Reviewerly/exHarmony huggingface dataset:
Shall We Go to the MALL? — Students‘ Perceptions of a Business English Learning App

The study reported in this paper investigated students' perceptions of an application (app) by which mobile-assisted language learning was used to learn business English.An app was developed for purposes of the research.The technology acceptance model was adopted to examine the intent to use the app and find relationships among acceptance variables.One-hundred students of engineering programs at a university in Taiwan joined this study and they were asked to use the app and report on their perceptions.Subsequently, thirteen of the participants also took part in a process of reflection, designed to gain a deeper understanding of their first impressions and perceptions of the app and the associated learning process.It was found that they had a positive attitude toward use of the app for business English learning and were willing to use it.Perceived ease of use was a major factor impacting their perceptions of use of the app for language learning.Furthermore, perceived satisfaction correlated with future behavioral intention; when students were satisfied with the system, they were more accepting of the learning tool.Finally, the students' feedback and suggestions implied that the app enhanced their language proficiency, indicating that mobile-assisted language learning continues to make progress in such contexts.
Resolution Adaptive Networks for Efficient Inference

Adaptive inference is an effective mechanism to achieve a dynamic tradeoff between accuracy and computational cost in deep networks. Existing works mainly exploit architecture redundancy in network depth or width. In this paper, we focus on spatial redundancy of input samples and propose a novel Resolution Adaptive Network (RANet), which is inspired by the intuition that low-resolution representations are sufficient for classifying "easy" inputs containing large objects with prototypical features, while only some "hard" samples need spatially detailed information. In RANet, the input images are first routed to a lightweight sub-network that efficiently extracts low-resolution representations, and those samples with high prediction confidence will exit early from the network without being further processed. Meanwhile, high-resolution paths in the network maintain the capability to recognize the "hard" samples. Therefore, RANet can effectively reduce the spatial redundancy involved in inferring high-resolution inputs. Empirically, we demonstrate the effectiveness of the proposed RANet on the CIFAR-10, CIFAR-100 and ImageNet datasets in both the anytime prediction setting and the budgeted batch classification setting.
Algorithm for optimal path planning of impact roller in high-embankment airport

With the continuous increase of high-embankment construction projects in China's airports, impact roller has become more and more widely used. And the construction technology with automatic driving, path planning and adaptive operation has become the development direction in high-embankment airport. Combined with advanced information technology such as Beidou positioning navigation system, the Internet and cloud computing, this paper proposes an algorithm for optimal path planning of impact roller to meet the need. The algorithm includes three aspects: working surface rasterization, full coverage path generation and optimal path planning. Based on the structure of the impact roller, the algorithm adopts the method of non-overlapping wheel tracks to plan the path which covers the entire working surface, and could get the optimal path by analyzing the compaction quality during the construction process. Through field test, it is proved that the proposed algorithm has certain feasibility and effectiveness in guiding impact roller to work. The impact roller can not only ensure the compaction quality, but also improve work efficiency by following the path generated by the algorithm.
On combining probabilistic and semantic similarity-based methods toward off-domain reasoning for situational awareness

Hard and soft high-level fusion plays an important role in the situational awareness literature. To deal with complex real-world situations, it is highly desirable that such systems are able to effectively capture the rich semantics associated with soft information and world/domain knowledge, to efficiently reason with imperfect information, and to benefit and learn from any data that may be available, among others. In these respects, first-order probabilistic models, such as Markov Logic Networks, hold great promise and have received recent attention for high-level fusion. By combining the expressiveness of first-order logic and probabilistic graphical models, such models are able to facilitate representation, reasoning and learning with complex relational information and rich probabilistic structure within a unifying framework. However, first-order probabilistic models may face various challenges in dealing with real-world situational awareness, including scalability of reasoning, learning and knowledge base construction, and robustness in open worlds. In this paper, we motivate a new and pragmatic approach toward collectively addressing these concerns; that is, endowing high-level fusion systems a capability to perform off-domain reasoning, through the ability to reason about unknown/unmodelled concepts. In particular, we will discuss how such an approach could be achieved by means of combining probabilistic and semantic similarity-based methods. We will also explore the potential contribution of semantic similarity measures derived from both taxonomic knowledge (e.g., ontologies) and distributional semantic models (generated from text corpora) toward achieving this goal.
“Big Challenges” and Regional Technical University: Values and Actions

