Reviewerly/exHarmony huggingface dataset:
BLENDED LEARNING MEDIATED INSTRUCTION IN ENGLISH CLASS: STUDENT ENGAGEMENT

There have been many discussions on the concepts of student engagement in learning during Blended learning mediated instruction. Student engagement tended to be discussed in academic activities during Pandemic learning. This study explored the students’ engagement in a project-based blended learning classroom. It investigated how the project-based blended learning model motivates student engagement. A case study approach was adopted that involved ten English department students. Questionnaires and interviews were used to collect the data. Deci and Ryan’s theory (1985) opted to analyze the data. The results contribute to higher education pedagogy by providing insights into the use of project-based blended learning models in engaging students.
NILAI PENDIDIKAN KARAKTER PADA NOVEL JANSHEN KARYA RISA SARASWATI SERTA RELEVANSINYA DALAM PEMBELAJARAN BAHASA INDONESIA SMA

Character education is the most important thing in human life to form character for students. The values ​​of character education can be taken through literary works, one of which is novels. This study aims to analyze the values ​​of character education in the novel Janshen by Risa Saraswati and their relevance in learning Indonesian in high school. The research used is a qualitative approach with a descriptive type of research. The data collection technique in this study was using the note-taking technique. The results of this study are the values ​​of character education in the novel Janshen by Risa Saraswati. The values ​​of religious character education are shown in three aspects, namely, obedient to worship, gratitude, and prayer. In addition, there are also educational values ​​for tolerance, creativity, social care, curiosity, discipline, independence, and a love of reading. And it can be relevant as teaching material for Indonesian language education for class XI in accordance with basic competence (KD) 3.20 Analyze the message of the two fiction books (novels and collections of poetry) that are read. Because, the novel contains the values ​​of character education.
Are Facemasks Safe in COVID Crisis?

Everything has changed with the onset of COVID. This pandemic has transformed societal norms. People have developed different and changed ways of living. In these pandemic times, face masks have become an important item, deployed as a major way by which people can protect themselves and others from coronavirus infection. Though covering the face is not a new culture but now the facemask has attained a new practical meaning. Face masks are an important component of personal protective equipment to prevent potentially infectious respiratory infections especially the latest coronavirus. Face masks have become a fashion item and people are choosing specific coloured masks for use which suits along with their clothes. But the question is whether the prolonged use of facemasks is safe? Until now, there are not many comprehensive investigations pointing out the adverse health effects associated with facemask use. Though wearing a mask has become a part of routine life, its extended use may lead to health risks. The present short communication points out the available scientific reports revealing the positive and negative aspects of facemask usage.
Speaker Diarization with Deep Learning Techniques

Speaker diarization is a task to identify the speaker when different speakers spoke in an audio or video recording environment. Artificial intelligence (AI) fields have effectively used Deep Learning (DL) to solve a variety of real-world application challenges. With effective applications in a wide range of subdomains, such as natural language processing, image processing, computer vision, speech and speaker recognition, and emotion recognition, cyber security, and many others, DL, a very innovative field of Machine Learning (ML), that is quickly emerging as the most potent machine learning technique. DL techniques have outperformed conventional approaches recently in speaker diarization as well as speaker recognition. The technique of assigning classes to speech recordings that correspond to the speaker's identity is known as speaker diarization, and it allows one to determine who spoke when. A crucial step in speech processing is speaker diarization, which divides an audio recording into different speaker areas. In-depth analysis of speaker diarization utilizing a variety of deep learning algorithms that are presented in this research paper. NIST-2000 CALLHOME and our in-house database ALSD-DB are the two voice corpora we used for this study's tests. TDNN-based embeddings with x-vectors, LSTM-based embeddings with d-vectors, and lastly embeddings fusion of both x-vector and d-vector are used in the tests for the basic system. For the NIST-2000 CALLHOME database, LSTM based embeddings with d-vector and embeddings integrating both x-vector and d-vector exhibit improved performance with DER of 8.25% and 7.65%, respectively, and of 10.45% and 9.65% for the local ALSD-DB database
AN IN-DEPTH ANALYSIS OF THE DOUBLE BASS-STAGE FLOOR CONTACT

