Reviewerly/exHarmony huggingface dataset:
Efficient Supergraph Search Using Graph Coding

This paper proposes a method for searching for graphs in the database which are contained as subgraphs by a given query. In the proposed method, the search index does not require any knowledge of the query set or the frequent subgraph patterns. In conventional techniques, enumerating and selecting frequent subgraph patterns is computationally expensive, and the distribution of the query set must be known in advance. Subsequent changes to the query set require the frequent patterns to be selected again and the index to be reconstructed. The proposed method overcomes these difficulties through graph coding, using a tree structured index that contains infrequent subgraph patterns in the shallow part of the tree. By traversing this code tree, we are able to rapidly determine whether multiple graphs in the database contain subgraphs that match the query, producing a powerful pruning or filtering effect. Furthermore, the filtering and verification steps of the graph search can be conducted concurrently, rather than requiring separate algorithms. As the proposed method does not require the frequent subgraph patterns and the query set, it is significantly faster than previous techniques; this independence from the query set also means that there is no need to reconstruct the search index when the query set changes. A series of experiments using a real-world dataset demonstrate the efficiency of the proposed method, achieving a search speed several orders of magnitude faster than the previous best.
Quantum smoother for open quantum systems driven by quantum jump-diffusion processes

In this paper, we develop a quantum smoothing theory to the class of open quantum systems driven by quantum jump-diffusion processes. A quantum smoothing theory, which is derived in a different senses by different researchers, has been applied for diffusive quantum Markovian systems, however, they only deal with quantum Wiener noises. We show how to derive the quantum smoother for quantum dynamical systems driven by quantum jump-diffusion noises.
Mining Key Users of Microblog Topics based on Trust Model

Microblog topics play an important role in public opinion.The effective identification of key users in microblog topics is key for microblog public opinion mining.In this paper, we select topic-related microblog messages based on topic features.Candidate key users are selected according to the microblog topic user graph and edge weight.Key users of microblog topics are ranked according to the users' trust values.Experiments on Sina microblog topic datasets show that our method can mine high quality key users that lead to the formation of microblog topics in the early stage.
Towards Comprehensive Recommender Systems: Time-Aware Unified Recommendations Based on Listwise Ranking of Implicit Cross-Network Data

The abundance of information in web applications make recommendation essential for users as well as applications. Despite the effectiveness of existing recommender systems, we find two major limitations that reduce their overall performance: (1) inability to provide timely recommendations for both new and existing users by considering the dynamic nature of user preferences, and (2) not fully optimized for the ranking task when using implicit feedback. Therefore, we propose a novel deep learning based unified cross-network solution to mitigate cold-start and data sparsity issues and provide timely recommendations for new and existing users. Furthermore, we consider the ranking problem under implicit feedback as a classification task, and propose a generic personalized listwise optimization criterion for implicit data to effectively rank a list of items. We illustrate our cross-network model using Twitter auxiliary information for recommendations on YouTube target network. Extensive comparisons against multiple time aware and cross-network baselines show that the proposed solution is superior in terms of accuracy, novelty and diversity. Furthermore, experiments conducted on the popular MovieLens dataset suggest that the proposed listwise ranking method outperforms existing state-of-the-art ranking techniques.
Study of Different Evapotranspiration Models under Polyhouse and Open Environment in Tarai Region of Uttarakhand

The average of mean monthly ET0 estimated under polyhouse by FAO PM (benchmark) model was 39.44 mm, but that of the FAO Penman, Hargreaves Stanghellini, Priestley-Taylor and FAO Radiation models were 38.37, 18.18, 37.80, 48.17 and 53.87 mm respectively. Whereas, the average of mean monthly ETo estimated under open environment by FAO PM (benchmark) model was 116.34 mm, but that of the FAO Penman, Hargreaves Stanghellini, Priestley-Taylor and FAO Radiation models were 119.33, 133, 126.41, 113.17 and 117.37 mm respectively. The FAO Penman and Hargreaves model are found to be most and least appropriate models for estimating daily ET0 under polyhouse. Whereas, FAO Radiation and Stanghellini model observed to be most and the least appropriate models in an open environment for estimating daily ET0 under polyhouse for the Pantnagar Tarai condition of Uttarakhand.
Assessment of trust building mechanisms of e-commerce: a discourse analysis approach

