Reviewerly/exHarmony huggingface dataset:
Technical and Infrastructural Aspects of Mobile Learning Adoption in Iran Higher Education

Nowadays learning has developed to a new way of anywhere and anytime by using mobile devices called m-learning which can provide flexibility, independency and creativity in academic environment. Most studies about m-learning are for higher education and the most users of m-learning are higher education students. Although developed countries are using m-learning in educational sectors, most of the Middle East countries are far from m-learning, and facing number of challenges. In Iran m-learning is still in early stage of implementation in higher education and in terms of technical and infrastructural aspects there is a vast gap in compare with developed countries. Although technical and infrastructural difficulties are one of the significant aspects in implementation and integration of m-learning technologies in education, the technology will not be successful if could not adopt with users. Due to the importance of user adoption with m-learning, there are limited studies about m-learning adoption in higher education of Iran. This paper attempts to review on technical and infrastructural aspects that facilitate m-learning which have effect on adoption of Iran higher education system. The review of the trend in the literature provides a reference for higher education institutes for decision making in developing m-learning for their students.
Responsibility Analysis by Abstract Interpretation

Given a behavior of interest in the program, statically determining the corresponding responsible entity is a task of critical importance, especially in program security. Classical static analysis techniques (e.g. dependency analysis, taint analysis, slicing, etc.) assist programmers in narrowing down the scope of responsibility, but none of them can explicitly identify the responsible entity. Meanwhile, the causality analysis is generally not pertinent for analyzing programs, and the structural equations model (SEM) of actual causality misses some information inherent in programs, making its analysis on programs imprecise. In this paper, a novel definition of responsibility based on the abstraction of event trace semantics is proposed, which can be applied in program security and other scientific fields. Briefly speaking, an entity ER is responsible for behavior B, if and only if ER is free to choose its input value, and such a choice is the first one that ensures the occurrence of B in the forthcoming execution. Compared to current analysis methods, the responsibility analysis is more precise. In addition, our definition of responsibility takes into account the cognizance of the observer, which, to the best of our knowledge, is a new innovative idea in program analysis.
Human Action Recognition Based on Deep Learning

Deep convolutional network has achieved great success in visual recognition of static images, while it is not so advantageous as traditional methods in action recognition in videos. However, in recent years, researchers have proposed a series of human action recognition networks for videos, and proposed some efficient and effective methods for action recognition. This paper will enumerate the current main action recognition network model. First we will introduce Two-Stream Convolutional Network, Temporal Segment Networks and C3D Network, and then we will introduce an efficient method for action recognition. The purpose is to introduce the characteristics of different network models, and provide some valuable reference for the research of action recognition.
The Effectiveness of Using Kahoot Media in History Learning at High School

The purpose of this study are: first, to know the perceptions of the teacher/lecturer about the Kahoot application program in apperception of the teaching and learning process towards indicators of student activity, student and teacher enthusiasm, IT skills, innovative learning, creative in learning, learning effectiveness, and fun learning, secondly the researchers want to know the teacher's perceptions of the Kahoot application program in the apperception of the teaching and learning process towards achievement motivation and thirdly want to know the constraints in using the Kahoot application program in apperception of the teaching and learning process.The methodology in this study used the Descriptive Research method by conducting an experiment using the Kahoot application for high school in Sleman Regency.Based on the results about the perception of the use of the Kahoot program, most of them gave answer that they're Strongly Agree that this program will be implemented by teachers/lecturers.
Development of Language in the Profits Participants Based on Age

The aim of this study is to find and describe the development of language mastery in the essay of students seen from the aspect of the number of entries, affix, spelling, and code change. To achieve these objectives, qualitative and quantitative methods are used. The data obtained are three articles of children, adolescents, and adults with one theme and the same time. The results of this study indicate that there is an increase in the number of entries, affixes, spelling use, and code changes in each age group. A prominent difference exists in the use of affixes and spelling in articles. Each age group uses different affixes. For example, children often use prefix {meN-}, adolescents use prefix {ber-}, and suffix {-nya}, whereas adults have more affix variations. The results of this study can help teachers and community to know the characteristics of the development of language mastery in the essay of learners based on their age.. Keywords: authorship, learners, age
생체 모방 기반 융합 수업 모델을 적용한 ‘배설과 여과작용’ 디지털 교재 개발

