Reviewerly/exHarmony huggingface dataset:
PENERAPAN MODEL PEMBELAJARAN CONTEXTUAL TEACHING AND LEARNING (CTL) TERHADAP AKTIVITAS SISWA KELAS X DI SMA NEGERI 5 MODEL LUBUKLINGGAU TAHUN PELAJARAN 2018/2019

This thesis is entitled "The Application of Contextual Teaching And Learning (CTL) Learning Model to the Activities of Class X Students in Lubuklinggau 5 Senior High Schools in Academic Year 2018/2019". The problem in this study is how student learning activities when practicing using CTL learning on the material magnitude and measurement of students of class X MIPA in Lubuklinggau State Senior High School 5 Model 2018/2019. The population in this study were all class X MIPA Lubuklinggau State Senior High School 5 Model 2018/2019 and part of the sample was class X MIPA 4, amounting to 36 students taken randomly. Data collection is done by observation, interviews, questionnaires, and tests. Based on data analysis that 'learning activities at practicum I had a percentage of 76.96% in good category and at practicum II had a percentage of 85.65% very good category for students of class X MIPA 4 in Lubuklinggau Model 5 High School 2018/2019.&#x0D;
A study of the factors affecting the content created by international travellers in Vietnam

Nowadays, international visitors often use the Internet to find information and share their travel experiences on social networks.This action helps other travellers make their traveling plans easier.However, there has not been much research on the user's perception on the use of travel content.This study aims to develop a model to measure the impact of user's perception on user-generated tourism content and to use the SEM model to test the feasibility of the proposed model.Results show that the perceived usefulness (PU) indirectly influenced user-generated content through the following channels: electronic word of mouth (eWOM), attitudes of visitors when choosing destinations and attitudes when using media social communication.The paper also provides recommendations to stakeholders in order to improve the value of content which is generated by international visitors for the development of the Vietnamese tourism industry.
ANALISIS KESANTUNAN BAHASA MEDIA SOSIAL: KOMUNIKASI MAHASISWA KEPADA DOSEN STMIK STIKOM INDONESIA

[Title: Analysis of Language Politeness on Social Media: Communication of Student to Lecturer of STMIK STIKOM Indonesia] In 2018, both student and lecture of STMIK STIKOM Indonesia are more active in communicating and interacting on social media rather than use Short Message Service (SMS). This research aimed to do an analysis about scheme of student utterance politeness, representation use of politeness maxim in their text, and the level of their politeness in interact with their lecture through social media. The data collected from documentation of their conversation on social media in two semesters of 2017 until 2018 and it is also collected from questioner and interview with the lecture as the respondent. The data analyzed by applied some theory. The main theory is the theory of politeness from Leech, continued by theory from Scollon, and then supported by Scale of Likert and Guttman. The Result shows that there are ten (10) schemes of interactions happen between student and lecture. They are (1) about assignment, (2) clarification about study, (3) permit of attendance, (4) confirming about lecture attendance, (5) informing about class schedule, (6) informing their contact, (7) confirming about guidance schedule, (8) additional class schedule, (9) module and learning tools, (10) confirming about test. It is found those politeness maxims used in their utterance are Tact Maxim and Generosity Maxim. The instruments that form their politeness are power, social distance, and weight of imposition. Furthermore, about the politeness level of student utterance based on interview with the lecture, it is found that they uses polite utterance, but most of the lecture said that the collage need to create regulation for controlling student in sending text through social media.
Virtual offender: a pilot project on nurturing social work students’ capacity to work with offenders

Although considerable literature conducted in the context of Western societies has concluded that the use of virtual reality (VR) technology can facilitate students’ learning, the applicability of this learning approach to nurturing social work students in the Chinese context remains uncertain. This pilot study empirically tests the effectiveness of VR technology in enhancing social work students’ perceived creativity and competence in working with offenders. The study uses mixed methods that include qualitative and quantitative procedures in a complementary manner. Survey data that capture the responses of 41 social work students collected in the pre-and post-test periods indicate a positive change in self-perceived confidence in handling offenders following the VR training session. The qualitative data generated from the focus groups echo the survey findings and provide insights into the use of VR technology in social work education. Issues and implications for innovative education in the social work profession are discussed.
Effectiveness of tablet computers in spatial data collection and surveys in rural Tanzania

