Reviewerly/exHarmony huggingface dataset:
Derivation Procedure of Coefficients of Metadata-based Model for Adaptive Bitrate Streaming Services

Since the quality of video streaming services is degraded due to the encoding, network congestion, and lack of a playout buffer, the normality of services needs to be monitored by gathering the quality measured at the end clients. When measuring quality at the end clients, the computational power should be sufficiently low, the bitstream information cannot be accessed for the quality estimation due to the encryption, and reference video cannot be used at the end clients. Therefore, metadata-based models have been developed and standardized that take metadata such as the resolution, framerate, and bitrate, and stalling information as input and calculate the quality. However, calculated quality for linear TV and video on demand (VoD) services cannot be compared because metadata-based models cannot calculate the impacts of codec strategies (e.g., H.264/AVC, H.265/HEVC, and AV1) and configurations (e.g., 1-pass encoding for linear TV or 2-pass encoding for VoD) on the quality. To take into account the impact of codec strategies and configurations, coefficients of metadata-based model need to be optimized per codec strategy and configuration using subjective quality. However, extensive subjective assessment tests are difficult to frequently conduct because they are expensive and time consuming and need to be conducted by video quality experts. Therefore, to monitor the quality of several types of video streaming services (e.g., linear TV and VoD) and to compare these qualities, a derivation procedure is proposed for obtaining coefficients of metadata-based models using a full-reference model. To validate the procedure, extensive subjective assessment tests were conducted. Finally, it is shown that three metadata-based models (i.e., the P.1203.1 mode 0 model, extended P.1203.1 mode 0 model, and model proposed by Yamagishi et al.) based on the proposed procedure using the video multimethod assessment fusion (VMAF) algorithm estimate quality accurately in terms of root mean squared error.
Use of the Graphic Tablet in the Art and Educational University

This article is discussing application of graphic tablet in the art educational University, by introducing it in design and work with graphical images related to the creation and editing of raster, vector graphics.Sequential drawing of the simplest elements for training the hand's muscle memory achieved during classes by use of the pen and graphic tablet.The relevance in the application of this device opens up new opportunities in art education when using modern graphic technologies.The main findings observed at the end of the class, where students familiarized themselves with the principles of connection and using the technique of pen (stylus) on the graphics tablet.Students mastered the techniques of drawing from the simplest elements to more complex tasks.The article discusses the practical use of a graphics tablet in modern art education in the context of distant learning.
Smart Wheelchair Remotely Controlled by Hand Gestures

The main objective of this paper is to generate a hand gesture-controlled smart wheelchair for those physically challenged people who are incapable to move from one place to another in day-to-day life. Hand gesture-controlled wheelchair is a special kind of movable device which can control the motion of wheelchair at right direction through the hand movements of any person. This type of wheelchair is a blessing for those people who are not capable to move their lower limbs and are fully dependent on the caretaker for their activities. The proposed system can be categorized into two parts: gesture unit and wheelchair unit. A three-axis accelerometer ‘Micro-Electro-Mechanical Systems (MEMS)’ is used here as a sensor which is connected to the hand and sends the position of the hand to the microcontroller Arduino Lilypad. On the basis of data gathered from the accelerometer, microcontroller drives the signal to move the wheelchair in the desirable direction.
Face-Image Anonymization as an Application of Multidimensional Data k-anonymizer

