Reviewerly/exHarmony huggingface dataset:
A quantum secret sharing scheme without monitoring signal disturbance

In order to improve the noise tolerability of quantum secret sharing, we propose a quantum secret sharing scheme based on a round-robin differential-phase-shift quantum key distribution (RRDPS-QKD) protocol, named RRDPS-QSS scheme. In the presented scheme, Alice prepares two L-bit weak coherent pulses (trains), and encodes two random bit sequences on each pulse of the two trains through phase modulation. Bob and Charlie measure received quantum signals and obtain the interference clicks and interference positions within their own measurement environment. With the announcement of their interference positions by Bob and Charlie, Alice can compute the QSS's sifted key, while Bob (Charlie) cannot obtain the sifted key without the help of the others. Finally, Alice, Bob, and Charlie could obtain a secret key via reconciliation and privacy amplification after getting enough sifted keys. The numerical simulation results show that the proposed scheme can produce a secure key without monitoring signal disturbance in quantum channels, and has a higher noise tolerance in comparison with the QSS scheme based on the BB84 protocol. The key generation rate of the proposed RRDPS-QSS scheme decreases with two transmission distances. The longer train length is and lower misalignment the error probability the RRDPS-QSS scheme has, the better the key generation rate performance the QSS scheme has.
Crowdsourcing identities: On identity as an existential practice mediated by contemporary digital technology

The purpose of this article is to outline crowdsourcing identities as one way to think about humans’ making of identity when practiced in conditions mediated by contemporary digital technologies. It brings together human practices, theory and technology and draws on perspectives of existentialism, social onstructivism, technology and ideas about crowdsourcing. Humans’ making of identity is considered a practice that concerns continuous requests and answers about existential matters of being, becoming and belonging: Who am I? How do I appear to others? Who can I be and become? Where do I belong? In that sense, it is a relational and social practice that is essential to exist as someone — rather than as no one. However, contemporary digital technologies such as social media are not considered essential to this practice. Technologies are thought of as mediating conditions where, for instance, social interactions about existential matters, such as the making of identity, can be practiced in ways that extend on what were possible in predigital times. In multimodal, multidirectional, collaborative and networked ways humans can represent, communicate, gather information and also engage online crowds of others in continuous requests and answers about being, becoming and belonging; Who am I? How do I appear to others? Who can I be and become? And, where do I belong? To illustrate this, two brief narratives of Mary and Steve introduce the article. The making of identity, the duality of Self and Other, the gaze as a panopticon of others, identity and technology and ideas about crowdsourcing are presented and outlined, converging into the interpretive lens of crowdsourcing identities — one way to think about the making of identity when practiced in conditions mediated by contemporary digital technology.
New instances classification framework on Quran ontology applied to question answering system

Instances classification with the small dataset for Quran ontology is the current research problem which appears in Quran ontology development. The existing classification approach used machine learning: Backpropagation Neural Network. However, this method has a drawback; if the training set amount is small, then the classifier accuracy could decline. Unfortunately, Holy Quran has a small corpus. Based on this problem, our study aims to formulate new instances classification framework for small training corpus applied to semantic question answering system. As a result, the instances classification framework consists of several essential components: pre-processing, morphology analysis, semantic analysis, feature extraction, instances classification with Radial Basis Function Networks algorithm, and the transformation module. This algorithm is chosen since it robustness to noisy data and has an excellent achievement to handle small dataset. Furthermore, document processing module on question answering system is used to access instances classification result in Quran ontology.
The use of mobile-assisted virtual reality in fear of darkness therapy

Fear of darkness is a common psychological problem that may extent to a specific phobia if it is not treated well. Several intervention techniques related to fear and phobia using actual exposure therapy have been studied for decades, however, there were some constraints emerged when the therapist provides a real environment to overcome the patient's reaction to his/her specific fear. Virtual reality (VR) technology is an innovative tool providing a more immersive, secure, personal, and controlled virtual environment. Therefore, we developed a novel framework for treating the fear of darkness named Mobile-assisted Virtual Reality (MAVR). The purpose of this study was to evaluate the use of MAVR to treat fear of darkness based on usability, time consumption and its ability to decrease fear. We used the GOMS model as an interaction guidance between human and computer which aimed to facilitate the process of re-learning in mindset change and individual’s behavioral toward situation of darkness and night. Therefore, a comprehensive evaluation was conducted to measure the efficacy of the MAVR. We developed the usability assessment checklist to assess the feasibility and acceptability of the MAVR, and fear of darkness thermometer to measure the degree of fear. The Wilcoxon Signed Rank Test showed that the fear of darkness was significantly decreased after participants received the MAVR therapy (z=-3.550, p-value&lt;0.001). We found that the MAVR was very useful, easy to be used and acceptable for participants. In conclusion, this study highlights the efficacy of Mobile-assisted Virtual Reality in treating specific fear, and it seems that Virtual Reality technology has a promising benefit to be implemented for other fear or specific phobia and also used in other psychological treatment.
Sampling Operator to Learn the Scalable Correlation Filter for Visual Tracking