Abstract Worldwide, there is increasing application of tablet computers and other portable electronic devices in research. Effectiveness of these devices as data collection tools is widely appreciated, but several challenges exist when used in remote/rural settings. A household survey and spatial data collection were undertaken using tablet computers in rural Tanzania. Over 5 days in the study village of Naitolia, 12 enumerators visited 170 households completing 154 household surveys and 156 women's surveys while simultaneously collecting spatial data on wet/dry season water sources for 92 households using the tablets. Power supply, internet connectivity, and app faults were among the primary challenges that require attention to facilitate efficient use of tablets to collect data in remote areas. Lessons learnt include the importance of alternative energy (eg, solar energy) for charging, carrying hard copies of survey instruments as a backup, and ensuring that newly developed apps have been thoroughly field tested.
DF 2.0: An Automated, Privacy Preserving, and Efficient Digital Forensic Framework That Leverages Machine Learning for Evidence Prediction and Privacy Evaluation

The current state of digital forensic investigation is continuously challenged by the rapid technological changes, the increase in the use of digital devices (both the heterogeneity and the count), and the sheer volume of data that these devices could contain.Although data privacy protection is not a performance measure, however, preventing privacy violations during the digital forensic investigation, is also a big challenge.With a perception that the completeness of investigation and the data privacy preservation are incompatible with each other, the researchers have provided solutions to address the above-stated challenges that either focus on the effectiveness of the investigation process or the data privacy preservation.However, a comprehensive approach that preserves data privacy without affecting the capabilities of the investigator or the overall efficiency of the investigation process is still an open problem.In the current work, the authors have proposed a digital forensic framework that uses case information, case profile data and expert knowledge for automation of the digital forensic analysis process; utilizes machine learning for finding most relevant pieces of evidence; and maintains data privacy of non-evidential private files.All these operations are coordinated in a way that the overall efficiency of the digital forensic investigation process increases while the integrity and admissibility of the evidence remain intact.The framework improves validation which boosts transparency in the investigation process.The framework also achieves a higher level of accountability by securely logging the investigation steps.As the proposed solution introduces notable enhancements to the current investigative practices more like the next version of Digital Forensics, the authors have named the framework 'Digital Forensics 2.0', or 'DF 2.0' in short.
Extinction imagers for measurements of atmospheric beam transmittance

Optical beam transmittance for a horizontal path of sight through the atmosphere may be measured with transmissometers, which typically are double-ended, with source and a receiver at opposite ends of the path. Determination of the beam transmittance for longer paths is more challenging in some applications, such as monitoring the surround of a ship at sea or an urban environment. Extinction imaging is a new, to the best of our knowledge, method we have developed for determining the beam transmittance and effective path extinction coefficient over extended atmospheric paths in multiple directions with a single-ended system. Our new advances to the classical theory are enabled by use of calibrated imagers operating in visible, near-infrared, or short-wave infrared wave bands. This article presents the theory, hardware, and tests with supporting instrumentation, including transmissometers and point scatter meters.
Elation KM-Arcs

In this paper, we study KM-arcs in PG(2, q), the Desarguesian projective plane of order q. A KM-arc $$\mathcal{A}$$ of type t is a natural generalisation of a hyperoval: it is a set of q+t points in PG(2, q) such that every line of PG(2, q) meets $$\mathcal{A}$$ in 0, 2 or t points. We study a particular class of KM-arcs, namely, elation KM-arcs. These KM-arcs are highly symmetrical and moreover, many of the known examples are elation KM-arcs. We provide an algebraic framework and show that all elation KM-arcs of type q/4 in PG(2, q) are translation KM-arcs. Using a result of [2], this concludes the classification problem for elation KM-arcs of type q=4. Furthermore, we construct for all q = 2h, h > 3, an infinite family of elation KM-arcs of type q/8, and for q=2h, where 4, 6, 7 | h an infinite family of KM-arcs of type q/16. Both families contain new examples of KM-arcs.
Robustness of distributed nonclassicality against local Gaussian noise

We study the robustness of single-mode squeezing nonclassicality against the action of single-mode Gaussian noise (Gaussian channels), first in its local single-mode form and second in its nonlocal distributed form as two-mode entanglement, with the channel acting locally on the single-mode. We find that the distributed single-mode nonclassicality is more robust than local single-mode nonclassicality against the local application of the canonical single-mode Gaussian channels i.e., for the canonical attenuator channel, the canonical amplifier channel, the canonical phase-conjugation channel, and the channel with a singular $X$ matrix, with the exception of the channel with a singular $Y$ matrix. This robustness persists even when the canonical channel is nonclassicality breaking. For the canonical channel with a singular $Y$ matrix, we find that the distributed as well as the local single-mode nonclassicality is equally robust against the local application of the channel. We illustrate examples where local single-mode squeezing is more robust than distributed squeezing against the local application of the channel, for channels not of the canonical form.
Challenges of Big Data Applications in Cloud Computing