The system of “big challenges” formulated in the “Strategy of scientific and technological development of the Russian Federation” is projected by the authors on the model of regional technical university. This projection is mediated by the concretization of the system of big challenges with regard to the social and economic development of a separate region of Russia (Komi Republic). An attempt to explicate the value basis of the system of “big calls” has been undertaken. The article considers the methods of embedding the value axiomatics of “big challenges” into the value profile of university organizational culture. There is a distinction between civilization and globalist paradigms of Russia positioning, as well as a distinction between well-being and security as two strategies of values generalization. In defining the development strategy of the regional technical university in the context of “big challenges,” the modus of values and the modus of action are highlighted. According to the modus of values, the expediency of focus on the “second scenario” of development provided for in the “Strategy of scientific and technological development of the Russian Federation” (optimistic) is justified. The authors emphasize the possibility of effective reflection of the value axiomatics of “big challenges” by the university community due to a special technology of monitoring and adjusting the university organizational culture referred to as “value-competence parallelepiped”.
How Usability Defects Defer from Non-Usability Defects? : A Case Study on Open Source Projects

Usability is one of the software qualities attributes that is subjective and often considered as a less critical defect to be fixed. One of the reasons was due to the vague defect descriptions that could not convince developers about the validity of usability issues. Producing a comprehensive usability defect description can be a challenging task, especially in reporting relevant and important information. Prior research in improving defect report comprehension has often focused on defects in general or studied various aspects of software quality improvement such as triaging defect reports, metrics and predictions, automatic defect detection and fixing. In this paper, we studied 2241 usability and non-usability defects from three open-source projects - Mozilla Thunderbird, Firefox for Android, and Eclipse Platform. We examined the presence of eight defect attributes - steps to reproduce, impact, software context, expected output, actual output, assume cause, solution proposal, and supplementary information, and used various statistical tests to answer the research questions. In general, we found that usability defects are resolved slower than non-usability defects, even for non-usability defect reports that have less information. In terms of defect report content, usability defects often contain output details and software context while non-usability defects are preferably explained using supplementary information, such as stack traces and error logs. Our research findings extend the body of knowledge of software defect reporting, especially in understanding the characteristics of usability defects. The promising results also may be valuable to improve software development practitioners' practice.
Multi-Scale Object Detection Using Feature Fusion Recalibration Network

In this paper, the object detection algorithm based on deep learning running on the robot platform is studied and optimized. The p has high requirements for the detection efficiency and scale invariance of the algorithm. In order to improve the detection accuracy on all scales and keep the balance between speed and accuracy, we propose the following methods: Aiming at the problem of low detection accuracy of object detection algorithm for scale changing objects, the traditional image pyramid technology of computer vision is used to verify its effectiveness in improving the detection accuracy of the algorithm for scale changing objects. Then, by embedding the image pyramid into the network, the memory consumption caused by the traditional pyramid is reduced, and the detection accuracy of the algorithm for different scale objects is improved. A new feature fusion recalibration structure is designed. Feature fusion can fuse the low-level location information and high-level semantic information. The recalibration assigns the importance weight of the channel of the feature maps. This structure can effectively improve the detection accuracy of the algorithm at all scales without losing too much speed. We apply these two structures to YOLO. The accuracy of the improved algorithm has a significant improvement and the algorithm can run at 16 FPS on a TITAN Xp GPU.
Interdisciplinary Knowledge Problem in a High-Tech Society

The actuality and relevance of the problem is determined by the importance of transformations in the structure of science, represented by the growth of interdisciplinary researches, as well as a change in the strategy of cognitive activity, definitely stated in the synthesis of science and technologies. The answer to the question of how scientific knowledge is changing is important for choosing an adequate social development strategy, since the role of science and scientific technologies in modern society is dramatically growing. The purpose of the article is to set out the social and philosophical reflection of social and cultural consequences of mutual impact of the high technologies and the interdisciplinary researches in the information society context. The positive and negative sides of the mutual influence of the cognitive and innovative instruments of the natural, social, technical and human sciences, under the conditions of information and communication, nano- and bio-technologies development are manifested. Interdisciplinary researches are newly problematizing the concept of expertise, which should be an interdisciplinary synthesis and cannot be obtained within a certain discipline. The growing phenomenon of interdisciplinary science, which manifests itself in the fusion of technology and society, the emergence of human-machine hybrids, which causes serious anthropological and sociocultural problems, imply new challenges for professional epistemologists, dictate a public need for knowledge of the causes and consequences of the high technologies impact on people and society as a whole.
Digital Image Processing For Detecting Yellow Lines (Patch Yellow) In Palm Oil Plant Using Sobel Algorithm