The correlation filter is suitable for tracking on account of its low computational complexity and promising performance. However, the number of available training samples is limited to the filter size, and the lack of samples leads to poor generalization. Moreover, spectral leakage degrades the filter quality. In this paper, we, therefore, propose a sampling operator for learning a scalable correlation filter in an enlarged window, whose size is larger than the object size. The scalable filter encodes the sparse frequency representation to reconstruct a larger filter with zeros outside of the object in the spatial domain. The sampling operator, which is composed of windowing and sampling operations, enables learning the scalable filter from a large window, and it suppresses spectral leakage. Our method was evaluated on the OTB-100, TC-128, and UAV-123 datasets and achieved the promising results in terms of precision and success rates.
The Effect of Cloud Workload Consolidation on Cloud Energy Consumption and Performance in Multi-Tenant Cloud Infrastructure

As energy consumption is becoming a problem in cloud data centers, cloud service providers have adopted different techniques to address this problem.One of the most attractive technique is virtual machine (VM) consolidation.Apart from reducing energy consumption in computing platforms, this technique has other advantages such as reduced infrastructure costs and ease of virtual machine management.However, VM consolidation, which does not recognize workload characteristics may, in the long run, increase energy consumption and lead energy wastage.This paper investigates the relationship between different VM workload types and server energy consumption in a multi-tenant datacenters.Experiments are conducted using well known CPU, I/O, memory and network intensive workload benchmark obtained from Phoronix Test Suite (PTS).Results obtained show that there is a noticeable difference in the amount of energy consumed when VMs run workloads, which dominate the various server physical resources.Secondly, consolidating homogeneous workloads is disastrous in terms of energy consumption and performance over heterogeneous workloads.The latter can further reduce energy consumption and achieve acceptable performance levels if an optimum workload mix is reached.
Teacher of Education for Family Life as an innovator in the reformed system

The analyses performed for this paper focused – among other things – on legal regulations concerning creative activities of a teacher-innovator and on various solutions in the area of curricula, methodology and organisational matters in regard to teaching, education and care at the new school. Pedagogical innovations implemented in everyday educational work involve, for example, replacing one methodological or organisational solution with another, expanding syllabuses and methodological solutions and enhancing the effectiveness of teaching and educational activities. These innovations help a teacher of education for family life to reinforce student creativity, develop an active attitude in students and prepare them for responsible participation in social life. Teachers’ of education for family life commitment, their creativity in thinking and actions and their thoughtfulness and professionalism are of great importance.
Checking integrity of data and recovery in the cloud environment

&lt;p&gt;Cloud computing provides many services to access them dynamically over the internet as per the user's demand. The data is growing in tremendous amount and it should be managed correctly where storage service proves efficient. The Data stored online can be hacked by the third party so to secure this data verifying integrity is essential. Because of the human error or natural disaster, the data may get deleted from the cloud servers. Therefore, there is a requirement of improving the existing algorithm to recover data efficiently. Many algorithms are proposed, but they lack in efficiency, computational cost, and reliability. The proposed system in this paper is having the feature of verifying data integrity using Identity Based Remote Data Integrity Checking and recovery using the XOR operation. When the data is unavailable to the user, the proposed system provides flexibility for the user to regain data from the remote server.&lt;/p&gt;
Special Issue on Biostatistics

In the framework of model-based clustering, a model allowing several latent class variables is proposed. This model assumes that the distribution of the observed data can be factorized into several independent blocks of variables. Each block is assumed to follow a latent class model (i.e., mixture with conditional independence assumption). The proposed model includes variable selection, as a special case, and is able to cope with the mixed-data setting. The simplicity of the model allows to estimate the repartition of the variables into blocks and the mixture parameters simultaneously, thus avoiding to run EM algorithms for each possible repartition of variables into blocks. For the proposed method, a model is defined by the number of blocks, the number of clusters inside each block and the repartition of variables into block. Model selection can be done with two information criteria, the BIC and the MICL, for which an efficient optimization is proposed. The performances of the model are investigated on simulated and real data. It is shown that the proposed method gives a rich interpretation of the data set at hand (i.e., analysis of the repartition of the variables into blocks and analysis of the clusters produced by each block of variables).
Reimagining the academic library: What to do next. Review article