Recently, the developments of data communication networks and advancements in the processing capacity of computers have significantly increased the amount of data that can be applied to a service. These data might result in future innovations. However, the privacy violation of data has become a problem. For example, images of customers’ faces captured with a surveillance camera may lead to new marketing strategies, as the reactions of the customers can be measured from their facial expressions. However, we must consider the privacy of customers when entrusting the images to a third-party data analyst. For solving this privacy problem, researchers have been developing anonymization technologies that are used to preserve privacy by deleting the private information from the original data. However, conventional anonymization techniques cannot appropriately anonymize high-dimensional data such as facial images. This is attributed to the fact that conventional anonymization techniques do not consider the complex relationships between dimensions and the semantic loss of data. However, studies regarding machine learning have been actively conducted; particularly, neural networks (NN) have developed remarkably since the advent of AlexNet [8]. Both machine learning and anonymization share the common idea in their bases to abstract statistical information from a given dataset . Therefore, the machine learning technology might enhance the functionality of anonymization techniques. In this study, we propose a method to apply the results of machine learning to anonymization based on the aforementioned common idea , and the method is named multi-input k-anonymizer unit (MIKU). Notably, MIKU has two modules called S and G maps for mapping a given data, and NNs are used for these modules to generate more natural anonymized data for humans than directly anonymized data, which is generated only by processing the pixel values of facial images. To evaluate MIKU, a direct anonymization method is used, without any NN, for the facial images. The facial images of CelebA [9] are used for both qualitative and quantitative evaluations. The qualitative evaluation is conducted by analyzing the anonymized facial images obtained using different methods, and the quantitative evaluation is performed using the Fréchet inception distance(FID) [4]. In the qualitative evaluation, there are cases where the quality of the anonymized images generated using the comparison method is low because unnatural edges and blurs are created on the anonymized facial images. However, MIKU maintains the quality and attributes of the original facial images even in those cases. In the quantitative evaluation, a different result is obtained when k = 2 anonymity. However, on anonymity greater than 2, the facial images anonymized using MIKU have higher quality than those anonymized using the comparison method.
HOTFRED: A Flexible Hotel Fake Review Detection System

Abstract The importance to cope with online fake reviews in Tourism becomes more and more evident. In the hotel sector hoteliers as well as guests often struggle with the challenges to separate true and fake reviews from each other. Therefore, our research introduces HOTFRED - a flexible hotel fake review detection system - as part of an on-going research project. By combining different analytical approaches, the HOTFRED system indicates via an aggregated probability whether a review is true or fake. As the evaluation of the prototypical implementation showed, this approach can support to detect fake reviews. Many different stakeholders in the Tourism sector can profit from this automatic tool. Thus, hoteliers can take measures to safe their reputation, guests can benefit in their decision-making process and research might use the tool as an initial starting point for future research in the area of fake information.
ARTIRILMIŞ GERÇEKLİK UYGULAMALARININ SOSYAL BİLGİLER EĞİTİMİNE ETKİSİ

The aim of this study is to examine the effects of investigate social studies teaching supported by teaching materials developed with augmented reality (AR) technology applications on students' academic achievement, attitudes and motivations towards the course and to determine the attitudes of students using AR technology towards the use of AR applications in the classroom. An experimental design using quantitative research methods was employed in the present study. The study sample consists of 70 5th grade students studying at a junior high school in south Turkey in the 2017-2018 academic year. Positive effects of the use of AR applications on students' academic achievement, attitudes and motivation towards the course were observed. In addition, students using AR applications were satisfied with the use of the applications, were willing to use the application and did not have any worries while using the application. Therefore, an increase in the use of activities related to AR applications in education processes is predicted to be beneficial for teaching and learning
Automatic object annotation in streamed and remotely explored large 3D reconstructions

Abstract We introduce a novel framework for 3D scene reconstruction with simultaneous object annotation, using a pre-trained 2D convolutional neural network (CNN), incremental data streaming, and remote exploration, with a virtual reality setup. It enables versatile integration of any 2D box detection or segmentation network. We integrate new approaches to (i) asynchronously perform dense 3D-reconstruction and object annotation at interactive frame rates, (ii) efficiently optimize CNN results in terms of object prediction and spatial accuracy, and (iii) generate computationally-efficient colliders in large triangulated 3D-reconstructions at run-time for 3D scene interaction. Our method is novel in combining CNNs with long and varying inference time with live 3D-reconstruction from RGB-D camera input. We further propose a lightweight data structure to store the 3D-reconstruction data and object annotations to enable fast incremental data transmission for real-time exploration with a remote client, which has not been presented before. Our framework achieves update rates of 22 fps (SSD Mobile Net) and 19 fps (Mask RCNN) for indoor environments up to 800 m 3 . We evaluated the accuracy of 3D-object detection. Our work provides a versatile foundation for semantic scene understanding of large streamed 3D-reconstructions, while being independent from the CNN’s processing time. Source code is available for non-commercial use.
The Application of Formal Methods to Real-World Cryptographic Algorithms, Protocols, and Systems

Computer hosts a virtual roundtable with four experts in formal methods to discuss recent developments in the area of cryptography.
The Intention Progression Competition

