Reviewerly/exHarmony huggingface dataset:
Expressiveness and machine processability of Knowledge Organization Systems (KOS): an analysis of concepts and relations

This study considers the expressiveness (that is, the expressive power or expressivity) of different types of Knowledge Organization Systems (KOS) and discusses its potential to be machine-processable in the context of the semantic web. For this purpose, the theoretical foundations of KOS are reviewed based on conceptualizations introduced by the Functional Requirements for Subject Authority Data (FRSAD) and the Simple Knowledge Organization System (SKOS); natural language processing techniques are also implemented. Applying a comparative analysis, the dataset comprises a thesaurus (Eurovoc), a subject headings system (LCSH) and a classification scheme (DDC). These are compared with an ontology (CIDOC-CRM) by focusing on how they define and handle concepts and relations. It was observed that LCSH and DDC focus on the formalism of character strings (nomens) rather than on the modelling of semantics; their definition of what constitutes a concept is quite fuzzy, and they comprise a large number of complex concepts. By contrast, thesauri have a coherent definition of what constitutes a concept, and apply a systematic approach to the modelling of relations. Ontologies explicitly define diverse types of relations, and are by their nature machine-processable. The paper concludes that the potential of both the expressiveness and machine processability of each KOS is extensively regulated by its structural rules. It is harder to represent subject headings and classification schemes as semantic networks with nodes and arcs, while thesauri are more suitable for such a representation. In addition, a paradigm shift is revealed which focuses on the modelling of relations between concepts, rather than the concepts themselves.
The VR Ecosystem and What to Expect in the Years to Come

This chapter is a snapshot of the wider virtual reality (VR) industry at the time of the writing of this book. VR innovation is constantly evolving, partly due to the fact that the latest wave of the consumer VR market is still in its early stages; in many aspects, not unlike the early smartphone market, when a large variety of technologies, formats, form factors, and user experience (UX) concepts were competing for consumer attention. Besides the almost daily news on breakthrough research in areas like resolution, eye tracking, and usability, there are stable long-term trends that have proven to be reliable pointers for the roadmap ahead of us. We will look at these established long-term VR trends and evaluate what is relevant in the mobile and standalone VR space.
An Overview of Apache Pig and Apache Hive

Ever since the enhancement of technology has taken place, the data is growing at an alarming rate. The most prominent factor of data growth is the “Social Media”, leads to the origination of a tremendous amount of data called Big Data. Big Data is a term used for data sets that are extremely large in size as well as complicated to store and process using traditional database processing applications. A saviour to deal with Big Data is “Hadoop” and two major components of Hadoop which are HDFS (Distributed Storage) and Map Reduce(Parallel Processing). Apache Pig and Hive is an essential part of the Hadoop Ecosystem. This paper covers an overview of both Apache Pig and Hive with their architecture. As Hadoop, no doubt is doing tremendously great work by storing and processing the huge volume of data but there are more frameworks now a days to increase the efficiency of Hadoop framework which are basically seen as the layers of Hadoop or a part of Apache Hadoop project. And that is why this paper includes the two most important layers namely Apache Pig and Apache Hive.
A Variant of Secret Sharing Protected with Poly-1305

We are working in the era of cloud computing, where all of the required resources are available online at pay-par-basis. It made all the IT industry easily accessible to all types of users. It provides the services in software, hardware and in storage terms. We are dealing the model of IAAS which provides on demand secured storage services. A number of researchers has designed and proposed a number of techniques and algorithms for assurance of storage services provided by cloud service providers. Our paper presents a modified approach of integrity verification in multiparty communication in decentralized cloud computing environment. We enhanced the basic model of AES with AES Poly Library 1305 and also redesigned the variant of secret sharing scheme for handling a secured group communication. Our factors for evaluation are the hardness and randomness of key i.e. entropy of the proposed technique and other measurable units which gives the efficiency of communication overhead and security.
Characterizing the Complexity of Code Changes in Open Source Software

In open source software (OSS), users are more involved in the process of feature enhancements, inclusion of new features and bug fixes due to the easy access of the source code. The participation of different users in building OSS leads to rapid improvements. This chapter examines the complexity of code changes into two factors: feature enhancements or inclusion of new features, and bug repairs for an open source software. It provides the suggested model building for characterization of the complexity of code changes. The chapter discusses the data sets, model authentication, and tables and figures backing the suggested model. Implementation of the suggested model has been estimated on the basis of several goodness-of-fit criteria and they have been found to fit the real data. The suggested model to characterize complexity of code changes of open source software will benefit in development of software.
Orientation Aware Object Detection with Application to Firearms