The purpose of this study is to develop digital textbooks on ‘excretion on filtration’, a part of Science in junior high school, using the learning...
Genuine and secure public auditing for the outsourced data in cloud

In cloud storage, the most common concerns for users are data integrity, confidentiality and availability. Recently, many data integrity auditing schemes have been proposed, some of which achieved privacy-preserving public auditing, some of which realised data sharing and group dynamic, and some of which supported data dynamic. However, as far as we know, there does not exist a practical auditing scheme which can simultaneously realise all the functions mentioned above; In addition, all the existing schemes adopt the method of computing message authentication code (MAC) by users to achieve the following data integrity auditing, nevertheless, it is a arduous task to compute MACs for these cloud users with constrained computing and storage capabilities. In this paper, we propose a novel privacy-preserving public auditing scheme for shared data in the cloud, which can also support data dynamic operation and group dynamic. First, we bring proxy signature into the existing auditing scheme. The cloud user can delegate the computation of MACs to a cloud computing server, so that the user's burden would be greatly reduced; second, by introducing a Lagrange interpolating polynomial, our scheme realises the identity privacy-preserving in the precondition of almost no addition of any new computation and less storage space, moreover it makes group dynamic simple; third, we make an index-switch table and combine it with the Merkle hash tree to realise the practical and secure dynamic operations of shared data by group users; fourth, in our scheme, the cloud storage server will add its private key in producing the proof information to protect the data privacy and resist the active attack. Theoretical analysis demonstrates our scheme is provably secure.
EOSC-SYNERGY: Una iniciativa para la implementación del EOSC liderada por España

The presentation shows the last trends in services of cloud computing for research data. It details the European Open Science Cloud and its Spanish implementation
Learning Factored Markov Decision Processes with Unawareness.

Methods for learning and planning in sequential decision problems often assume the learner is fully aware of all possible states and actions in advance. This assumption is sometimes untenable: evidence gathered via domain exploration or external advice may reveal not just information about which of the currently known states are probable, but that entirely new states or actions are possible. This paper provides a model-based method for learning factored markov decision problems from both domain exploration and contextually relevant expert corrections in a way which guarantees convergence to near-optimal behaviour, even when the agent is initially unaware of actions or belief variables that are critical to achieving success. Our experiments show that our agent converges quickly on the optimal policy for both large and small decision problems. We also explore how an expert's tolerance towards the agent's mistakes affects the agent's ability to achieve optimal behaviour.
Visionary Security: Using Uncertain Real-Time Information in Signaling Games

In important domains from natural resource conservation to public safety, real-time information is becoming increasingly important. Strategic deployment of security cameras and mobile sensors such as drones can provide real-time updates on illegal activities. To help plan for such strategic deployments of sensors and human patrollers, as well as warning signals to ward off adversaries, the defender-attacker security games framework can be used. Previous works do not consider the combined situation of uncertainty in real-time information in addition to strategically signaling to adversaries. In this thesis, we will not only address this gap, but also improve the overall security result by considering security game models and computer vision algorithms together.
ATSFFT: A Novel Sparse Fast Fourier Transform Enabled With Sparsity Detection

The Fast Fourier Transform(FFT) is a classic signal processing algorithm that is utilized in a wide range of applications. For image processing, FFT computes on every pixel's value of an image, regardless of their properties in frequency domain. The Sparse Fast Fourier Transform (SFFT) is an innovative algorithm for discrete Fourier transforms on signals that possess characteristics of the sparsity in frequency domain. A reference implementation of the algorithm has been proven to be efficient than modern FFT library in cases of sufficient sparsity. However, the SFFT implementation has a critical drawback that it only works reliably for very specific input parameters, especially signal sparsity $k$, which hinders the extensive application of SFFT. In this paper, we propose an Adaptive Tuning Sparse Fast Fourier Transform (ATSFFT), which is a novel sparse fast fourier transform enabled with sparsity detection. In the case of unknown sparsity $k$, ATSFFT is capable of probing the sparsity $k$ via adaptive dynamic tuning and completing the sparse Fourier transform. Experimental results show that ATSFFT outperforms SFFT while it is able to control the computation error better than SFFT. Furthermore, ATSFFT achieves an order of magnitude of performance improvement than the state-of-the-art FFT library, FFTW.
The Impact of Time on Hashtag Reuse in Twitter: A Cognitive-Inspired Hashtag Recommendation Approach

