Reviewerly/exHarmony huggingface dataset:
ID-based deletion searchable encryption scheme

Searchable encryption technology frees up user's local storage resources and also facilitates user's search on files in the server. At present, general searchable encryption schemes are merely static searches and cannot dynamically manipulate data already stored in server. In order to solve this problem, this paper proposes a dynamic searchable encryption scheme based on bilinear pairing, which can delete the specified identity file. When delete operation is performed, rights authentication is performed on user who issues delete request, and malicious deletion of illegal users is effectively prevented. At the same time, the security of the scheme is proved. Based on the implementation of delete function, the scheme achieves security against the choose plaintext attack.
A Context-aware Architecture for Energy Saving in Smart Classroom Environments

In this paper, we present a novel context-aware architecture for a classroom environment, recognizing student activities and saving energy. In contrast to previous models which are mainly focused on either video or image, various sensor data such as temperature, humidity, and luminance are combined with a video sequence in the proposed context-aware architecture. More specifically, student activities are classified using the convolutional 3D network (C3D) model and classroom temperature and humidity are predicted using a long short term memory (LSTM) network. Then, the outputs of the C3D model and LSTM network are combined and fed into fully connected layers to produce control parameters such as turn on/off air conditioners/heaters/dehumidifiers. Experimental results show that the proposed context-aware framework can be used for energy saving in classroom environments.
The Linked Data Wiki: Leveraging Organizational Knowledge Bases with Linked Open Data

Building meaningful knowledge bases for organizations like enterprises, NGOs or civil services is still a labor intensive and therefore expensive work, although semantic wiki approaches are already adopted in organizational contexts and corporate environments. One reason is that exploiting knowledge from external sources like other organizational knowledge bases or Linked Open Data as well as sharing knowledge in a meaningful way is difficult due to the lack of a common and shared schema definition. Therefore, redundant work has to be carried out for each new context. To overcome this issue, we introduce Linked Data Wiki, an approach that combines the power of Linked Open Vocabularies and -Data with established organizational semantic wiki systems for knowledge management in order to leverage the knowledge represented in organizational knowledge bases with Linked Open Data. Our approach includes a recommendation system to link concepts of an organizational context to openly published concepts and extract statements from that concepts that leverage the concept definition within the organizational context. The inclusion of potentially uncertain, incomplete, inconsistent or redundant public statements within an organization’s knowledge base poses the challenge of interpreting such data correctly within the respective context.
Automatic Text Preprocessing for Intelligent Dialog Agents

The paper describes a new Text Preprocessing Pipeline based on a Hybrid approach which involve rule-based and stochastic approaches. The presented pipeline is part of a larger project titled Big Data for Multi-Agent Specialized System developed by Network Contacts in collaboration with University of Salerno and other institutional partners. The aim of the project is to build an Hybrid Question Answering System composed by sets of Dialog Bots able to process great volumes of data. Due to the importance of unstructured textual data, a particular focus of the project is on automatic processing of Text. The paper will describe the three main modules of the preprocessing pipeline, which involve a Style Correction Module, a Clitic Decomposition Module and a POS Tagging and Lemmatization Module.
Estimation of Emotion Type and Intensity in Japanese Tweets Using Multi-task Deep Learning

Accurate estimation of emotions in SNS posts plays an essential role in a wide variety of real world applications such as intelligent dialogue systems, review analysis for recommendations and so on. In this paper, we focus on developing accurate models for estimating types of emotions and their intensities in Japanese tweets by using multi-task deep learning. More concretely, three deep learning models for estimating intensities of emotions were extended to be able to predict the emotional types and their intensities at a time. The effectiveness of the developed models was confirmed through experiments using the database of Japanese tweets annotated with intensity scores of four types of emotions using best-worst scaling.
An Improved Training Sample Set Construction Method

Sample set construction is an important step in the super-resolution reconstruction algorithm based on dictionary learning, which has an important impact on the training of dictionary and the effect of image reconstruction. When the sample set was built, the similarity between the sample blocks was not taken into account, which resulted in the redundancy of the sample set, thus increasing the time overhead of the follow-up dictionary training. To solve this problem, this paper proposes an effective method for constructing sample sets. By setting a reasonable threshold value of Euclidean distance similarity, the proposed method can ensure that the constructed sample set have structural anisotropy and diversity. When the number of pre-set blocks is the same, the training time of dictionary is reduced to about 50% of the original method, and the quality of image reconstruction is improved.
Using Edmodo as assessment for learning to facilitate improvement of 21<sup>st</sup> century health literacy