This article reviews the conclusions of the author’s 2016 book, Reimagining the academic library and considers changes in scholarly communication and academic libraries that have taken place since its publication. Recommendations for alterations in the practice of individual libraries are provided. The problem of created integrated community-controlled open infrastructure is considered at length, especially the collective action problem that the library community must overcome.
ANALISIS HUBUNGAN ANTARA MOTIVASI BELAJAR DENGAN HASIL BELAJAR SISWA PADA SEKOLAH DASAR NEGERI 3 ALLAKUANG KECAMATAN MARITENGNGAE KABUPATEN SIDENRENG RAPPANG

Abstract: This study aims to find out and analyze the relationship between learning motivation and student learning outcomes at State Elementary School 3 Allakuang, Maritengngae District, Sidenreng Rappang Regency. This research is quantitative research by distributing questionnaires to 44 students. This study used a simple linear regression analysis method to determine the effect of learning motivation on student learning achievement, and correlation coefficient to measure the relationship between one variable and another variable. The results showed that motivation had a significant influence on student learning achievement and there was a positive relationship between learning motivation towards student learning outcomes, but the relationship was weak because the results of the discussion showed a relationship between learning motivation and student learning outcomes of 0.10 or 10%.
COMPARISON OF BLOCK EXPECTATION TIME FOR VARIOUS CONSENSUS ALGORITHMS

Context. We consider security properties of decentralized blockchain-based consensus protocols. The object of research is blockconfirmation time for users to get assurance that their transaction will not be reverted.Objective. The goal of the paper is to analyze double-spend attacks on the different blockchain-based systems and compareresulting probabilities of attacker’s success.Method. We presented two models for two types of attacks on the Ouroboros protocol (for the general and covert adversaries).The models allow calculating the exact number of slots needed to achieve the required level of security. It was shown that theOuroboros protocol allows achieving the required security level with significantly shorter confirmation period in comparison withBitcoin. We estimated minimal number of confirmation blocks and compare estimation time for Bitcoin, GHOST and Ouroborosprotocols. As a measure of comparison, we considered transaction confirmation time for which the probability of a double-spendattack is less than 0.1%. We use different standard probability distribution and different properties of Markov chains and RandomWalks to get comparison of estimated security properties of Bitcoin blockchain against three different models of Bitcoin doublespend attack. The splitting attack based on the model where resources of honest participants are divided to compete different chainsis applied to Bitcoin and GHOST consensus protocols. Properties of Markov chains and Random Walks are also applied to obtainsecurity estimations for the Ouroboros protocol.Results. We developed methods to get specific numbers for average block confirmation time for Ouroboros protocol. Wecompared minimal number of confirmation blocks needed to ensure a high security for considered protocols: Bitcoin, GHOST andOuroboros.Conclusions. The obtained results allow determination of security bounds for the Bitcoin, GHOST and Ouroboros consensusprotocols. Users of the practically deployed blockchain systems may get specific parameters for a given assurance level.
Analytically Computable Symmetric Quantum Correlations

Abstract One of the greatest challenges in developing the resource theory of a quantum feature is to establish an analytically computable quantifier, which directly limits the practicability of such quantifiers. Here, analytic quantifiers of both the symmetric quantum discord (SQD) and the symmetric measurement‐induced nonlocality (SMIN) in a bipartite system of qubits are studied on the basis of the quantum skew information. It is shown that the SMIN of any two‐qubit system and the SQD of bipartite “X”‐type states and block‐diagonal states can be analytically determined. In addition, the SQD and the SMIN are invariant with an attached quantum state. The validity of our analytical expressions is further illustrated numerically on the basis of several randomly generated density matrices.
A Hierarchical k-Anonymous Technique of Graphlet Structural Perception in Social Network Publishing