Nowadays the Internet occupies the primary place in many people’s lives. It gives people many different opportunities including online shopping. The deep understanding of all the elements of trust building mechanisms is essential in order to guarantee future prosperous development of the e-commerce sphere as trust seems the key point of e-commerce success. The current study aims to assess trust building mechanisms of online shopping, namely customers’ comments, using the linguistic tool. By adopting the discourse analysis methodology this paper explores the language units used in the comments. First of all, the importance of feedbacks (customers’ comments) was assessed by means of self-compiled questionnaire and the results of the analyses indicate that feedbacks were proved to be significant for customers while forming the buying intention. Then qualitative data was collected from Amazon website. Customers’ comments were collected, systematized and grouped according to the specification of the comment. The following groups were singled out: attitude, duration of usage, quality, price, purpose of usage, function. Comments were divided into positive and negative as well and later on analyzed by means of discourse analysis. Two types of comments were pointed out, namely explanation and evidence (photo). Special features of online comments were pointed out.
Research on the conceptual teaching in Design College

Conceptual knowledge and applied skill have always been two contradictory aspects of teaching art design. How to balance the relation between them is an often new topic in design teaching. Colleges should encourage students to explore and think deeply in conceptual subjects, which will greatly benefit students' development of creativity and comprehensive ability. Based on conceptual teaching, this thesis will try to explain how to combine traditional teaching with conceptual teaching together to provide a reference for classroom teaching and conceptual teaching of art design courses.
Bayesian deep learning: A model-based interpretable approach

Deep learning is considered to be a model-free, end-to-end, and black-box approach. It requires numerous data samples instead of expert knowledge on the target domain. Hence, it does not specify the mechanism and reasons for its decision making. This aspect is considered a critical limitation of deep learning. This paper introduces another viewpoint, namely Bayesian deep learning. Deep learning can be installed in any framework, such as Bayesian networks and reinforcement learning. Subsequently, an expert can implement the knowledge as the graph structure, accelerate learning, and obtain new knowledge on the target domain. The framework is termed as the deep generative model. Conversely, we can directly introduce the Bayesian modeling approach to deep learning. Subsequently, it is possible to explore deep learning with respect to the confidence of its decision making via uncertainty quantification of the output and detect wrong decision-making or anomalous inputs. Given the aforementioned approaches, it is possible to adjust the “brightness” of deep learning.
Effects of Field Trip and Peer Tutoring Instructional Strategies on Students’ Science Process Skills Acquistion in Basic Science and Technology in Junior Secondary Schools in Osun State

The study investigated the effects of field trip and peer tutoring instructional strategies (as against the conventional method of teaching) on secondary school students’ science process skills (observation, classification, communication, measurement, inference and prediction) acquisition in Basic Science and Technology in Junior Secondary Schools (JSS) in Osun State. These were with a view to provide information on usability of the strategies to enhance the students’ development of science process skills through Basic Science and Technology lesson in Osun State classrooms. The study employed the pretest, post-test control group quasi experimental research design. The population for the study comprised all junior secondary school students in Osun State. The study sample consisted of 315 Junior Secondary II (JS II) students in nine intact classes selected from nine schools using simple random sampling technique. The instrument used for data collection was Science Process Skills Performance Test (SPSPT). Data collected were analysed using mean, standard deviation and Analysis of Covariance (ANCOVA). The results of the study showed that there was significant effect of the treatment (use of field trip and peer tutoring) on science process skills of secondary school students as follow: Observation (F=1.030, p&lt;0.05), classification (F=3.529, p&lt;.05), communication (F=1.326, p&lt;0.05), measurement (F=6.623, p&lt;0.05), inference (F=12.310, p&lt;0.05) and prediction (F=21.210, p&lt;0.05); with peer tutoring leading in observation, classification, communication and measurement skills of students in Basic Science and Technology with x̄=30.6000, x̄=31.3429, x̄=24.9429 and x̄=25.3714 while students exposed to field trip strategy, exhibited higher students’ inference and prediction skills in Basic Science and Technology with x̄=22.9714 and x̄=27.4847 respectively. The study concluded that field trip and peer tutoring instructional strategies are effective and innovative teaching methods in improving students’ science process skills acquisition in Basic Science and Technology in secondary schools in Osun State, Nigeria.
Classification of Historical Anatolian Coins with Machine Learning Algorithms