Automatic detection of firearms is important for enhancing security and safety of people, however, it is a challenging task owing to the wide variations in shape, size and appearance of firearms. To handle these challenges we propose an Orientation Aware Object Detector (OAOD) which has achieved improved firearm detection and localization performance. The proposed detector has two phases. In the Phase-1 it predicts orientation of the object which is used to rotate the object proposal. Maximum area rectangles are cropped from the rotated object proposals which are again classified and localized in the Phase-2 of the algorithm. The oriented object proposals are mapped back to the original coordinates resulting in oriented bounding boxes which localize the weapons much better than the axis aligned bounding boxes. Being orientation aware, our non-maximum suppression is able to avoid multiple detection of the same object and it can better resolve objects which lie in close proximity to each other. This two phase system leverages OAOD to predict object oriented bounding boxes while being trained only on the axis aligned boxes in the ground-truth. In order to train object detectors for firearm detection, a dataset consisting of around eleven thousand firearm images is collected from the internet and manually annotated. The proposed ITU Firearm (ITUF) dataset contains wide range of guns and rifles. The OAOD algorithm is evaluated on the ITUF dataset and compared with current state of the art object detectors. Our experiments demonstrate the excellent performance of the proposed detector for the task of firearm detection.
Strong bounds on required resources for quantum channels by local operations and classical communication

Given a protocol ${\cal P}$ that implements multipartite quantum channel ${\cal E}$ by repeated rounds of local operations and classical communication (LOCC), we construct an alternate LOCC protocol for ${\cal E}$ in no more rounds than ${\cal P}$ and no more than a fixed, constant number of outcomes for each local measurement, the same constant number for every party and every round. We then obtain another upper bound on the number of outcomes that, under certain conditions, improves on the first. The latter bound shows that for LOCC channels that are extreme points of the convex set of all quantum channels, the parties can restrict the number of outcomes in their individual local measurements to no more than the square of their local Hilbert space dimension, $d_\alpha$, suggesting a possible link between the required resources for LOCC and the convex structure of the set of all quantum channels. Our bounds on the number of outcomes indicating the need for only constant resources per round, independent of the number of rounds $r$ including when that number is infinite, are a stark contrast to the exponential $r$-dependence in the only previously published bound of which we are aware. If a lower bound is known on the number of product operators needed to represent the channel, we obtain a lower bound on the number of rounds required to implement the given channel by LOCC. Finally, we show that when the quantum channel is not required but only that a given task be implemented deterministically, then no more than $d_\alpha^2$ outcomes are needed for each local measurement by party $\alpha$.
The use of social networks in a Virtual University

The aim of this paper is to discover the educational use of social networks in initial training by students of the Bachelor's Degree in Teaching and Master's Degree in Secondary Education in a non-classroom-based and exclusively online University. In this study, we have selected the most popular social networks on which the University itself is present: YouTube, Instagram, Facebook, Pinterest and Twitter. The methodology focuses on the design, validation and distribution of an online questionnaire, with a valid sample of 130 participants studying Bachelor's Degrees and 132 studying the Master's Degree. The results obtained show that the Bachelor's Degree students are more active on social networks and use them more for educational purposes than the Master's Degree students. It can be noted that Pinterest and YouTube are used the most for educational purposes, while the educational use of Facebook is declining despite being the most promoted social network in this area given its benefits in cooperative learning. Instagram and Twitter are those that are least used for educational purposes. With regards to Instagram, it may be related to the age range of our students, as it is higher than that of the regular users of this network.
Inferring translation candidates for multilingual dictionary generation with multi-way neural machine translation

This publication has emanated from research supported in part by a research grantfrom Science Foundation Ireland (SFI) under Grant Number SFI/12/RC/2289,co-funded by the European Regional Development Fund, and the EuropeanUnion’s Horizon 2020 research and innovation programme under grant agreementNo 731015, ELEXIS - European Lexical Infrastructure.
The Relationship between Carbon Emission and Economic Activity in the United States

The objective of this paper is to analyze the relationship between the carbon emission, GDP and its growth rate, energy consumption, and unemployment rate in case of the United States and moreover, we also aim to explore where US and the fifty states now lie on the Kuznets curve. By using the panel data to investigate five different districts from 2000 to 2015 and the US as a whole, the empirical results indicate a long-run equilibrium relationship between variables and the positions the United States and its different states now lie on the curve. These findings suggest that the United States should adopt other low-carbon supporting policies and technologies to real-ize the movement from the left side of the curve to the right side of it, with the major goal to abstain from negatively affecting economic development by decreasing vitality utilization.
TIAD 2019 shared task: Leveraging knowledge graphs with neural machine translation for automatic multilingual dictionary generation