In this 21st century era student health literacy is becoming increasingly important to improve and can see as an asset for students. Assessment for learning are viewed as powerful levers for improving student health literacy by giving feedback, so student as learners know their strengths and weaknesses and how to improve their learning. This study aimed to investigate Edmodo as Assessment for learning to assess student performance during the biology learning process. Data collection techniques used are teachers and students questionnaires, documentation, and students digestive system tasks based on health literacy indicators. The analytical technique used in this study is descriptive method. The result showed improvement students average score from task 1 to task 2 in every indicators that indicate by using Edmodo students can improve 4 dimension of health literacy indicators based on Partnership-21 Century Skills frameworks.
Efficient Proactive Secret Sharing for Large Data via Concise Vector Commitments

Proactive secret sharing has been proposed by Herzberg, Jarecki, Krawczyk, and Yung (CRYPTO'95) and is a powerful tool for storing highly confidential data. However, their scheme is not designed for storing large data and communication and computation costs scale linearly with the data size. In this paper we propose a variant of their scheme that uses concise vector commitments. We show that our new scheme, when instantiated with a variant of the Pedersen commitment scheme (CRYPTO'92), reduces computation costs by up to $$50\%$$ and broadcast communication costs by a factor of L, where L is the length of the commitment message vectors.
Virtual Reality (VR) Applications in Learning

This chapter illustrates the use of VR applications in professional development and introduces an application used to assist teachers, learning support assistants (LSAs), and teaching assistants (TAs) to better understand autistic children's behaviors while in the classroom. One of the challenges faced in classrooms is how to understand the autistic children's behaviors and empathize with them. The proposed VR application repurposes a different form of narrative of the world of a child on the autism spectrum in an immersive environment designed for educators. The VR application in this chapter uses recorded footage through 360-degree cameras and special effects powered by Unity. In a context where integration is a key to today's learning and education, the researchers believe that the use of VR to assist the teachers in empathizing with their learners' traits and conditions may be of great benefit to the learners' school experiences.
IT Governance Audit at Lampung University Using COBIT 5 Framework Focus on EDM Domain

Abstract The competitiveness of an organization depends on IT governance because it enables organizations to optimize the benefits of implementing IT. The process of IT governance in the university’s answer to the needs of the organization which will guarantee the certainty of value creation of information technology as well as an assurance of the return of information technology investments that have been invested. Currently, IT governance and implementation at the University of Lampung has been run fairly well, but there are some steep obstacles in its business processes. COBIT 5 is a standard for IT governance processes that can help an organization to manage, develop, and retain its assets. In this study, an information technology governance audit was conducted based on the focal framework of COBIT 5 EDM domains with case studies at the University of Lampung. Data collection methods used are data analysis, interviews, and questionnaires. From the audit process, it was found that Lampung University’s ability to manage information technology at EDM generally focuses on level 4, but the achievement in each process has not been fulfilled yet and has not achieved the expected results by University of Lampung.
MAR UX design principles for vocational training

Current scientific research on MAR UX design does not address all facets of user experience in a holistic manner. It only covers usability, value, and desirability of the fundamental elements of UX. No known current published research considers the adoptability design principles of UX in MAR. To address this need, this Paper proposed UX design principles for MAR vocational training application and apply it to the design and development of AR4Juan. AR4Juan is an android based MAR vocational skills training application for Senior High School students specializing in Computer System Servicing National Competency (CSS NC) 2. CSS NC2 is under Technical Vocational Livelihood (TVL) track of the K12 education program in the Philippines. The researchers used MAR UX design principles research process to formulate the proposed UX Design principles. The study found out that other literature's design principles only focus on usefulness, usability, and desirability of UX elements and does not consider the adoptability element. Adoptability principles are essential in ensuring the application's natural users usage increase. Thus, drives business success. AR4Juan is still in its prototype stage and needs to be evaluated in every key UX element to determine the degree of its usefulness, ease of use, user enjoyment and engagement and ease of access. Other researchers could adopt the proposed UX designed principles and improve it iteratively after every evaluation of the MAR application.
Quantum Bit Error Rate Analysis of the Polarization based BB84 Protocol in the Presence of Channel Errors

In the BB84 quantum key distribution (QKD) protocol, the communicating parties do a quantum bit error rate (QBER) test to determine whether there is an eavesdropper trying to gain information about the secret key. However, the QBER is not only influenced by the eavesdropper’s strategies, but also by the imperfections of the physical devices and the channel through which the quantum states propagate. We developed a simple channel model with error parameters describing the channel and the potential polarization switching in the transmitter, to see how those effects influence the QBER in a polarization-qubit BB84 implementation. Certain well-defined probabilistic channel models are compared to see which is responsible for the highest error probability.
A Method of Mining Spatial High Utility Co-location Patterns Based on Feature Actual Participation Weight

