness of online learning on student achievement is obtained a value of 75.6% through the calculation of the coefficient of determination. The results of this study indicate that there is quite a significant or low influence on online learning on student achievement.
Body and Emotion in the Sonic Act

This chapter develops an embodied cognitive framework connecting timbre perception to emotion, arousal, and meaning. Examining the dynamics of vocal exertion, it shows a basic correlation between the “sound of arousal” in the voice and high-arousal affect, even in relation to instrumental timbres. It claims that this relation plays a significant role in the way noisy timbres are perceived in musical and cultural contexts, and helps to explain how qualities of timbre can be somatically marked with positive or negative affective implications. These markings, which are largely conveyed through the acoustical properties of spectral centroid, inharmonicity, and auditory roughness, enable and constrain the affective response, thus determining how and what a given timbral quality can mean. The chapter moves through embodied theories of emotion to the expression of emotion in the voice (including an analysis of singer-songwriter Brandi Carlile’s “The Story”); from the role of vocality in musicking to the vocal aspects of instruments; from a physiological discussion of arousal and exertion to an acoustical reading of how arousal affects timbre; and, finally, to an account of the sensorimotor dimensions of hearing and the auditory dimensions of doing. It concludes with the suggestion that perceiving timbre involves some level of motor resonance with the acts that produce sound and with the human actors standing behind that sound.
Research on the Transformation Paths of TV Anchors in the Context of Oral Communication

In the all-media era, information is ubiquitous. The trend of increasingly intellectualized and mobilized media convergence is building up a new social connection, which also brings new opportunities and challenges to the traditional TV hosting industry. Aiming at providing new paths for the TV anchors' transformation into new areas, this paper explored on cases like the live stream show for "helping Hubei Province" jointly held by China Media Group and Li Jiaqi, and other livestreaming commerce for public benefits and conducted a comparative study on the oral communication behaviors between the net celebrities (especially livestreaming hosts for commerce) and TV anchors. It was supposed to inductively analyze the characteristics of oral communication from two perspectives: linguistic communication and non-linguistic communication and offer new insights for the anchors' transformation.
PROMOTING TEACHERS` MOTIVATION TO TEACH: THE POWER OF VALUES AND COMPETENCES

Nowadays, in the most cases, the lack of motivation is associated with a necessity to promote intrinsic motivation. It must be admitted that teachers` motivation has a significant influence on students` motivation. Teachers` motivation has an impact on positive attitude towards their professional work, creating initiatives and willingness to promote positive change in students. The publication emphasizes the factors influencing teachers` motivation, e.g. the effectiveness and quality of cooperation and the environmental context, as well as three basic psychological needs: the need for autonomy, competence and cooperation (including communication and diverse interaction), according to Deci and Ryan, 2000 self-determination theory. The article also focuses on values as motivational basis, highlighting that such values as self-initiative, innovation, cooperation are important for teachers` motivation and professional competence. The aim or article is discover teachers` motivation to teach, using theoretical methods: concepts of motivation formation and conceptual framework of teachers` competence and empirical methods: a survey of teachers about their experience, motivation, values and competence. The authors explore existing links and synergies between values, competences and teachers` motivation to teach our future generation.
Automatic inspection and analysis of digital waveform images by means of convolutional neural networks

&amp;lt;p&amp;gt;Analyzing seismic data to get information about earthquakes has always been a major task for seismologists and, more in general, for geophysicists.&amp;lt;br&amp;gt;Recently, thanks to the technological development of observation systems, more and more data are available to perform such tasks. However, this data&amp;lt;br&amp;gt;&amp;amp;#8220;grow up&amp;amp;#8221; makes &amp;amp;#8220;human possibility&amp;amp;#8221; of data processing more complex in terms of required efforts and time demanding. That is why new technological&amp;lt;br&amp;gt;approaches such as artificial intelligence are becoming very popular and more and more exploited. In this work, we explore the possibility of interpreting seismic waveform segments by means of pre-trained deep learning. More specifically, we apply convolutional networks to seismological waveforms recorded at local or regional distances without any pre-elaboration or filtering. We show that such an approach can be very successful in determining if an earthquake is &amp;amp;#8220;included&amp;amp;#8221; in the seismic wave image and in estimating the distance between the earthquake epicenter and the recording station.&amp;lt;/p&amp;gt;
Data Input Quality Metrics on Mobile Positioning Data (MPD)

