Reviewerly/exHarmony huggingface dataset:
Preparing Preservice Teachers to Use Mobile Technologies

The use of mobile technologies to enhance 21st century learning is increasing in K-12 schools and teacher education institutions. Thus, there is a need to effectively prepare preservice teachers to use mobile technologies in their future classrooms. This chapter explores the effective use of mobile technologies in teacher preparation in ways that are transferred to K-12 teaching and learning. It goes on to look at two major organizations: the university and partner school involved in the preparation of preservice teachers. Additionally, the purposes of incorporating information and communication technologies in teacher preparation as identified by Davis are explored to understand how mobile technologies align with these purposes. Common challenges of using mobile technologies in teacher preparation are also presented.
Research on Expressway Network Tolling Platform Technology Based on GIS+BIM

Architecture of expressway network tolling platform is put forward based on GIS+BIM, real-time tolling fee calculation for each vehicle in networked and prewarning of equipment and facilities as well as data analysis and mining are studied, the basic network diagram of GIS+BIM is presented, and the database and data dictionary are researched. Platform performance requirements such as high efficiency of calculation of fee, transparency of business management, data identification, high fault tolerances of storage, and rapid response are studied. The research of this paper will provide certain reference for the construction of expressway tolling system.
Faktor-Faktor yang Berpengaruh dalam Mendapatkan Pekerjaan bagi Lulusan Statistika IPB dengan Menggunakan Metode CHAID

Unemployment is one of the economic problems in Indonesia. Judging from the level of education that was completed there were unemployment from the level of college graduates. This encourages the level of competition in getting jobs to be more stringent, so that college graduates (bachelor of Statistics in IPB) must have the preparation of various factors to maintain the quality of their graduates. The quality of college graduates can be seen from the length of time waiting to get a job. This study aims to determine the influential factors in getting a job for graduates of the IPB Statistics degree, so that the CHAID method can be used in this study. The results of CHAID's analysis in this study in the form of tree diagrams using α = 10% explained that the factors influencing the waiting period variables were sex, internship, and the ability to master statistical software, where the accuracy value generated by the classification model was 79.3 %.
Human Linguistic Perception of Distances for Location-Aware Systems

Location referencing relative to landmarks or between two points of interest is often presented by navigation systems (e.g., GPS, Google Maps) in quantitative terms (e.g., 100m, 2km, etc.). However, humans refer to distances between points of interests in linguistic forms, such as very close, far, almost there, nearby, etc. When location information is presented to humans in quantitative terms, they often reprocess the quantities into linguistic terms and articulate it in linguistic labels because quantitative articulations are not directly in line with the natural human cognition. Therefore, this research seeks to evaluate the possibility of applying perceptive computing to reprocess quantitative location references from landmarks or two points of interest into linguistic labels easily understood by humans. A comparative analysis between the perception of quantitative distances and similar physical distances in an environment familiar to the subjects has been carried out, and there is a clear disparity between the perceptions in these two contexts.
Learning Relational Representations with Auto-encoding Logic Programs

Deep learning methods capable of handling relational data have proliferated over the last years. In contrast to traditional relational learning methods that leverage first-order logic for representing such data, these deep learning methods aim at re-representing symbolic relational data in Euclidean spaces. They offer better scalability, but can only numerically approximate relational structures and are less flexible in terms of reasoning tasks supported. This paper introduces a novel framework for relational representation learning that combines the best of both worlds. This framework, inspired by the auto-encoding principle, uses first-order logic as a data representation language, and the mapping between the original and latent representation is done by means of logic programs instead of neural networks. We show how learning can be cast as a constraint optimisation problem for which existing solvers can be used. The use of logic as a representation language makes the proposed framework more accurate (as the representation is exact, rather than approximate), more flexible, and more interpretable than deep learning methods. We experimentally show that these latent representations are indeed beneficial in relational learning tasks.
STUDY COPING STRATEGIES AT THE TERTIARY LEVEL OF EDUCATION

Appears in: INTED2019 Proceedings Publication year: 2019Pages: 8650-8658ISBN: 978-84-09-08619-1ISSN: 2340-1079doi: 10.21125/inted.2019.2160Conference name: 13th International Technology, Education and Development ConferenceDates: 11-13 March, 2019Location: Valencia, Spain
Quality of theoretization and culture formation of individual self-study in the continuous education system