Spatial high utility co-location patterns mining takes pattern utility participation index as interest measure, but it still lacks standard method for calculating pattern utility participation index. This paper presents some reasonable definitions of spatial high utility colocation patterns based on the datasets of instance with utility value, such as Feature Actual Participation Weight, Feature Utility Participation Index and Pattern Utility Participation Index and many more. A basic algorithm BWHMA(high utility co-location patterns mining based on feature actual participation weight) is proposed. For improving the efficiency of algorithm BWHMA, one pruning algorithm FUPRA(based on feature utility participation rate) and one optimization algorithm FRPWA(based on feature actual participation weight) are put forward. Finally, a large number of comparative experiments have been done on both synthetic and real datasets. The experiments show that the proposed algorithms are feasible, correct and valid.
Social and Community Related Themes in Ontology Evaluation: Findings from an Interview Study

A deep exploration of what the term “quality” implicates in the field of ontology selection and reuse takes us much further than what the literature has mostly focused on, that is the internal characteristics of ontologies. A qualitative study with interviews of ontologists and knowledge engineers in different domains, ranging from biomedical field to manufacturing industry reveals novel social and community related themes, that have long been neglected. These themes include responsiveness of the developer team or organization, knowing and trusting the developer team, regular updates and maintenance, and many others. This paper explores such connections, arguing that community and social aspects of ontologies are generally linked to their quality. We believe that this work represents a significant contribution to the field of ontology evaluation, with the hope that the research community can further draw on these initial findings in developing relevant social quality metrics for ontology evaluation and selection.
Development of android-based chemistry learning media oriented towards generic science skills

This study aims to describe the production stages and the eligibility value of Android-based learning media oriented towards students' generic science skills on the concept of colloids. This study uses Research and Development method with three stages, namely analysis, design, and development. Analysis stage produces conceptual connectivity and generic science skill indicators in concept presentation strategies. Design stage produces flowchart and storyboard, and development stage produces Android-based learning media with conceptual characteristics presented through materials and directional questions to develop generic science skills, equipped with visualization in the form of videos, pictures, and texts. The result of validity test conducted by chemists and media experts is declared valid with the value of rcalculate = 0.8-0.9. The result of limited trials conducted to chemical education students shows that 90,76% of them agree and 9.24% disagree. These results indicate that Android-based learning media oriented towards generic science skills is feasible to use in learning the concept of colloids.
Parallel Multi-View Graph Matrix Completion for Large Input Matrix

We propose a method for parallel multi-view graph matrix completion for the prediction of ratings in recommender systems. The missing ratings are computed based on both the similarity matrix in addition to a rating matrix. The rating matrix is sparse and some items might not have any rating information available. The similarity matrix can be calculated from different item attributes available from ecommerce websites. As the input matrix becomes large, the need for more computationally efficient matrix completion increases. The main contribution of this paper is to show speed-up in calculating the missing ratings by using multi-threaded programming. Simulation results are based on the large input matrix and show reduction in RMSE for the case of cold start prediction.
New Understanding in Academic Science

The present paper is a compilation of extractions from author's works.Those works were performed along different lines of research, each line reflecting some relevant side of the surrounding world.The review is focused on the world around us and on our understanding of this world.While writing the present paper, author tried to outline the disadvantages of the contemporary scientific picture of the world.Those disadvantages mainly stem from the presently adopted scientific method in which almost all explanations of physical phenomena are based on hypotheses.Gradually, the arbitrariness of adopted hypotheses gets forgotten, and fictional explanations get establish as the real structures of the world.In the present paper, the electromagnetic and gravitational interactions are treated from the position of the no-hypothesis approach.The latter approach consists in the study of phenomena as they are, in the measurement of the various properties of natural objects, and in the establishment of relations between them.The gained results form a new understanding of the world's phenomena which will allow using them in humans' practical activity.Moreover, the new understanding of the world permits anticipation of future results without recurring to hypotheses.Some unveiled sides of the surrounding world are also analyzed, together with the problems the human society presently faces.In conclusion, author summarizes the main shortcomings of the contemporary academic science which have caused public distrust in obtained results.Author would like to emphasize here that science can only earn trust in society via reliability and trustworthiness of its reached results.Author hopes that this publication will prove interesting for a wide range of readers.
Moments of the Count of a Regular Expression in a Heterogeneous Random Sequence