In our work [KPL17], we study temporal usage patterns of Twitter hashtags, and we use the Base-Level Learning (BLL) equation from the cognitive architecture ACT-R [An04] to model how a person reuses her own, individual hashtags as well as hashtags from her social network. The BLL equation accounts for the time-dependent decay of item exposure in human memory. According to BLL, the usefulness of a piece of information (e.g., a hashtag) is defined by how frequently and how recently it was used in the past, following a time-dependent decay that is best modeled with a power-law distribution. We used the BLL equation in our previous work to recommend tags in social bookmarking systems [KL16]. Here [KPL17], we adopt the BLL equation to model temporal reuse patterns of individual (i.e., reusing own hashtags) and social hashtags (i.e., reusing hashtags, which has been previously used by a followee) and to build a cognitive-inspired hashtag recommendation algorithm. We demonstrate the efficacy of our approach in two empirical social networks crawled from Twitter, i.e., CompSci and Random (for details about the datasets, see [KPL17]). Our results show that our approach can outperform current state-of-the-art hashtag recommendation approaches.
A Monte Carlo Tree Search Player for Birds of a Feather Solitaire

Artificial intelligence in games serves as an excellent platform for facilitating collaborative research with undergraduates. This paper explores several aspects of a research challenge proposed for a newly-developed variant of a solitaire game. We present multiple classes of game states that can be identified as solvable or unsolvable. We present a heuristic for quickly finding goal states in a game state search tree. Finally, we introduce a Monte Carlo Tree Search-based player for the solitaire variant that can win almost any solvable starting deal efficiently.
ConCORDe-Net: Cell Count Regularized Convolutional Neural Network for Cell Detection in Multiplex Immunohistochemistry Images

In digital pathology, cell detection and classification are often prerequisites to quantify cell abundance and explore tissue spatial heterogeneity. However, these tasks are particularly challenging for multiplex immunohistochemistry (mIHC) images due to high levels of variability in staining, expression intensity, and inherent noise as a result of preprocessing artefacts. We proposed a deep learning method to detect and classify cells in mIHC whole-tumour slide images of breast cancer. Inspired by inception-v3, we developed Cell COunt RegularizeD Convolutional neural Network (ConCORDe-Net) which integrates conventional dice overlap and a new cell count loss function for optimizing cell detection, followed by a multi-stage convolutional neural network for cell classification. In total, 20447 cells, belonging to five cell classes were annotated by experts from 175 patches extracted from 6 whole-tumour mIHC images. These patches were randomly split into training, validation and testing sets. Using ConCORDe-Net, we obtained a cell detection F1 score of 0.873, which is the best score compared to three state of the art methods. In particular, ConCORDe-Net excels at detecting closely located and weakly stained cells compared to other methods. Incorporating cell count loss in the objective function regularizes the network to learn weak gradient boundaries and separate weakly stained cells from background artefacts. Moreover, cell classification accuracy of 96.5% was achieved. These results support that incorporating problem-specific knowledge such as cell count into deep learning-based cell detection architectures improve the robustness of the algorithm.
PENGUATAN MUTU JASA MELALUI PELATIHAN LITERASI INFORMASI PERPUSTAKAAN DALAM MENDUKUNG SUSTAINABLE DEVELOPMENT GOALS

Educational institutions included in service providers the category. The services usually includes scholarships, assessments, providing guidance to the students, parents, and sponsors. As the heart of the university, library has an important role in strengthening the academic quality of students. In addition to providing quality resources for students, many libraries had implemented training programs for information literacy for university academic community. The literacy training program intended for the academic community to literate with the information , seeking the valid information and use the information based on the needs. The need for valid information makes education as agent of change and sustainable development naturally be a key part that integrates the 17 points of SDG's which is the goal of sustainable development. Therefore, information literacy program implemented by the library is one effort in supporting the realization of SDG's in education. UMM library has run several functions such as education, research, and the information set forth in a form of information literacy training with the aim to educate, guide students in doing research, and information sharing. UMM library has also implemented a literacy program which is considered very important for the academic community. That is because the density of the material provided in this training is expected to increase the competence of the academic community in the face of the information era. The information literacy training has been followed by many students and lectures at the University of Muhammadiyah Malang. Students who attend this training average wending towards the thesis and thesis writing. While the lecturers usually groups and individuals participated in this information literacy training. A lot of knowledge and skills acquired by the participants of this information literacy training. And along the communication with the participants, the participants were very enthusiastic and happy to gain experience and new abilities through this literacy training.
It's All About The Scale -- Efficient Text Detection Using Adaptive Scaling