Statistics Indonesia (BPS) has been using Mobile Positioning Data (MPD) to support official statistics since 2016. As a source of big data, MPD also has veracity characteristics, indicating uncertainty in the data. Therefore, it is necessary to check that the data are good enough to allow further analysis and the quality assurance process. Currently, there is no established international standard for quality assurance of MPD. This paper describes the quality matrix used by BPS in examining data from mobile operators. BPS uses thirteen indicators in conducting quality assurance, where the inspection uses several different methods, such as setting a threshold, checking data completeness, and checking the form of data distribution. Exploratory Data Analysis is carried out to determine whether the data meets the requirements for further analysis. We conducted this research on a mobile network operator data for June - July 2020 as the basis for MPD analysis in 2021. Based on the inspection during this period, BPS can cooperate with this cellular operator to conduct data analysis in 2021. However, the operator must repeat the calculation of the required matrix as quality assurance every month.
Virtual structured-light three-dimensional point cloud compression with geometric reshaping

Converting three-dimensional (3D) point cloud data to two-dimensional (2D) image based on virtual structured-light system is a popular method in 3D data compression because the image format is easier to process by the exiting storing and transmitting methods. When this method is used in the 3D point cloud compression, the quantization error is introduced during the coordinate mapping. To solve this problem, a virtual structured-light 3D point cloud compression algorithm based on geometric reshaping is presented. And a least-square system parameter optimization method is proposed to further improve the data decoding accuracy. In the proposed method, the 3D spatial coordinates are reshaped to a 2D matrix first, and then the 2D matrix is stored as a “Holoimage” by using the parameter optimized virtual structured-light system. The quantization error introduced by coordinate mapping between X and Y coordinates of the 3D point cloud and the 2D image pixel coordinates is suppressed, so the decoding accuracy is improved. In addition, the geometric information of the 3D point cloud is hidden synchronously when the 3D point cloud is compressed, which is of great significance in the copyright protection of the point cloud data. Experiments verify the effectiveness of the proposed algorithm. The decoding root mean square error (RMSE) of the proposed method is decreased by 79.86% on average compared with the traditional one under the same compression ratio, and the compression ratio of the proposed method is 1.96 times bigger than the traditional one under the similar decoding accuracy.
Partition Functions I

We review partition functions in thermodynamics and quantum mechanics, and introduce string partition functions as generalisations of particle partition functions. The concept of modular invariance is introduced and highlighted as a feature which distinguishes (closed) strings from particles.
Design of Obstacle Avoidance Control System for Mobile Robot Based on Vision

In order to realize the obstacle avoidance control system of mobile robot through robot vision technology, this paper will carry out relevant research, mainly discusses the basic concept of robot vision technology, the relationship between visual technology and obstacle avoidance control, and then puts forward the design requirements and design scheme of obstacle avoidance control system, and finally tests the system. By using the method in this paper, the robot obstacle avoidance control system can be designed to meet the requirements. The system has been tested and applied effectively, and the obstacle avoidance control effect is good.
Privacy preserving reversible watermarking in the encrypted domain through self-blinding

This paper presents robust image-based reversible watermarking in the encrypted domain. Two algorithms with a high embedding rate are proposed for embedding data in a homomorphic-encrypted domain using the Paillier encryption scheme. Both algorithms exploit the self-blinding property of the Paillier scheme to accomplish flexibility in extraction. Using these algorithms, blind and error-free watermark extraction is possible in the plaintext domain and encrypted domain. The robustness of the algorithms has validated by considering various noise attacks in the encrypted domain. The proposed methods outperform its predecessors on the same embedding platform either in the flexibility of the extraction process or in the embedding capacity and find applications in privacy preserving distributed signal processing which is a major requirement in a cloud environment.
Learning across cultures and communities