While double bassists claim the importance of compliant floors for producing a warm and nuanced sound, the scarce research focusing on this topic has left few conclusions. In the mean time stage floors are constructed with great spread in design and conviction. In the present project floors of several halls were analysed, including the Oslo Concert Hall and Oslo's new Opera House. It was found that in the range where the double bass radiates poorly, i.e., below 100 Hz, and in particular below its Helmholtz resonance at ca 60 Hz, a favourable coupling can take place when the floor is compliant. In such cases the velocity transfer from the instrument's bridge to the floor (via the corpus and end pin) may often rise significantly above zero dB. The coupling, bass to floor, was also seen to affect the bridge mobility and thus the playing properties of the instrument. In the same frequency range the power transfer was observed to boost from about 3 to 40%. It remains to investigate how noticeable this effect is to the audience. Our measurements indicate, however, that a coupling to the stage floor can make an audible difference to the player.
Securing Financial XML Transactions Using Intelligent Fuzzy Classification Techniques

The eXtensible Markup Language (XML) has been widely adopted in many financial institutions in their daily transactions. This adoption was due to the flexible nature of XML providing a common syntax for systems messaging in general and in financial messaging in specific. Excessive use of XML in financial transactions messaging created an aligned interest in security protocols integrated into XML solutions in order to protect exchanged XML messages in an efficient yet powerful mechanism. However, financial institutions (i.e. banks) perform large volume of transactions on daily basis which require securing XML messages on large scale. Securing large volume of messages will result performance and resource issues. Therefore, an approach is needed to secure specified portions of an XML document, syntax and processing rules for representing secured parts. In this research we have developed a smart approach for securing financial XML transactions using effective and intelligent fuzzy classification techniques. Our approach defines the process of classifying XML content using a set of fuzzy variables. Upon fuzzy classification phase, a unique value is assigned to a defined attribute named “Importance Level”. Assigned value indicates the data sensitivity for each XML tag. The research also defines the process of securing classified financial XML message content by performing element-wise XML encryption on selected parts defined in fuzzy classification phase. Element-wise encryption is performed using symmetric encryption using AES algorithm with different key sizes. Key size of 128-bit is being used on tags classified with “Medium” importance level; a key size of 256-bit is being used on tags classified with “High” importance level. An implementation has been performed on a real-life environment using online banking system in Jordan Ahli Bank one of the leading banks in Jordan to demonstrate its flexibility, feasibility, and efficiency. Our experimental results of the system verified tangible enhancements in encryption efficiency, processing-time reduction, and resulting XML message sizes. Finally, our proposed system was designed, developed, and evaluated using a live data extracted from an internet banking service in one of the leading banks in Jordan. The results obtained from our experiments are promising, showing that our model can provide an effective yet resilient support for financial systems to secure exchanged financial XML messages.
Mapping spatial cognition with computers

In this chapter, fractal geometry, digital video technology and research regarding spatial cognition are combined to create new methods for describing mental models. Pointing behaviours recorded on videotape, and digitised sketch maps, provide general purpose means to derive quantitative measures for the study of cognitive maps. Fractal dimension (D) estimates of cognitive maps were computed to study individual differences, and the dynamic properties of mental models. Estimates of D for cognitive maps were found to be sensitive to an apparent asymmetry of spatial processes, but to exhibit low correlations with tests of personality and intelligence. Results suggest that the type of “imagination” used in cognitive maps may not be assessed effectively by the principal factors common to psychometric measures of cognition.
Comparison of Bayesian nonparametric density estimation methods