Abstract Through computer hardware, digital images can manipulate and analyze data is the process and purpose of processing digital images. Where the data from the Digital Image used is edge detection. The use of visual techniques such as image processing can simplify the process of analyzing an object without dealing directly with the object to be observed. Image of palm oil leaves through image processing can be analyzed through the level of the greenness of the leaves and the patterns that exist on the leaves to determine the fertility of oil palm plants. This research uses descriptive analysis method, by collecting leaf samples that need to be analyzed using image processing software, then detected to determine the pattern of the shape of the leaf being attacked. The process of this research goes through the Preprocessing stage of palm oil leaf image, Feature Extraction, System Implementation, System Testing, documentation and report preparation. The difference from the leaves that are attacked by yellow patch yellow, on the surface of the affected leaves there are patterns such as patches, the more patterns, the more severe the disease attack. Spots at each stage of the disease were successfully obtained through image processing based on a single filter.
Respon Dosen dan Mahasiswa Terhadap Penggunaan Media Pembelajaran Multimedia Interaktif Macromedia Flash

This research is motivated by changes in the science learning process, especially at the tertiary level which trains student teacher candidates to use their thinking potential through social interaction in learning. In addition, the lack of development of the media in Integrated Science lectures is also a particular concern in this study. The purpose of this study is to analyze how the response of lectures and students of Physics Education at the University of Muhammadiyah Makassar to the use of Interactive Multimedia Macromedia Flash in Integrated Science lectures. The research sample consisted of 5 (five) integrated science lectures and 35 (thirty-five) students. This research is qualitative research that describes how the response of lecturers and students to the learning media used. The data in this research are lecturer response questionnaire data and student response questionnaire data. The data is then analyzed descriptively. Based on the results of the validation analysis of learning media by the validator (expert), the Aiken index average 0,76 in the medium category. Based on the results of the study found 87,50 % of lecturers responded positively to the use of interactive multimedia learning media with Macromedia Flash in Integrated Science lecturers. And as much as 83,86 % students gave a positive response to the use of interactive multimedia learning media with Macromedia flash in Integrated Science lectures
Robot Playing Kendama with Model-Based and Model-Free Reinforcement Learning

Several model-based and model-free methods have been proposed for the robot trajectory learning task. Both approaches have their benefits and drawbacks. They can usually complement each other. Many research works are trying to integrate some model-based and model-free methods into one algorithm and perform well in simulators or quasi-static robot tasks. Difficulties still exist when algorithms are used in particular trajectory learning tasks. In this paper, we propose a robot trajectory learning framework for precise tasks with discontinuous dynamics and high speed. The trajectories learned from the human demonstration are optimized by DDP and PoWER successively. The framework is tested on the Kendama manipulation task, which can also be difficult for humans to achieve. The results show that our approach can plan the trajectories to successfully complete the task.
About the science of pedagogy (how to avoid the transformation of pedagogical science into ideology)

Introduction. Numerous declarative official statements on the key role of education and scientific-pedagogical knowledge to ensure the well-being of the nation and the sustainable development of Russia in the modern world directly contradict the situation in this strategically significant social area. Thus, when merging two specialised academies (medical and agricultural sciences) with alma mater – the Russian Academy of Sciences (RAS), the Russian Academy of Education (RAE) was not included in the list of the elected representatives, which may indirectly demonstrate the “inferiority” of scientific pedagogy, calling into question its mission in the development of education. The aim of the article is to discuss the conditions and prerequisites for the possible loss of scientific status by pedagogy, its transformation into ideology and the replacement of pedagogical principles of education development by ideological ones. Methodology and research methods. In the course of the research, the methods of the system-based, retrospective and comparative analysis, synthesis and generalisation were employed. Results and scientific novelty. The author of the present publication considers and analyses the origins of scientific pedagogy and its essential definitions in the works of classical scholars – K. D. Ushinsky, L. N. Tolstoy, P. F. Kapterev, V. P. Vakhterov, K. N. Ventzel, etc. with contemporary interpretation of scientific status of pedagogical branch of knowledge. The paper highlights the impact on its reorganisation, content transformations of political and social processes, as well as transformations of governmental management structures. It has been proved that the reforms of the Russian science carried out in recent years consistently lead to the loss of scientific status by pedagogy, to the loss of academic principle “science should be managed by scientists” and to the transformation of scientific institutions into subordinate structures. Contrary to the Article 13 of the Constitution of the Russian Federation, which states that “no ideology may be established as state or mandatory”, there is a conversion of pedagogy into ideology. The classical pedagogical foundations evolutionary development inherent in scientific knowledge are deformed by current ideological attitudes, nullifying the constitutional guarantee (Article 44) on freedom of literary, artistic, scientific, technical and other forms of creativity. The depoliticisation of educational sphere and pedagogical science was planned at the end of the 20th century but it was abandoned. Therefore, the author emphasises the importance of such process. Practical significance. The assessments and conclusions presented in the article create methodological prerequisites for developing a discussion about the mission of pedagogical science in modern Russian society, adjusting scientific reforms and, as a result, freeing education from ideological violence.
Recoloring of visual multimedia using matlab to aid color vision deficient individuals