We define the direction and conditions for improving the quality of theorization and culture formation of individual self-study, detail and visualize the constructs and models of culture formation of individual self-study in the continuous education system. The practice of theorizing the culture formation of individual self-study in constructs of modern scientific knowledge and the use of pedagogical models facilitating the understanding and presentation of the quality of forming a culture of individual self-study and the specifics of accounting for adaptive acmepedagogical features of solving problems of personality development in the system of continuing education is detailed. The possibilities of forming a culture of individual self-study through the use of the construct “pedagogical conditions” (pedagogical conditions for improving the quality of forming a culture of individual self-study) are clarified and theorized. We determine and unify pedagogical conditions for improving the quality of forming a culture of individual self-study in the continuous education system by taking into account the age and scientific character, normal distribution of abilities and health of students in the continuous education system and other components of modern scientific and pedagogical research and scientific knowledge in pedagogy. We also prove the consistency of the deterministic model of theorization and culture formation of individual self-study in the continuous education system. We determine the features and prospects of measuring the quality of theorization of culture formation of individual self-study in the continuous education system.
Scalable Gaussian Process Using Inexact Admm for Big Data

Gaussian process (GP) for machine learning has been well studied over the past two decades and is now widely used in many sectors. However, the design of low-complexity GP models still remains a challenging research problem. In this paper, we propose a novel scalable GP regression model for processing big datasets, using a large number of parallel computation units. In contrast to the existing methods, we solve the classic maximum likelihood based hyper-parameter optimization problem by a carefully designed distributed alternating direction method of multipliers (ADMM). The proposed method is parallelizable over a large number of computation units. Simulation results confirm the benefits of the proposed scalable GP model over the state-of-the-art distributed methods.
OPEN ONLINE E-LEARNING RESOURCES AT EPRAKSIS.NO AS PREPARATION FOR HANDS-ON LABORATORY PRACTICE

Appears in: INTED2019 Proceedings Publication year: 2019Page: 6693 (abstract only)ISBN: 978-84-09-08619-1ISSN: 2340-1079doi: 10.21125/inted.2019.1628Conference name: 13th International Technology, Education and Development ConferenceDates: 11-13 March, 2019Location: Valencia, Spain
Securely Aggregating Testimonies with Threshold Multi-key FHE

Many data management applications, such as setting up Web portals, managing enterprise data, managing community data, and sharing scientific data, require integrating data from multiple sources. Each of these sources provides a set of values and different sources can often provide conflicting values. To discover the true values, data integration systems should resolve conflicts. In this paper, we present a formal probabilistic framework in the expert/authority setting. Each expert has a partial and maybe imperfect view of a binary target vector $$\varvec{b}$$ that an authority wishes recovering. The goal of this paper consists of proposing a multi-party aggregating function of experts’ views to recover $$\varvec{b}$$ with an error rate as small as possible. In addition, it is assumed that some of the experts are corrupted by an adversary $$\mathcal {A}$$ . This adversary controls and coordinates the behavior of the corrupted experts and can thus perturb the aggregating process. In this paper, we present a simple aggregating function and we provide a formal upper-bound over of the output vector error expectation in the worst case, i.e. whatever the behavior of the adversary is. We then propose to securely implement this aggregating function in order to preserve the privacy of experts’ views. A natural secure implementation could be achieved with recent powerful cryptographic tools, i.e. Threshold Multi-key Fully Homomorphic Encryptions schemes (TMFHE). Finally, trade-off between the time complexity and the number of interaction rounds are proposed.
Analysis of Multiscale Quantum Harmonic Oscillator Algorithm Based on a New Multimode Objective Function

The wavefunction is an important element of the multiscale quantum harmonic oscillator algorithm (MQHOA). To verify the physical model and the multimode optimization performance of the MQHOA for multimode optimization in this paper, we define a multidimensional harmonic-Gaussian potential function. When the wavefunction of the MQHOA for multimode optimization and the probability amplitude of an ammonia molecule are compared, the ground-state wavefunction can reflect the probability distribution of the two-state ammonia molecule. We obtain the extrema of the proposed function using the Hessian matrix and optimize the proposed function with the multimode algorithm. The experiments show that the multimode algorithm can determine the extrema of the proposed function with appropriate parameters. Changes in the proposed function barriers have little effect on the optimization ability of the multimode algorithm. The optimization ability of the multimode algorithm is determined by its own wavefunction.
Patterns of Emotional Argumentation in Twitter Discussions

The paper presents the results of an ongoing research of Twitter discussions on inter-ethnic conflicts. The case of Biryulevo bashings is already thoroughly analyzed by the research group; in this paper, we develop a qualitative method for finding patterns of emotional (irrational) argumentation, the patterns that would link emotion and argumentation. Our pilot empirical study is based on 306 tweets of 7 top non-media users; the tweets are analyzed qualitatively for the spectrum of emotions for conflict cases – sadness/sorrow, surprise/fear, anger/indignation, as well as for irony and call to action for complete reconstruction of the chains of emotions. We find that there are two patterns in tweeting strategies – broadcasting the development of the conflict and ‘broadcasting’ emotions of individuals that reflect collective ones. The total number of expressed emotions was counted, followed by reconstructing chains of emotions chronologically. There are three groups of patterns: starting and ending with neutral informing; starting and ending with one certain emotion, no clear pattern with different emotions in the start and in the end. Despite we find only a few sustainable patterns, we argue that further computational research can be done to connect the type of a top user and his/her emotional patterns.
User-Managed Access (UMA) 2.0 Grant for OAuth 2.0 Authorization