In this paper, we propose a nonparametric Bayesian approach for Lindsey and penalized Gaussian mixtures methods. We compare these methods with the Dirichlet process mixture model. Our approach is a Bayesian nonparametric method not based solely on a parametric family of probability distributions. Thus, the fitted models are more robust to model misspecification. Also, with the Bayesian approach, we have the entire posterior distribution of our parameter of interest; it can be summarized through credible intervals, mean, median, standard deviation, quantiles, etc. The Lindsey, penalized Gaussian mixtures, and Dirichlet process mixture methods are reviewed. The estimations are performed via Markov chain Monte Carlo (MCMC) methods. The penalized Gaussian mixtures method is implemented via Hamiltonian Monte Carlo (HMC). We show that under certain regularity conditions, and as n increases, the posterior distribution of the weights converges to a Normal distribution. Simulation results and data analysis are reported.
ROOM ACOUSTICS IN THE SCENE II IN THE NEW OSLO OPERA AND THE RIDEHUSET - TWO VARIABLE ACOUSTICS COUPLED SPACE VENUES

Two coupled volume halls for music has been accomplished in Oslo the last year where we have been acoustical consultants. Scene II (two) is a 440 seat multi purpose hall in the new Oslo Opera. The ‘Ridehuset’ is a refurbishment of an old building to a rehearsal room and 230 seat concert hall for the Staff Band of the Norwegian Armed Forces (FSMK). Both auditoria have coupled volumes and variable absorption. In spite of being very different rooms and shapes, they share some acoustical properties we would like to address.
Symbolic Leaf Representation in Decoupled Search

Star-Topology Decoupled Search has recently been introduced in classical planning. It splits the planning task into a set of components whose dependencies take a star structure, where one center component interacts with possibly many leaf components. Here we address a weakness of decoupled search, namely large leaf components, whose state space is enumerated explicitly. We propose a symbolic representation of the leaf state spaces via decision diagrams, which can be dramatically smaller, and also more runtime efficient. We further introduce a symbolic version of the LM-cut heuristic, that nicely connects to our new leaf representation. We show empirically that the symbolic representation indeed pays off when the leaf components are large.
STUDIO ACUSTICUM - A CONCERT HALL WITH VARIABLE VOLUME

In the town of Pitea in the north of Sweden a new multipurpose concert hall, Studio Acusticum, was inaugurated in 2007. The acoustics of the hall is designed by Tunemalm Akustik with some help from ...
Fuzzy c-means clustering and elliptic curve cryptography using privacy preserving in cloud

Cloud computing is the distribution of computing devices which reduce the cost for IT infrastructure. In this projected approach, the databases are measured to collecting method generate the transitional datasets. These datasets acquire the facts increase to pick the responsive data to the encryption and decryption procedure, the responsive data preferred procedure depend upon the entry value. The facts increase is integrated to get the superior bound limitation for the combined maintaining outflow. Responsive data to the elliptic curve cryptography (ECC) system to encrypt the data to isolation procedure. Encrypted data storage system is utilised to protected cloud data standards. Encrypting every transitional data sets are neither competent nor rate effectual one. From the trial outcome, the isolation defending charge of transitional datasets can be appreciably condensed by our method above obtainable ones where the entire datasets are encrypted.
Unsupervised Sentence Embedding Using Document Structure-Based Context

We present a new unsupervised method for learning general-purpose sentence embeddings. Unlike existing methods which rely on local contexts, such as words inside the sentence or immediately neighboring sentences, our method selects, for each target sentence, influential sentences from the entire document based on the document structure. We identify a dependency structure of sentences using metadata and text styles. Additionally, we propose an out-of-vocabulary word handling technique for the neural network outputs to model many domain-specific terms which were mostly discarded by existing sentence embedding training methods. We empirically show that the model relies on the proposed dependencies more than the sequential dependency in many cases. We also validate our model on several NLP tasks showing 23% F1-score improvement in coreference resolution in a technical domain and 5% accuracy increase in paraphrase detection compared to baselines.
Total functions in QMA