We focus here on the distribution of the random count N of a regular expression in a multi-state random sequence generated by a heterogenous Markov source. We first briefly recall how classical Markov chain embedding techniques allow reducing the problem to the count of specific transitions in a (heterogenous) order 1 Markov chain over a deterministic finite automaton state space. From this result we derive the expression of both the mgf/pgf of N as well as the factorial and non-factorial moments of N. We then introduce the notion of evidence-based constraints in this context. Following the classical forward/backward algorithm in hidden Markov models, we provide explicit recursions allowing to compute the mgf/pgf of N under the evidence constraint. All the results presented are illustrated with a toy example.
H<inline-formula> <tex-math notation="LaTeX">$\infty$ </tex-math> </inline-formula> Optimal Control-Based Robust Pose Estimation in Light Field Three-Dimensional Display

Naked eye augmented reality technology has the capability of satisfying the immersion feeling. Therefore, light field three-dimensional (3-D) display technology has received considerable attention. In this paper, a novel pose estimation method in light field 3D display based on H∞ optimal control is proposed. Compared with the current pure hardware platform method, the proposed method releases hardware resources such as the graphic processing unit and avoids numerous parameter adjustments. The proposed method comprises three key parts. First, we calculate the normal of the rigid objects and apply it to obtain the normal estimation. Second, we calculate H∞ of the normal estimation result on the Hardy space, which is the maximum singular value of the rational function matrix parsed in the right half plane of the complex plane. Finally, we use the optimal result of H∞ for pose estimation and transformation. The experiments are carried out on the virtual and real classical datasets acquired by Kinect, Mian, and Clutter. The experiments show that the proposed method can be used to obtain high-quality display effects with low cost and high efficiency.
Single Image Dehazing using Positive Correlation under Gradient Constraint

We propose an accurate and efficient dehazing method. First of all, we obtain the positive relationship between the minimum channel of a hazy image and the corresponding depth map. Based on this relationship and the gradient constraint, the depth map is estimated. And then, in a hazy image, according to the derivation that the atmosphere light relates to the brightest pixels, quad-tree subdivision algorithm is introduced to achieve the region with maximum intensity which can be further utilized to calculate the atmosphere light. Finally, the haze-free image is restored using the traditional atmospheric scattering model. Experimental results demonstrates that the proposed method can not only restore the high-grade image but also have a lower time consumption compared to the state-of-the-art methods.
Saint: An API-Generic Type-Safe Interpreter

Typed functional programming allows us to write interesting programs without sacrificing type safety. Programs that expose their API to an open world, however, are faced with the problem of dynamic type checking. In Haskell, existing techniques that address this problem, such as Typeable and Dynamic, are often closed and difficult to extend. We have constructed an extensible Haskell library for describing APIs using annotated type representations. As a result, API calls can be interpreted in a type-safe manner without extra programming effort. In addition, the user has full control over the universe of allowed types, which helps to catch misconceptions in an early stage. We have applied our technique to connect a real-world DSL (GRACe) to a JavaScript GUI.
TEN OBSERVATIONS ON WHY INCIDENT RESPONSE NEEDS YOUR ATTENTION

Few areas within the security domain are more critical than incident response. Unfortunately, incident response—similar to business continuity—tends to be overlooked or under-prioritized given day-to-day requirements on the CISO’s time. Ignore incident response at your own peril.
Block-based Temporal Metric for Video Quality Assessment

Perceptual video quality assessment (VQA) is an integral part of various video applications as it enables quality-control for videos delivered to end-users. Video Multi-method Assessment Fusion (VMAF) was recently proposed as a full reference VQA model that combines quality-aware features to predict perceptual quality. Owing to the limited spatiotemporal resolution capacity of the human eye, VMAF uses motion along with other spatial features to configure its model. The model implements a basic co-located luminance difference method to determine motion. It has been observed that this method is inadequate to capture the temporal characteristic of the video.In this paper, we propose an improvement to the existing temporal metric of VMAF. The newly proposed Temporal Motion Vector based VMAF (TMV-VMAF) replaces the existing temporal metric in VMAF with a block-based motion state classification method that approximates motion score of a frame by leveraging motion estimation and block level energy information. This temporal feature is fed along with other spatial features of VMAF into a Support Vector Regression framework where the model is trained using a database of HD videos. Our results show that the TMV-VMAF achieves better correlation to opinion score when compared to the existing VMAF.