This paper describes the different proposed approaches to the TIAD 2019 Shared Task, which consisted in the automatic discovery and generation of dictionaries leveraging multilingual knowledge bases. We present three methods based on graph analysis and neural machine translation and show that we can generate translations without parallel data.
Optimistic Sampling Strategy for Data-Efficient Reinforcement Learning

A high required number of interactions with the environment is one of the most important problems in reinforcement learning (RL). To deal with this problem, several data-efficient RL algorithms have been proposed and successfully applied in practice. Unlike previous research, that focuses on optimal policy evaluation and policy improvement stages, we actively select informative samples by leveraging entropy-based optimal sampling strategy, which takes the initial samples set into consideration. During the initial sampling process, information entropy is used to describe the potential samples. The agent selects the most informative samples using an optimization method. This way, the initial sample is more informative than in random and fixed strategy. Therefore, a more accurate initial dynamic model and policy can be learned. Thus, the proposed optimal sampling method guides the agent to search in a more informative region. The experimental results on standard benchmark problems involving a pendulum, cart pole, and cart double pendulum show that our optimal sampling strategy has a better performance in terms of data efficiency.
GNU Privacy Guard- A Tool for Safe Computing

In the modern era, digital communications play a vital role. For an elementary security, encryption is preventing a huge number of problems. When a data is passed through many number of nodes, we don’t know what vulnerability is present at each node. Our data may be affected. GPG encryption is a tool for encrypting emails, digital materials. It uses the concept of key pairs like public key, private key. The public keys are exchanged between the sender and the receiver. The private key is kept as a secret by user. This paper focus on new GNU privacy guard which is a tool to ensure security in huge users traffic and reliable data transmission .
Hubungan Literasi Informasi Digital dengan Self Direct Learning Mahasiswa pada Daerah Miskin di Kepulauan Sumatra

Access to learning is seeking more literacy through digital media than conventional media. Information literacy is also called information literacy, evaluating, and combining information. Students are required to follow the development of information literacy technology. However, in the Sumatra region, it still has a lagging province with a poverty percentage of 16.45%, namely the province of Bengkulu. Based on this, students in Bengkulu province have more challenges to information literacy. It is important to analyze how the relationship between digital information literacy and self-direct learning for students in weak areas of the Sumatra islands. The study was conducted in May 2018 with respondents as many as 100 students. Data processing is done by using rank spearman to see the relationship between information literacy and self-direct learning. Based on the results of the study, identifying information is significantly related to learning to self-manage. Furthermore, evaluating information sources is significantly related to students thinking independently. While other factors are not related.
A Computational Segmentation Tool for Processing Patient Brain MRI Image Data to Automatically Extract Gray and White Matter Regions

Brain MRI imaging is necessary to screen and detect diseases in the brain, and this requires processing, extracting, and analyzing a patient’s MRI medical image data. Neurologists and neurological clinicians, technicians, and researchers would be greatly facilitated and benefited by a graphical user interface-based computational tool that could perform all the required medical MRI image processing functions automatically, thus minimizing the cost, effort, and time required in screening disease from the patient’s MRI medical image data. Thus, there is a need for automatic medical image processing software platforms and for developing tools with applications in the medical field to assist neurologists, scientists, doctors, and academicians to analyze medical image data automatically to obtain patient-specific clinical parameters and information. This research develops an automatic brain MRI segmentation computational tool with a wide range of neurological applications to detect brain patients’ disease by analyzing the special clinical parameters extracted from the images and to provide patient-specific medical care, which can be especially helpful at early stages of the disease. The automatic brain MRI segmentation is performed based on modified pixel classification technique called fuzzy c-means followed by connected component labeling.
Composable security of two-way continuous-variable quantum key distribution without active symmetrization

We present a general framework for CV-QKD protocols involving only a Gaussian modulation of coherent states and heterodyne detection. We exploit their symmetry in phase-space to obtain a composable security proof against general attacks.
A Research about Air Force Academy Freshmen’s Fitness, Academic Achievement and Grit