The structural information of social network data plays an important role in many fields of research. Therefore, privacy-preserving social network publication methods should preserve more structural information, such as the higher-order organizational structure of complex networks (graphlets/motifs). Therefore, how to preserve the graphlet structure information in a social network as much as possible becomes a key problem in social network privacy protection. In this paper, to address the problem of excessive loss of graphlet structural information in the privacy process of published social network data, we proposed a technique of hierarchical k-anonymity for graphlet structural perception. The method considers the degree of social network nodes according to the characteristics of the power-law distribution. The nodes are divided according to the degrees, and the method analyzes the graphlet structural features of the graph in the privacy process and adjusts the privacy-processing strategies of the edges according to the graphlet structural features. This is done, in order to meet the privacy requirement while protecting the graphical structural information in the social network and, improving the utility of the data. This paper uses two real public data sets, WebKB and Cora, and conducted experiments and evaluations. Finally, the experimental results show that the method proposed in this paper can concurrently provide the same privacy protection intensity, better maintain the social network’s structural information and improve the data’s utility.
Machine learning predicts responses to conceptual tasks using eye movements

Research has shown that students' responses to conceptual questions correlate with their eye movements.However, to what extent is it possible to predict whether a particular learner might answer a question correctly by monitoring their eye movements in real time?To answer this question, we used spatialtemporal eye-movement data from about 400 participants, as well as their responses to four conceptual physics questions with diagrams.Half of these data were used as a training set for a machine learning algorithm (MLA) that would predict the correctness of students' responses to these questions.The other half of the data were used as a test set to determine the performance of the MLA in terms of the accuracy of the prediction.We will discuss the results of our study with specific attention to the prediction accuracy of the MLA under different conditions.
Development of Conceptual Learning Model Based on Various Stability Features

To promote efficient and flexible learning, a conceptual learning model can be developed using the differences between the stable features of various concepts, thereby allowing scalable and continuous learning concepts to be obtained from a small number of labeled samples. By using a stable feature expressed as a vector to represent each concept, the conceptual learning model can distinguish the representative feature of each concept, thus increasing the interpretability of the model. In addition to adjusting the mapping relationship between concept instances and concept stability features, constraints on the differences between stable features of different concepts are also introduced into the model. Because this constraint can improve the sensitivity of the instance features to the stable features of each concept, the proposed model has the ability to learn from a small number of samples. In this paper, sub-networks of the same structure were applied to learn each concept, and unified learning methods were used to achieve scalability and the ability to easily manage new concepts. By training the proposed model using the MNIST dataset, the model demonstrates that prediction accuracy of 96% can be achieved as a result of using 100 labeled training samples for each concept. Furthermore, the proposed model not only demonstrated an improved learning ability for small samples, but it was also found to considerably reduce the training time, with each concept requiring only 18 s on average. Thus, the small-sample and continuous learning capabilities of the proposed model enable the construction of a conceptual space that promotes improved knowledge representation and complex scene understanding.
Compound PDE-Based Additive Denoising Solution Combining an Improved Anisotropic Diffusion Model to a 2D Gaussian Filter Kernel

A second-order nonlinear anisotropic diffusion-based model for Gaussian additive noise removal is proposed.The method is based on a properly constructed edgestopping function and provides an efficient detail-preserving denoising.It removes additive noise, overcomes blurring effect, reduces the image staircasing and does not generate multiplicative noise, thus preserving boundaries and all the essential image features very well.The corresponding PDE model is solved by a robust finite-difference based iterative scheme consistent with the diffusion model.The method converges very fast to the model solution, the existence and regularity of which is rigorously proved.
Lemmatisation for under-resourced languages with sequence-to-sequence learning: A case of Early Irish

Lemmatisation, which is one of the most important stages of text preprocessing, consists in grouping the inflected forms of a word together so they can be analysed as a single item, identified by the word’s lemma, or dictionary form. It is not a very complicated task for languages such as English, where a paradigm consists of a few forms close in spelling; but when it comes to morphologically rich languages, such as Russian, Hungarian or Irish, lemmatisation becomes more challenging. However, this task is often considered solved for most resource-rich modern languages irregardless of their morphological type. The situation is dramatically different for ancient languages characterised not only by a rich inflectional system, but also by a high level of orthographic variation, and, what is more important, a very little amount of available data. These factors make automatic morphological analysis of historical language data an underrepresented field in comparison to other NLP tasks. This work describes a case of creating an Early Irish lemmatiser with a character-level sequence-to-sequence learning method that proves efficient to overcome data scarcity. A simple character-level sequence-to-sequence model trained during 34,000 iterations reached the accuracy score of 99.2 % for known words and 64.9 % for unknown words on a rather small corpus of 83,155 samples. It outperforms both the baseline and the rule-based model described in [21] and [76] and meets the results of other systems working with historical data.