To find out which period the historical coins belong to requires a number of scientific procedures that archaeologists or experts can do. These operations can often be time-consuming and demanding operations. From this point on, in this study, the automatically classification of historical coins by using machine learning methods is discussed.Being able to use machine learning methods to classify historical coins can help experts and can become an analysis tool without the need for scientific tests for non-experts. For this purpose, some physical properties of different coins used in Anatolian geography were collected and classified by various machine learning methods named SVM, Random Forest, Bagging, and Decision Trees. Also, two different missing values strategies are deployed in conjunction with each chosen method. Based on our findings, random forest method together with imputing missing values with mean gives an acceptable results with the accuracy rate of %71, although there are some limitations such as high rate of missing values and working with a small dataset.
A Neural Network Model to Include Textual Dependency Tree Structure in Gender Classification of Russian Text Author

The research proposes the neural network methods to include a textual dependency tree structure in classification tasks of Russian texts. Author profiling task of gender identification was chosen to test the models, and two corpora used in experiments: based on a crowdsource, and in-person polling. The first approach is based on a long short-term memory (LSTM) layers, and developed graph embedding algorithm. The second one is based on a graph convolution network and LSTM. Two syntactic parsers were used to obtain dependency trees from the texts. Input data was represented in different forms: morphological binary vectors, FastText vectors, and their combination. The developed models result was compared to the state-of-the-art, that is neural network model based on a convolutional and LSTM layers. Finally, we demonstrate that including textual dependency tree structure to input feature space improves f1-score of gender classification task on 4% for the RusPersonality dataset, and 7% for the crowdsource dataset in average. The developed models resulting f1-score is 84% and 83%, respectively.
Proposal and Evaluation of Visual Analytics Interface for Time-Series Data Based on Trajectory Representation

This paper proposes a visual analytics (VA) interface for time-series data so that it can solve the problems arising from the property of time-series data: a collision between interaction and animation on the temporal aspect, collision of interaction between the temporal and spatial aspects, and the trade-off of exploration accuracy, efficiency, and scalability between different visualization methods. To solve these problems, this paper proposes a VA interface that can handle temporal and spatial changes uniformly. Trajectories can show temporal changes spatially, of which direct manipulation enables to examine the relationship among objects either at a certain time point or throughout the entire time range. The usefulness of the proposed interface is demonstrated through experiments.
EASY-M: Evaluation System for Multilingual Summarizers

Automatic text summarization aims at producing a shorter version of a document (or a document set).Evaluation of summarization quality is a challenging task.Because human evaluations are expensive and evaluators often disagree between themselves, many researchers prefer to evaluate their systems automatically, with help of software tools.Such a tool usually requires a point of reference in the form of one or more human-written summaries for each text in the corpus.Then, a systemgenerated summary is compared to one or more human-written summaries, according to selected measures (also called metrics).However, a single metric cannot reflect all quality-related aspects of a summary.In this paper we present the EvAluation SYstem for Multilingual Summarization (EASY-M), which enables the evaluation of system-generated summaries in 23 languages with several quality measures, based on comparison with their human-generated counterparts.The system also provides comparative results with two built-in baselines.The EASY-M system is freely available for the NLP community 1 .
Exploring the Digital Divide between Pre-clinical Teachers and Students in an Integrated Medical Curriculum from a Malaysian Private University

Background:The effectiveness of the integration of technology within the classroom setting has been well documented, however the use of this technology is still far from reaching its true potential.Although the presence of digital divides between teacher and staff documents, the extent and nature of the divide is not well defined.This could have limited the potential exploitation of technology in teaching and learning, especially in a medical school with an integrated curriculum.Our study intended to identify the student's and teachers' perceptions of benefits of using technology in teaching and learning in a medical school and also investigate the nature of the gap that is present between teachers and students.Methodology: This cross-sectional study was conducted through a census during which we had 212 pre-clinical medical students and 30 lecturers engaged in the teaching of these students in an integrated curriculum.A pre-validated self-administrative questionnaire survey was conducted for Usage of ICT in learning and Perception of the Use of E-education Tools and Implementation of E-courses.The significance of the association between teachers and students was tested using Kruskal-Wallis test.Results: Over 3 hours in a day was spent by teachers and students on desktop and smartphone for TLA, indicating that students are mobile learners engaging in learning activities in their time and place of choice.60-65% of students preferred using YouTube for their leaning activates, and around 50% of the teacher used this platform in teaching and related activities.Usages of journals as found to be high among teachers (55%) while the use of other popular learning applications.This study also showed that both teachers and students strongly agreed that they enjoy using technology, indicating the acceptance and willingness to explore its potential.Conclusion: The study shows the use of ICT is prevalent between both medical teachers and staff; the difference was only in the degree of usage and also both the groups enjoyed the use of technology.It was also clear that both medical teachers and students viewed the use of ICT to be integral in delivering the curricula.
Vocational Students’ Perceptions of Mobile Assisted Language Learning (MALL) Materials