The complexity class $${\textsc {QMA}}$$ is the quantum analog of the classical complexity class $${\textsc {NP}}$$ . The functional analogs of $${\textsc {NP}}$$ and $${\textsc {QMA}}$$ , called functional $${\textsc {NP}}$$ ( $${\textsc {FNP}}$$ ) and functional $${\textsc {QMA}}$$ ( $${\textsc {FQMA}}$$ ), consist in either outputting a (classical or quantum) witness or outputting NO if there does not exist a witness. The classical complexity class total functional $${\textsc {NP}}$$ ( $${\textsc {TFNP}}$$ ) is the subset of $${\textsc {FNP}}$$ for which it can be shown that the NO outcome never occurs. $${\textsc {TFNP}}$$ includes many natural and important problems. Here we introduce the complexity class total functional $${\textsc {QMA}}$$ ( $${\textsc {TFQMA}}$$ ), the quantum analog of $${\textsc {TFNP}}$$ . We show that $${\textsc {FQMA}}$$ and $${\textsc {TFQMA}}$$ can be defined in such a way that they do not depend on the values of the completeness and soundness probabilities. We provide examples of problems that lie in $${\textsc {TFQMA}}$$ , coming from areas such as the complexity of k-local Hamiltonians and public key quantum money. In the context of black-box groups, we note that group non-membership, which was known to belong to $${\textsc {QMA}}$$ , in fact belongs to $${\textsc {TFQMA}}$$ . We also provide a simple oracle with respect to which we have a separation between $${\textsc {FBQP}}$$ and $${\textsc {TFQMA}}$$ .
Distributed Relationship Mining over Big Scholar Data

In this paper, we propose a system infrastructure to construct the big scholar data as a large knowledge graph, discover the meta paths between the entities and calculate the relevancy between entities in the graph. The core infrastructure is established on the secured and private Amazon Elastic Compute Cloud(Amazon EC2) platform. The infrastructure maintains the data evenly across the repositories, processes the data parallel by utilizing open source Spark framework, manages computing resources optimally by utilizing YARN and Hadoop HDFS, and discovers the relationship distributedly between different types of entities. We incorporate four relationship discovery tasks including citation recommendation, potential collaborator discovery, similar venue measurement and paper to venue recommendation on top of this infrastructure. For relationship mining tasks, we propose a mixed and weighted meta path (MWMP) method to explore the potential relationship between different types of entities. To verify the accuracy and measure parallelization speedup of our algorithm, we set up clusters through Amazon EC2 platform.
Human Activity Recognition using Fourier Transform Inspired Deep Learning Combination Model

Background &amp; Objective: This paper proposes a Fourier transform inspired method to classify human activities from time series sensor data. Methods: Our method begins by decomposing 1D input signal into 2D patterns, which is motivated by the Fourier conversion. The decomposition is helped by Long Short-Term Memory (LSTM) which captures the temporal dependency from the signal and then produces encoded sequences. The sequences, once arranged into the 2D array, can represent the fingerprints of the signals. The benefit of such transformation is that we can exploit the recent advances of the deep learning models for the image classification such as Convolutional Neural Network (CNN). Results: The proposed model, as a result, is the combination of LSTM and CNN. We evaluate the model over two data sets. For the first data set, which is more standardized than the other, our model outperforms previous works or at least equal. In the case of the second data set, we devise the schemes to generate training and testing data by changing the parameters of the window size, the sliding size, and the labeling scheme. Conclusion: The evaluation results show that the accuracy is over 95% for some cases. We also analyze the effect of the parameters on the performance.
News Article Teaser Tweets and How to Generate Them

Sanjeev Kumar Karn, Mark Buckley, Ulli Waltinger, Hinrich Schütze. Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers). 2019.
Revisiting Quick Response (QR) Code Technology: Corporate Perspectives