This specification defines a means for a client, representing arequesting party, to use a permission ticket to request an OAuth 2.0access token to gain access to a protected resource asynchronouslyfrom the time a resource owner authorizes access.
Logic for exact real arithmetic

Continuing earlier work of the first author with U. Berger, K. Miyamoto and H. Tsuiki, it is shown how a division algorithm for real numbers given as a stream of signed digits can be extracted from an appropriate formal proof. The property of being a real number represented as a stream is formulated by means of coinductively defined predicates, and formal proofs involve coinduction. The proof assistant Minlog is used to generate the formal proofs and extract their computational content as terms of the underlying theory, a form of type theory for finite or infinite data. Some experiments with running the extracted term are described, after its translation to Haskell.
Foundations, Properties, and Security Applications of Puzzles: A Survey

Cryptographic algorithms have been used not only to create robust ciphertexts but also to generate cryptograms that, contrary to the classic goal of cryptography, are meant to be broken. These cryptograms, generally called puzzles, require the use of a certain amount of resources to be solved, hence introducing a cost that is often regarded as a time delay---though it could involve other metrics as well, such as bandwidth. These powerful features have made puzzles the core of many security protocols, acquiring increasing importance in the IT security landscape. The concept of a puzzle has subsequently been extended to other types of schemes that do not use cryptographic functions, such as CAPTCHAs, which are used to discriminate humans from machines. Overall, puzzles have experienced a renewed interest with the advent of Bitcoin, which uses a CPU-intensive puzzle as proof of work. In this paper, we provide a comprehensive study of the most important puzzle construction schemes available in the literature, categorizing them according to several attributes, such as resource type, verification type, and applications. We have redefined the term puzzle by collecting and integrating the scattered notions used in different works, to cover all the existing applications. Moreover, we provide an overview of the possible applications, identifying key requirements and different design approaches. Finally, we highlight the features and limitations of each approach, providing a useful guide for the future development of new puzzle schemes.
An Analysis on the Operation Level of the Dry Dock for Warship Maintenance Using Priority Queue

Dry dock is an important facility for the maintenance of the hull of a ship. As the ship size of ROK Navy has become larger, the lack of large dry dock is getting worse. Therefore, it is necessary to improve the maintenance policy or construct an additional dry dock. Meanwhile, ROK Navy is using the dry dock for two types of maintenance: ordinary scheduled maintenance and prioritized urgent maintenance. This paper analyzes the operation level of large dry dock using the priority queuing model with two classes, which suggests insightful results for maintenance policy of ROK Navy as well as an analytic model of the operation level of the dry dock.
Perencanaan Arsitektur SI/TI menggunakan Framework TOGAF (Studi Kasus : Dinas Perpustakaan dan Kearsipan Daerah Kota Tasikmalaya)

The planning of system and information technology architecture is one effort which is needed by the organization so that the IS/IT which applied are fitting the needs of business organization. The Tasikmalaya City Department of Library and Archive has not done the IS/IT architecture planning nor fully applied the IS/IT. Therefore, a system and information technology planning is required to identify what system and technology which needed by the Tasikmalaya City Department of Archive and Library. The framework used in composing the IS/IT planning was TOGAF with the ADM as the architecture development method. The TOGAF's stages used in this research were started from Preliminary Phase to Phase E: Opportunities and Solutions. The result of this IS/IT architecture planning is a blueprint which in sync with the business needs for Tasikmalaya City Department of Archive and Library which consisted of business architecture, information system architecture and technology architecture.
Relationship 6 task KKNI for student’s scientific publications

The KKNI is a reference framework in the tertiary level to improve the quality of human resources by community needs analysis. The primary objective of this study was to increase the interest of scientific publications of students of mathematics education through mini research, and its relationship with six tasks in the KKNI applied in Unimed. This research is a descriptive study, the population in this study was the fifth-semester students of mathematics education in Medan State University. The sample in the study consisted of one class in the research methodology course chosen randomly. Data collection was carried out through interest questionnaires and mini-research-focused response questionnaires and their relevance to the implementation of the six KKNI tasks. The results obtained show a significant increase in interest of students to publish the results of mini research. As many as 83% of students are interested in publishing their scientific papers. 10 draft articel ready for publish. With the connection of the implementation of the 6 KKNi tasks that ended in the project in the form of writing a scientific document from the results of mini-research, students became more confident and confident. The interconnected and gradual tasks of the KKNI make it easier for students to conduct research and publish them in a systematic and structured manner.
A Data Integrity Verification Scheme with Secure Deduplication in Smart Grid Cloud Storage