Text can appear anywhere. This property requires us to carefully process all the pixels in an image in order to accurately localize all text instances. In particular, for the more difficult task of localizing small text regions, many methods use an enlarged image or even several rescaled ones as their input. This significantly increases the processing time of the entire image and needlessly enlarges background regions. If we were to have a prior telling us the coarse location of text instances in the image and their approximate scale, we could have adaptively chosen which regions to process and how to rescale them, thus significantly reducing the processing time. To estimate this prior we propose a segmentation-based network with an additional predictor, an output channel that predicts the scale of each text segment. The network is applied on a scaled down image to efficiently approximate the desired prior, without processing all the pixels of the original image. The approximated prior is then used to create a compact image containing only text regions, resized to a canonical scale, which is fed again to the segmentation network for fine-grained detection. We show that our approach offers a powerful alternative to fixed scaling schemes, achieving an equivalent accuracy to larger input scales while processing far fewer pixels. Qualitative and quantitative results are presented on the ICDAR15 and ICDAR17 MLT benchmarks to validate our approach.
Analysis of the Influence of Education APP on the Independent Learning Mode of Junior Middle School Students

In the context of the era of artificial intelligence, mobile learning methods are increasingly popular. Through literature research, it is found that with the deepening of subject research, more and more researchers have begun to explore the impact of mobile learning methods on disciplines, teacher teaching, teacher evaluation, etc., while exploring the educational APP for students' independent learning methods. The field research of influence is scarce. With the maturity of mobile learning technology, education APP occupies the main force of the mobile application market, and students' learning styles tend to diversify. As an auxiliary tool for education and teaching, APP helps students to acquire more extracurricular learning resources, and it also influences students' independent learning methods. Therefore, this paper uses the method of comparative analysis to analyze the influence of educational APP on the self-learning mode of junior high school students, and clearly concludes that education APP has a certain impact on the self-learning mode of junior high school students, and deeply analyzes its influence on junior high school students. The factors of self-learning are followed by suggestions for management from software developers, managers, teachers and parents. Finally, it is intended to provide a new way of thinking and perspective for exploring the field of students' independent learning
InP photonic circuit for deep neural networks

We perform weight addition of four 10 Gbit/s channels employing a photonic integrated indium phosphide chip based on semiconductor optical amplifiers technology. We demonstrate classification of classes of Iris flowers with an accuracy of 91.6%.
KENDALA DAN TANTANGAN PENDIDIKAN BERBASIS TEKNOLOGI (TECHNOLOGY-BASED CONSTRAINTS AND EDUCATIONAL CHALLENGES)

Education will develop more and more, of course, supported by the existence of information and communication technology so that it becomes digital education that can support the advancement of the world of education. However, this technology-based education is still experiencing various obstacles and challenges. For example, remote areas that still lack the introduction of digital technology and networks so that there are delays in the development of technology-based education.
Semantic Enrichment of Linked Personal Authority Data: A Case Study of Elites in Late Imperial China

The study uses the Database of Names and Biographies (DNB) as an example to explore how in the transformation of original data into linked data, semantic enrichment can enhance engagement in digital humanities. In the preliminary results, we have defined instance-based and schema-based categories of semantic enrichment. In the instance-based category, in which enrichment occurs by enhancing the content of entities, we further determined three types, including: 1) enriching the entities by linking to diverse external resources in order to provide additional data of multiple perspectives; 2) enriching the entities with missing data, which is needed to satisfy the semantic queries; and, 3) providing the entities with access to an extended knowledge base. In the schema-based categories that enrichment occurs by enhancing the relations between the properties, we have identified two types, including: 1) enriching the properties by defining the hierarchical relations between properties; and, 2) specifying properties’ domain and range for data reasoning. In addition, the study implements the LOD dataset in a digital humanities platform to demonstrate how instances and entities can be applied in the full texts where the relationship between entities are highlighted in order to bring scholars more semantic details of the texts.