Big Data applications are used for decision making process for gaining useful insights hidden from large volume of data.They make use of cloud computing infrastructure for massive scale and complex computation which eliminates the need to maintain dedicated hardware and software resources.The relationship between big data and cloud computing is presented with focus on challenges and issues in data storage with different formats, data transformation techniques applied, data quality and business challenges associated with it.Also, some good practices which helps in big data analysis has been listed.
Augmented Reality Technology for Displaying Close-Proximity Sub-Surface Positions

When designing human-system collaboration to assist in strenuous manual tasks we need to develop methods of communication between the system and the human. In this paper we are evaluating augmented reality (AR) technologies for displaying task-relevant information when the target is on a work surface for a typically standing work operation. In this case we are testing AR interfaces for displaying sub-surface positions. To do this we compare four types of AR interfaces, a head-mounted see-through display, a mounted see-through display, top-down surface projection and graphical overlays on a static monitor. We performed the experiment with 48 participants. Data analyses show significant difference between the AR interfaces in terms of task completion times and user satisfaction with the projection-based display being the fastest and most satisfying to the participants.
Remote detection of idling cars using infrared imaging and deep networks

Idling vehicles waste energy and pollute the environment through exhaust emission. In some countries, idling a vehicle for more than a predefined duration is prohibited and automatic idling vehicle detection is desirable for law enforcement. We propose the first automatic system to detect idling cars, using infrared (IR) imaging and deep networks. We rely on the differences in spatio-temporal heat signatures of idling and stopped cars and monitor the car temperature with a long-wavelength IR camera. We formulate the idling car detection problem as spatio-temporal event detection in IR image sequences and employ deep networks for spatio-temporal modeling. We collected the first IR image sequence dataset for idling car detection. First, we detect the cars in each IR image using a convolutional neural network, which is pre-trained on regular RGB images and fine-tuned on IR images for higher accuracy. Then, we track the detected cars over time to identify the cars that are parked. Finally, we use the 3D spatio-temporal IR image volume of each parked car as input to convolutional and recurrent networks to classify them as idling or not. We carried out an extensive empirical evaluation of temporal and spatio-temporal modeling approaches with various convolutional and recurrent architectures. We present promising experimental results on our IR image sequence dataset.
Reasoning about Social Choice and Games in Monadic Fixed-Point Logic

Whether it be in normal form games, or in fair allocations, or in voter preferences in voting systems, a certain pattern of reasoning is common.From a particular profile, an agent or a group of agents may have an incentive to shift to a new one.This induces a natural graph structure that we call the improvement graph on the strategy space of these systems.We suggest that the monadic fixedpoint logic with counting, an extension of monadic first-order logic on graphs with fixed-point and counting quantifiers, is a natural specification language on improvement graphs, and thus for a class of properties that can be interpreted across these domains.The logic has an efficient model checking algorithm (in the size of the improvement graph).
Automatic Radiology Report Generation based on Multi-view Image Fusion and Medical Concept Enrichment

Generating radiology reports is time-consuming and requires extensive expertise in practice. Therefore, reliable automatic radiology report generation is highly desired to alleviate the workload. Although deep learning techniques have been successfully applied to image classification and image captioning tasks, radiology report generation remains challenging in regards to understanding and linking complicated medical visual contents with accurate natural language descriptions. In addition, the data scales of open-access datasets that contain paired medical images and reports remain very limited. To cope with these practical challenges, we propose a generative encoder-decoder model and focus on chest x-ray images and reports with the following improvements. First, we pretrain the encoder with a large number of chest x-ray images to accurately recognize 14 common radiographic observations, while taking advantage of the multi-view images by enforcing the cross-view consistency. Second, we synthesize multi-view visual features based on a sentence-level attention mechanism in a late fusion fashion. In addition, in order to enrich the decoder with descriptive semantics and enforce the correctness of the deterministic medical-related contents such as mentions of organs or diagnoses, we extract medical concepts based on the radiology reports in the training data and fine-tune the encoder to extract the most frequent medical concepts from the x-ray images. Such concepts are fused with each decoding step by a word-level attention model. The experimental results conducted on the Indiana University Chest X-Ray dataset demonstrate that the proposed model achieves the state-of-the-art performance compared with other baseline approaches.
Automated Translation Preference with a Bilingual Chatbot

Free, online language translation services are being used by people around to the world to facilitate communication. However, it takes time and effort for a person to load the Web page in a browser, copy and paste text into the site, and translate words. The process quickly becomes tiresome. Instead, some computer programs are providing automated translation. However, no studies have been conducted to determine the efficiency or effectiveness of such an approach. In this study, we compare how students used an English-based chatbot with and without German automatic translation. Results show that students took nearly 1.5 times longer than their stated upper time limit to manually translate. In contrast, the automated translation was at least 30 times faster. In addition, the students were significantly more satisfied with the automated than the manual system.
Full state revivals in higher dimensional quantum walks