The chapter presents a research line developed at the University of Siegen in Germany that had improving the living conditions of migrant communities in their new social environment as its aim. To this purpose, the research team set up intercultural computer clubs in various German cities that had a high presence of migrant families. The positive experience in Germany motivated the research team to replicate the same idea in two completely different contexts, namely a refugee camp in Palestine and a community in the High Atlas in Morocco. The basic choices made regarding how to construct the initial computer clubs were almost similar in each instance but at the same time were adapted to the different situations and improved to offer more robust services to these critical communities. The key idea was to aim for cultural integration and leave the ownership of the technology to the locally involved stakeholders.
Analisis Spasial Peluang Lokasi Unit Sekolah Baru Menggunakan Metode Score dan Sistem Informasi Geografis

School is a formal educational institution recognized by the Indonesian State Education Institute, every citizen has the right to get it. In accordance with field interview observations, there is no equal distribution of junior high school locations around sub-districts in Aceh Besar and the distance of school locations from students' residences is very far. The purpose of this study is to analyze geographic information systems (GIS) on the probability of building new school in Aceh district, which are divided in 4 criterias; high, medium, low, and no probability, as well as using school spatial analysis (buffer, overlay and clip) and scoring methods to determine the probability for the location of the new school. The results show 4 criteria for the location probability; there are 11 districts with high probabilities, 21 medium probabilities, 23 low probabilities, and 23 no probabilities in each district.
“Patiyoot” Cryptography Authentication Protocol for Computer Network

Authentication is the process of identifying users that request access to a system, network, or device. It keeps invalid users out of databases, networks, and other resources. It is a system for differentiating legitimate users from illegitimate ones. If a message is not original and timely, then it is considered as not being authentic, not being from whom it claims to be. An authentication protocol is defined as a computer system communication protocol which may be encrypted and designed specially to securely transfer authenticated data between two parties. In this research paper, the new of authentication protocol called "Patiyoot" is invented and explained. It consists of ten steps. Each step is elaborated with the reason why it is done in that particular way. It enhances security from the previous conventional authentication protocols, which have some security issues, by using Nonce and Timestamp.
Parallel implementation of a despeckling in SAR images using GPU based segmentation and 3D-MAP estimation

This paper proposes a parallel scheme for suppressing speckle noise in SAR images. The designed technique is based on forming 3D arrays of a clustered image by areas and using Maximum a Posteriori (MAP) estimation, where the a priori information is obtained by the Discrete Wavelet Transformation (DWT), improving the despeckling quality. Moreover, a variant of the bilateral filter is used as a post-processing stage to recover and enhance edges’ quality after the filtering procedure. The proposed scheme was implemented in serial and two parallel versions. The first one uses OpenMP to parallelize over a multi-core CPU, and the second utilizes CUDA to be executed in a GPU. Experimental results have demonstrated that the framework guarantees a good despeckling performance on SAR images obtained from the TerraSAR-X database, considering objective quality criteria such as Peak Signal-to-Noise Ratio (PSNR), Structural Similarity Index (SSIM) and Edge Preservation Index (EPI). Furthermore, the parallel implementations’ simulation results present their efficiency for a real-time environment.
Fregata: A Low-Latency and Resource-Efficient Scheduling for Heterogeneous Jobs in Clouds