Color Vision Deficiency (CVD) has a widespread impact and has affected more than 300 million people across the world. CVD has three major types as Monochromacy, Dichromacy and Anomalous Trichromacy. We design and develop a website with re-coloring algorithms incorporated for anomalous trichromats a nd dichromats. Unlike an image recoloring schemes, recoloring a video demands maintaining color consistency in the frames which implies similar color in adjacent frames should be recolored to similar necessary color. Our aim is to show the Color Vision Deficient (CVD) the primary colors distinguishably, which they cannot see in normal lighting conditions. To achieve this goal, we have incorporated recoloring algorithm into the website using Matlab as a back-end language, which will first conduct Ishihara plates test to check if a user is CVD. If yes, the test will then comment on the type of its Color Blindness, based on this user would be recommended to go to the respective module of recoloring. User would be asked to submit video or an image which is to be re-colored. Video/Image would be re-colored on server side, the recolored output would be displayed on a screen with an option to download result. The RGB to LMS algorithm is used to recolor those pixels in images and videos which are unperceivable to users. After recoloring they will be able to distinguish the colors and it will be more recognizable. Finally, there would be a feedback/suggestions which users are asked to fill-out, to understand the performance of algorithm and quality of re-colored image. Experimental results prove that with the help of our website almost all CVD irrespective of their types would be helped in their vision to experience multimedia. Thus our website would aid millions of CVD’s to experience colors in multimedia which they are unable to see distinguishably.
A Comprehensive Software Project Management Framework

Project management is vital for software project success.Project Management Body of Knowledge (PMBOK), CMMI for Development (CMMI-DEV), and other software project management models and practices established to assist software organizations, each of these models offers diverse focal points.Combined effective software project management practices remain a challenge to software organizations.In striving to raise the likelihood of software project success, this paper introduces a framework for software project management effectiveness which integrated a set of software project management practices based on the PMBOK guide processes, CMMI-DEV processes, and management areas that influence a software project.The proposed framework called 4PTRB which includes people, process, product, project, technology, risk, and business management areas.The 4PTRB software project management framework provides a whole comprehensive view to help software project managers to improve their project management effectiveness.The framework is validated by conducting a web-based survey of software professional practitioners around the world.A statistical analysis of twenty software projects has been conducted as a case study thereafter, spanning small to large development projects.The consistency of the case study and survey results shows the power of the 4PTRB framework as an inclusive software project management framework.
Progressively-Growing AmbientGANs for learning stochastic object models from imaging measurements

The objective optimization of medical imaging systems requires full characterization of all sources of randomness in the measured data, which includes the variability within the ensemble of objects to-be-imaged. This can be accomplished by establishing a stochastic object model (SOM) that describes the variability in the class of objects to-be-imaged. Generative adversarial networks (GANs) can be potentially useful to establish SOMs because they hold great promise to learn generative models that describe the variability within an ensemble of training data. However, because medical imaging systems record imaging measurements that are noisy and indirect representations of object properties, GANs cannot be directly applied to establish stochastic models of objects to-be-imaged. To address this issue, an augmented GAN architecture named AmbientGAN was developed to establish SOMs from noisy and indirect measurement data. However, because the adversarial training can be unstable, the applicability of the AmbientGAN can be potentially limited. In this work, we propose a novel training strategy|Progressive Growing of AmbientGANs (ProAGAN)|to stabilize the training of AmbientGANs for establishing SOMs from noisy and indirect imaging measurements. An idealized magnetic resonance (MR) imaging system and clinical MR brain images are considered. The proposed methodology is evaluated by comparing signal detection performance computed by use of ProAGAN-generated synthetic images and images that depict the true object properties.