Full state revivals in a quantum walk can be viewed as returning of the walker to the initial quantum state in a periodic fashion during the propagation of the walk. In this paper we show that for any given number of spatial dimensions, a coin operator can be constructed to generate a quantum walk having full revivals with any desired period. From the point of view of quantum computation and simulations, these coin operators can be useful in implementing quantum walks which oscillate between any two states with a finite periodicity.
Latent Path Connected Space Model for Recommendation

Matrix Factorization (MF) is a latent factor model, which has been one of the most popular techniques for recommendation systems. Performance of MF-based recommender models degrades as the sparseness of user-item rating data increases. MF-based models map each user and each item into a low dimensional space, where either of them is represented by a point in the space. While a point is a concise and simple representation of a user’s preference or an item’s characteristics, it is hard to learn the precise position of the point, especially when the data is very sparse. In this paper we propose an alternative latent space model, Latent Path Connected Space model (LSpace), to address this issue. In this model, users and items are both represented by path connected space described by different latent dimensions and spatial intersection between user space and item space reflects their matching degree. Extensive evaluations on four real-world datasets show that our approach outperforms the Matrix Factorization model on rating prediction task especially when the rating data is extremely sparse.
Importance-Aware Semantic Segmentation with Efficient Pyramidal Context Network for Navigational Assistant Systems

Semantic Segmentation (SS) is a task to assign semantic label to each pixel of the images, which is of immense significance for autonomous vehicles, robotics and assisted navigation of vulnerable road users. It is obvious that in different application scenarios, different objects possess hierarchical importance and safety-relevance, but conventional loss functions like cross entropy have not taken the different levels of importance of diverse traffic elements into consideration. To address this dilemma, we leverage and re-design an importance-aware loss function, throwing insightful hints on how importance of semantics are assigned for real-world applications. To customize semantic segmentation networks for different navigational tasks, we extend ERF-PSPNet, a real-time segmenter designed for wearable device aiding visually impaired pedestrians, and propose BiERF-PSPNet, which can yield high-quality segmentation maps with finer spatial details exceptionally suitable for autonomous vehicles. A comprehensive variety of experiments with these efficient pyramidal context networks on CamVid and Cityscapes datasets demonstrates the effectiveness of our proposal to support diverse navigational assistant systems.
Creative Industries: The Experience of Practice-Oriented Approach Application in the Modern Russian Education

The sphere of creative industries is the activity based on applying creative approach. Due to the complex composition of the areas of production attributed to creative industries, and the interdisciplinarity of approaches to their study, a consolidated position regarding their definition, composition, classification and essential characteristics has not been developed yet. At the same time, many resear­chers believe that activities in the field of creative industries have the ability to develop human potential. The inclusion of the topic, principles and approaches of the creative industries to the educational process of the higher education institutes of Russia conforms to the educational field development tendencies connected with the formation of necessary metasubject skills and abilities in present conditions. In those si­tuations when there is no possibility and need for introducing specialized educational courses in creative industries into educational programs, the same positive results can be achieved by applying a practice-oriented approach to the disciplines, provided by curriculum plans, educational and work practi­ces, and educational events. Through acquain­tance with creative businesses, inventions of their own creative projects, students have the opportunity to expand the future professional field, to apply the know­ledge, gained in particular from stu­dying a wide range of disciplines, to solve real problems in public life. The practice-oriented approach in educational programs can be embodied in the forms of specialized edu­cational programs, project semi­nars (competitions, incubators) and educational events. The article considers the practical examples of the specified formats implemented by “Creative Industries” Agency from 2015 to 2018 in partnership with a range of state institutes of higher education (Moscow School of Social and Economic Sciences, Russian Presidential Academy of National Economy and Public Administration, Moscow City Government University of Management, Petrosavodsk State University, Siberial Fede­ral University) and cultu­ral institutions.
Fairness in Reinforcement Learning

Decision support systems (e.g., for ecological conservation) and autonomous systems (e.g., adaptive controllers in smart cities) start to be deployed in real applications. Although their operations often impact many users or stakeholders, no fairness consideration is generally taken into account in their design, which could lead to completely unfair outcomes for some users or stakeholders. To tackle this issue, we advocate for the use of social welfare functions that encode fairness and present this general novel problem in the context of (deep) reinforcement learning, although it could possibly be extended to other machine learning tasks.