An increasing number of large-scale data analytics frameworks move towards larger degrees of parallelism aiming at low-latency guarantees. It is challenging to design a scheduler with low latency and high resource utilization due to task dependency and job heterogeneity. The state-of-the-art schedulers in cloud/datacenters cannot well handle the scheduling of heterogeneous jobs with dependency constraints (e.g., dependency among tasks of a job) for simultaneously achieving low latency and high resource utilization. The key issues lie in the scalability in centralized schedulers, ineffective and inefficient probing and resource sharing in both distributed and hybrid schedulers. To address this challenge, we propose Fregata, a low-latency and resource-efficient scheduling for heterogeneous jobs with constraints (e.g., dependency constraints among tasks of a job) in clouds. Fregata first uses the machine learning algorithm to classify jobs into two categories (high priority jobs and low priority jobs) based on the extracted features. Next, Fregata splits the jobs into tasks and distributes the tasks to the master nodes based on task dependency and the load of master nodes. Then, Fregata utilizes the dependency information of tasks to determine task priority (tasks with more dependent tasks have higher priority), and packs tasks by leveraging the complementary of tasks’ requirements on different resource types and task dependency. Finally, the master nodes distribute tasks to workers in the system based on priority of tasks and workers and the resource demands of tasks and the available resources of workers. To test the performance of Fregata, we conduct trace-driven experiments. Extensive experimental results based on a real cluster and Amazon EC2 cloud service show that Fregata achieves low-latency and high resource utilization compared to existing schedulers.
Active Learning Optimization for Boundary Identification Using Machine Learning-Assisted Method

&lt;div class="section abstract"&gt;&lt;div class="htmlview paragraph"&gt;Identifying edge cases for designed algorithms is critical for functional safety in autonomous driving deployment. In order to find the feasible boundary of designed algorithms, simulations are heavily used. However, simulations for autonomous driving validation are expensive due to the requirement of visual rendering, physical simulation, and AI agents. In this case, common sampling techniques, such as Monte Carlo Sampling, become computationally expensive due to their sample inefficiency. To improve sample efficiency and minimize the number of simulations, we propose a tailored active learning approach combining the Support Vector Machine (SVM) and the Gaussian Process Regressor (GPR). The SVM learns the feasible boundary iteratively with a new sampling point via active learning. Active Learning is achieved by using the information of the decision boundary of the current SVM and the uncertainty metric calculated by the GPR. The optimal sampling point is selected through multi-objective optimization so that the sampling point is close to the decision boundary of the current SVM as well as it has a high uncertainty metric. To illustrate the characteristics and effectiveness of the proposed approach, we apply the proposed approach to both simple problems with synthetic feasible regions and a complex industrial problem: autonomous driving path planning algorithm validation. With the proposed approach, edge cases are more effectively identified, so the classification accuracy of the resulting SVM is considerably higher than that of the SVM trained with conventional sampling techniques such as Monte Carlo Sampling and Latin Hypercube Sampling.&lt;/div&gt;&lt;/div&gt;
The cost estimation of power grid technological transformation project based on Shapley value

The project cost of power grid engineering technology transformation projects has shown importance in the development of China's power industry. By summarizing the models that can be used to predict the cost of technological transformation, the genetic algorithm based support vector machine (GA-SVM) and the cost estimation model based on extreme gradient boosting machine learning algorithm (XGBoosting) are studied. This paper combines the above theories and the Shapley value theory in game theory to construct a combined cost estimation model, which can realize high-precision estimation of power grid technological transformation projects. This paper takes the overhead line project as an example for simulation, which proves that the combined forecasting model can accurately reflect the actual project cost, and it has reference value for the cost forecast of the power grid engineering technology transformation project.
The University Academic Guide and Academic Satisfaction during the COVID - 19 Pandemic

The covid-19 pandemic determined that different governments established mandatory confinement or quarantine; for this reason, the education had to be carried out at a distance or remotely; However, some universities already taught this modality; on the other hand, these changes caused discontent for many students because they were forcibly drawn to this modality without having been the choice for their professional training. In this context, the role played by the academic guide turned out to be fundamental to motivate and keep them hooked, preventing students from abandoning their careers. The objective of the study was to determine the relationship between the accompaniment of the academic guide and the level of academic satisfaction in the students of a private university during the COVID-19 pandemic. The quantitative approach method was used. From the statistical processing, it was concluded that there is a significant relationship between the variables academic guidance and academic satisfaction.