The purpose of the study is to analyze the Air Force Academy freshmen’s fitness, academic achievement and grit for educational development. The results from quantitative and qualitative method are as follows.BR First, entrance fitness examination is related with first semester’s fitness(R=.490, p＜.001). Entrance fitness examination is related with grit’s subfactor passion(R=.168, p＜.05), but in first semester not.BR Second, first semester’s academic achievement is related with entrance fitness examination and first semester’s, but the trends became smaller(R=.415, p＜.001 vs R=.247, p＜.05).BR Third, grit is not related with entrance examination’s grade, but first semester’s grade with grit(R=.260, p＜.05) and it’s subfactor passion(R=.176, p＜.05) and perseverance(R=.241, p＜.05).BR Conclusively, freshmen’s fitness, academic achievement and grit are related. And freshmen recognized fitness as officer’s basic trait, academic achievement as cadet’s faithfulness indicator, grit as energy for arousing fighting power.BR In follow-up study, grit’s forming process and type is needed for more educational development.
Bitcoin financial forecasting

Bitcoin being a standout among the most unusual money had all the earmarks of being a decent point of the study. Our point is to prepare our model to foresee the estimation of Bitcoin in USD at some random time stamp. For these different methods and calculations of machine learning have been utilized. To prepare our model, an expansive arrangement of information in a legitimate organization was required. Different sites were investigated to gather the information in the ideal configuration. In spite of the fact that foreseeing the estimation of Bitcoin is practically unimaginable, yet we attempted to anticipate the incentive in the given political and monetary situation.
Advanced Trajectory Generator for Two Carts with RGB-D Sensor on Circular Rail

This paper presents a motorised circular rail that generates the motion of two carts with an RGB-D sensor each. The objective of both carts’ trajectory generation is to track a person’s physical rehabilitation exercises from two points of view and his/her emotional state from one of these viewpoints. The person is moving freely his/her position and posture within the circle drawn by the motorised rail. More specifically, this paper describes the calculation of trajectories for safe motion of the two carts on the motorised circular rail in detail. Lastly, a study case is offered to show the performance of the described control algorithms for trajectory generation.
A Source Domain Extension Method for Inductive Transfer Learning Based on Flipping Output

Transfer learning aims for high accuracy by applying knowledge of source domains for which data collection is easy in order to target domains where data collection is difficult, and has attracted attention in recent years because of its significant potential to enable the application of machine learning to a wide range of real-world problems. However, since the technique is user-dependent, with data prepared as a source domain which in turn becomes a knowledge source for transfer learning, it often involves the adoption of inappropriate data. In such cases, the accuracy may be reduced due to “negative transfer.” Thus, in this paper, we propose a novel transfer learning method that utilizes the flipping output technique to provide multiple labels in the source domain. The accuracy of the proposed method is statistically demonstrated to be significantly better than that of the conventional transfer learning method, and its effect size is as high as 0.9, showing high performance.
Fitbit for Developers: Self-Monitoring at Work

Recently, we have seen an explosion in the number of devices and apps that we can use to track various aspects of our lives, such as the steps we walk, the quality of our sleep, or the calories we consume. People use devices such as the Fitbit activity tracker to increase and maintain their physical activity level by tracking their behavior, setting goals (e.g. 10'000 steps a day) and competing with friends. Many of these approaches have been shown to successfully encourage users to change their behaviors, often motivated through persuasive technologies, such as goal-setting, social encouragement and sharing mechanisms. We explored how we can map the tremendous success of these smart devices to the workplace, with the aim to increase software developers' self-awareness about productivity through self-monitoring. Yet, little is known about expectations of, the experience with, and the impact of self-monitoring in the workplace. From a mixed-methods approach we inferred design elements for building workplace self-monitoring tools, which we then implemented as a technology probe called WorkAnalytics. We field-tested these design elements during a three-week study with software development professionals. In the field study, we found that self-monitoring paired with experience sampling increases developers' awareness about work and motivates many to improve their behaviors, and that a wide variety of different metrics is needed to fulfill developers' expectations. Our work can serve as a starting point for researchers and practitioners to build self-monitoring tools for the workplace.
A complete no-reference image quality assessment method based on local feature

Image quality assessment (IQA) is widely used in digital image processing, and no-reference (NR) IQA has become research focus recently. This paper proposes a NR IQA method based on local features without access to prior knowledge of the images or their distortions. Four gradient masks are used to detect the maximum local gradient (MLG), and the analysis shows that the MLG of strong structure (such as region boundary) includes very tiny noise component, thus this paper assesses image visual quality by using MLGs of strong structures. The proposed method can assess noisy image and blurred image at the same time, and the quality score drops either when the test image becomes blurred or corrupted by random noise. The experiment results show that the proposed approach works well on LIVE, TID2013 and CSIQ databases, and it outperforms some state-of-the-art algorithms.