Quick response code (QRC) technology is facing various impediments to its adoption and usage. It has an uncertain future amidst an evolving landscape of technology and social tools available to companies. This study examines QRC's utilising marketing executives to gain current insights on its present use as well as future direction. A mixed methods survey was used to capture qualitative and quantitative responses from corporate marketing executive perspectives about QRC. Executives view QR codes primarily as a technology to increase both strategic advantage and communication advantage with customers. The findings suggest QRC will evolve as an important technology to support future corporate marketing and sales efforts.
Predictive Analytics of Hyper-Connected Collaborative Network

The foundation of the infrastructure of a collaborative network for ubiquitous connectivity will employ hyper-connected technologies in smart and sustainable cities. Typically, there are millions of items for processing and analytics on the massive generated data. The predictive analytics are indispensable for such volumes of which there are many drifts in data structures and contents. In order to make better decisions and future planning of ubiquity, a model, and correspondence implementation are designed and developed. It brings decision-making to the expected boundary of collaboration for different performance indexes. The selected method finds cause-and-effect between data to predict the optimum responses to incoming events. The core of approach focuses on Event-Condition-Action rules to build decision trees, which helps further planning. The method can summarize complexity via effective recommended decisions to local experts and analysts.
Session types and subtyping for orchestrated interactions

In the setting of the π-calculus with binary sessions, we aim at relaxing the notion of duality of session types by the concept of retractable compliance developed in contract theory. This leads to extending session types with a new type operator of “speculative selection” including choices not necessarily offered by a compliant partner. We address the problem of selecting successful communicating branches by means of an operational semantics based on orchestrators, which has been shown to be equivalent to the retractable semantics of contracts, but clearly more feasible. A type system, sound with respect to such a semantics, is hence provided. The introduction of subtyping when interactions are orchestrated naturally leads to explicit subtyping, where coercions are functors on orchestrators. Besides, priority-governed selection policies (either at type- or process-level) are investigated in order to get rid of nondeterministic behaviours but those of the partner processes of the interactions.
Quantitative comparison of structural and alteration genesis relations of Masahim and Bidkhan-volcano calderas: Located in northern strip of Dehej-Sardu'ieh, Kerman, Iran

The origin of hydrothermal alterations in different facies of calderas can be exogenous or endogenous, depending on the age of volcanism and the kind of structures in an area. Naturally, most alterations have proportion with faults and fractures which facilitate fluid penetration. Comparing different locations of areas with high concentration of fractures in studied volcanic areas of Masahim and Bidkhan calderas and investigating the alterations of central facies within current study suggests that there is the strong relation between argillic alteration (as supergenic and exogenous alteration) and lineaments whereas phylic alterations as more endogenous genesis alteration have less general relation with lineaments. In Masahim caldera, with younger volcanism age, the origin of clay minerals is mostly related with meteoric waters and its major mineral is kaolin. According to XRD experiments, minerals such as pyrophyllite and dickite (as endogenous clays) are not very common in Masahim caldera; most alterations in Masahim volcanic area are argillic with exogenous origin. On the other hand in Bidkhan caldera, as older volcano than Masahim caldera, despite the argillic presence, phylic alterations (with endogenous origin) are significantly observed as well. Conformity matrix of alteration map and upper bound of the lineament-index map in Masahim and Bidkhan areas shows conformity coefficients of 37.8 and 29 percent for argillic and 1.1 and 10.7 for phylic alterations, respectively. Positive relation between argillic alteration and lineament-index, and weaker relation between phylic alteration and the factor of lineaments in center of Masahim and Bidkhan calderas, alongside with closer relation between general alterations regime of Masahim area (mostly exogenous) and lineament-index, indicates and confirms stronger effects of faults in supergenic processes compared with hypogenic processes within these areas. Also, due to older age of volcanism activity in Bidkhan caldera (longer time of erosion action) and younger age of Masahim volcanism (shorter time of erosion action), current surface observable facies are deeper facies in Bidkhan caldera with more endogenous phylic alterations, while in Masahim caldera, more surface facies are mostly argillic exogenous alterations.