In order to solve the problem of data deduplication and data integrity in smart grid cloud storage, a data integrity verification scheme which can support security deduplication is proposed in the context of smart grid cloud storage. This scheme refers to an efficient and safe deduplication method based on the bloom filter, realizes the quick verification of the user's hash value and initialization value. In addition, the secure deduplication method is combined with S—PDP integrity verification mechanism, data segmentation and random sampling strategy, to realize the security deduplication and integrity verification of smart grid clients. By using erasure correction code, users can repair the damaged data. The analysis results show that this scheme can effectively reduce the computing cost and communication cost while ensuring that the cloud storage data of smart grid can be secure deduplicated and integrity verified.
Supporting Video Queries on Zero-Streaming Cameras.

Low-cost cameras grow rapidly, producing colossal videos that enable powerful analytics but also stress network and compute resources. An unexploited opportunity is that most of the videos remain cold without ever being queried. For resource efficiency, we advocate for these cameras to be zero-streaming: they capture videos directly to their cheap local storage and only communicate with the cloud when analytics is requested. To this end, we present a system that spans the cloud and cameras. Our key ideas are twofold. When capturing video frames, a camera learns accurate knowledge on a sparse sample of frames, rather than learning inaccurate knowledge on all frames; in executing one query, a camera processes frames in multiple passes with multiple operators trained and picked by the cloud during the query, rather than one pass processing with operator(s) decided ahead of the query. On diverse queries over 15 videos and with typical wireless network bandwidth and low-cost camera hardware, our system prototype runs at more than 100x video realtime. It outperforms competitive alternative designs by at least 4x and up to two orders of magnitude.
Recommendations for M-Government Implementation in Developing Countries: Lessons Learned from the Practitioners

Researchers argue the potential of mobile technologies to bridge challenges of e-government in developing countries. Despite the demand, research on the design and implementation of m-government is still scarce. The current literature hardly provides comprehensive recommendations for implementing such services in developing countries. This paper aims to bridge the gap by examining the challenges of m-government services from the m-government practitioners of developing countries. Also, it explores the solutions applied by the practitioners to address the challenges. To achieve these goals, online questionnaire and interviews techniques were used to collect data. MAXQDA tool was used to analyze raw data, and we applied the PESTELMO method to categorize the challenges. Results show that designers are facing problems related to requirement engineering, stakeholder management, budget allocation and technology standards. We provide recommendations to improve m-government designs in the future to ensure accessibility and sustainability of services. The recommendations are applicable to government organizations and practitioners of mobile public services.
An efficient approach for sarcasm detection in tweets using polarity flip

Sentiment analysis is the qualitative method that uses natural language processing, text analysis, and computational linguistics. The goal of sentiment analysis is to determine if a specific passage in the text shows positive, negative or neutral sentiment towards the subject. Social media platforms, like Twitter, offer a wide ability that allows users to express their thoughts by employing figurative language devices such as sarcasm to achieve different communication purposes. Dealing with such kind of content represents a big challenge for computational linguistics. Due to these difficulties and the inherently tricky nature of sarcasm, it is generally ignored during social network analysis. As a result, the outcome of such analysis is affected adversely. Also, nowadays, Customers use twitter as a platform to express their emotions and opinions about social issues, products or services. Lexicon based methods of text mining can at times fail to recognize sarcasm used by customers online. This has direct implications for companies using text mining on online content to identify new customers, address customer problems, and reduce customer turn rates or for any other CRM (Customer Relationship Management) activities. Thus, sarcasm detection poses to be one of the most critical problems which we need to overcome while trying to yield high accuracy insights from abundantly available data. This work considers sarcastic tweets (including those based on products) and proposes an effective approach to detect sarcasm. The system utilizes 21 features based on context, contrast, and emotions. The results show that sarcastic tweets inclined to have more polarity flips than Non-sarcastic tweets. Also, it is found that MLP and the Random Forest classifier tend to perform better than other classifiers with an accuracy of 94%.
Quantum teleportation of a three-qubit entangled states via W-class states

We propose a novel and efficient scheme for quantum teleportation a three-qubit entanglement state via two sets W-class states. The local measurement on Alice's particle using two three von-Neumann projective and Bob reconstruct using unitary transformation. This quantum teleportation scheme is perfect, i.e. the success of probability and fidelity can reach one.