&lt;p&gt;&lt;strong&gt;Abstract:&lt;/strong&gt; Digital resources have been proven able to assist English language learners in improving their interaction skills, widening their vocabulary range, and enhancing their reading comprehension. Despite their benefits, digital tools can also create distractions for students. This article reports on the result of a survey to 150 vocational students on Mobile Assisted Language Learning (MALL) materials. The students agreed that MALL materials were more authentic (67%) and more accessible (59%) than traditional learning materials. The use of MALL materials could trigger classroom interactions (64%) and facilitated learning (56%). The implementation of MALL materials allowed students to get immediate feedback from the teachers (73%).&lt;/p&gt;&lt;strong&gt;Abstrak:&lt;/strong&gt;&lt;em&gt; &lt;/em&gt;Sumber daya digital telah terbukti dapat membantu pelajar bahasa Inggris dalam meningkatkan keterampilan interaksi, memperluas jangkauan kosa kata, dan meningkatkan pemahaman bacaan mereka. Selain bermanfaat, alat digital juga dapat menggangu proses belajar. Artikel ini melaporkan hasil survei terhadap 150 siswa kejuruan terkait dengan implementasi materi berbasis gawai atau Mobile Assisted Language Learning (MALL). Ditemukan bahwa siswa sepakat bahwa materi MALL lebih otentik (67%) dan lebih mudah diakses (59%) daripada materi pembelajaran tradisional. Penggunaan MALL dapat memicu interaksi kelas (64%) dan memfasilitasi pembelajaran (56%). Dengan menerapkan materi berbasis gawai atau MALL materials di kelas, para siswa mengakui bahwa mereka bisa mendapatkan umpan balik langsung dari para guru (73%).
A Combined Single Trace Attack on Global Shuffling Long Integer Multiplication and its Novel Countermeasure

Advanced collision-based single trace attacks which can be applied on simple power analysis resistant scalar multiplications become virtual threat on elliptic curve cryptosystems recently as their practical experimental results are increasingly reported in the literature. Since such attacks are based on detecting collisions of data dependent leakage caused by underlying long integer multiplications, so-called global shuffling countermeasure which breaks such collision correlation by independently randomizing the execution order of unit operations such as single precision multiplication and carry propagation, is considered as promising countermeasure if theoretical randomness of shuffling order is guaranteed. In this paper, we firstly analyze the practical security of the global shuffling long integer multiplications by exhibiting a combined single trace attack on software implementations on an ARM Cortex-M4 microcontroller. Our combined attack consists of a simple power analysis for revealing random permutation vectors which enables later collision-based single trace attack. First we demonstrate how to reveal random permutation vectors for carry propagation process of whole global shuffling long integer multiplications within a single power trace by simple power analysis accompanied with straightforward substitution of power consumption samples. Then we perform collision-based single trace attacks after rearranging the order of subtraces for unit carry propagations based on revealed permutation vectors. Since the vulnerability to simple power analysis is originated from the if-statement for selection of proper entries of the permutation vectors, we propose a novel countermeasure which eliminates such selection with simple addition and modulus operation and also demonstrate practical result achieving regularity in power trace patterns.
Face Threats in Threads: Assessing the Responses to Impoliteness in Facebook Comments on 1MDB

Impoliteness has become common among online users and appears to be consented by netizens. This study seeks to investigate the reaction to impoliteness from the perspective of face-threat witnesses (FTWs) in Facebook comments. Twelve news posts on Facebook regarding the 1MDB scandal in 2015 were selected, and impolite comment threads that were reactions to the news were extracted. Fifty-two threads were found to contain impolite comments targeted at non-participants of this interaction, thus corresponding to the characteristics of face-threat witnesses. Dobs and Blitvich’s (2013) model for participant response options, Culpeper’s (2011, 2016) Conventionalised Impoliteness Formulae (CIF) and Bousfield’s (2007) list of defensive counter-strategies were used to analyse the responses. Impolite responses by the FTWs were found to be atypical. Denying the opposition either via being offensive or defensive subjugated the preference in the findings, though offensive appeared more prominently. Apart from deny opposition, corroborate opposition, and react, the current study also discovered new categories for the response options which did not fit in any of the categories, hence labelled as Distinct Features. The FTWs not only sanctioned impoliteness, but initial impoliteness in their responses, despite being ‘other-directed'. Keywords: Impoliteness; Face-threat witness; Responses; Facebook; Online Comments