In this extended abstract, we briefly recall the background, aims and format of the Intention Progression Competition, describe the format and generation of the test problems used to evaluate entries, and outline the architecture of the competition software.
Anti-phishing System using LSTM and CNN

Users prefer to do e-banking and e-shopping now-a-days because of the exponential growth of the internet. Because of this paradigm shift, hackers are finding umpteen ways to steal our personal information and critical details like details of debit and credit cards, by disguising themselves as reputed websites, just by changing the spelling or making minor modifications to the URL. Identifying whether an URL is benign or malicious is a challenging job, because it makes use of the weakness of the user. While there are several works carried out to detect phishing websites, they only use heuristic methods and list based techniques and therefore couldn't avoid phishing effectively. In this paper an anti-phishing system was proposed to protect the users. It uses an ensemble model that uses both LSTM and CNN with a massive data set containing nearly 2,00,000 URLs, that is balanced. After analyzing the accuracy of different existing approaches, it has been found that the ensemble model that uses both LSTM and CNN performed better with an accuracy of 96% and the precision is 97% respectively which is far better than the existing solutions.
Improvement of Identity Recognition with Occlusion Detection-Based Feature Selection

Image-based facial identity recognition has become a technology that is now used in many applications. This is because it is possible to use only a camera without the need for any other device. Besides, due to the advantage of contactless technology, it is one of the most popular certifications. However, a common recognition system is not possible if some of the face information is lost due to the user’s posture or the wearing of masks, as caused by the recent prevalent disease. In some platforms, although performance is improved through incremental updates, it is still inconvenient and inaccurate. In this paper, we propose a method to respond more actively to these situations. First, we determine whether an obscurity occurs and improve the stability by calculating the feature vector using only a significant area when the obscurity occurs. By recycling the existing recognition model, without incurring little additional costs, the results of reducing the recognition performance drop in certain situations were confirmed. Using this technique, we confirmed a performance improvement of about 1~3% in a situation where some information is lost. Although the performance is not dramatically improved, it has the big advantage that it can improve recognition performance by utilizing existing systems.
Enhancing Attention Models via Multi-head Collaboration

Neural attention based models have recently boosted performance on various NLP tasks. Compared with single-head attention, multi-head attention is more powerful and popular. Multi-head attention independently attend to information from different feature subspaces and generates multiple attention distributions. In this paper, we make an assumption that the current multi-head attention method can generate complementary attention distributions, but these distributions may not collaborate properly to improve prediction quality. To validate our assumption, we propose a simple but effective method to enhance the collaboration of different attention heads, which allows different heads to have the chance to rectify their attention scores with other heads. Empirical study shows that our proposed method can significantly improve the performance of multi-head attention over a range of NLP tasks, and the experimental results also prove the existence of the problem of multi-head collaboration.
Finding and Generating a Missing Part for Story Completion

Creating a story is difficult. Professional writers often experience a writer’s block. Thus, providing automatic support to writers is crucial but also challenging. Recently, in the field of generating and understanding stories, story completion (SC) has been proposed as a method for generating missing parts of an incomplete story. Despite this method’s usefulness in providing creative support, its applicability is currently limited because it requires the user to have prior knowledge of the missing part of a story. Writers do not always know which part of their writing is flawed. To overcome this problem, we propose a novel approach called “missing position prediction (MPP).” Given an incomplete story, we aim to predict the position of the missing part. We also propose a novel method for MPP and SC. We first conduct an experiment focusing on MPP, and our analysis shows that highly accurate predictions can be obtained when the missing part of a story is the beginning or the end. This suggests that if a story has a specific beginning or end, they play significant roles. We conduct an experiment on SC using MPP, and our proposed method demonstrates promising results.
Consistency of Archetypal Analysis

Archetypal analysis is an unsupervised learning method that uses a convex polytope to summarize multivariate data. For fixed $k$, the method finds a convex polytope with $k$ vertices, called archetype points, such that the polytope is contained in the convex hull of the data and the mean squared distance between the data and the polytope is minimal. In this paper, we prove a consistency result that shows if the data is independently sampled from a probability measure with bounded support, then the archetype points converge to a solution of the continuum version of the problem, of which we identify and establish several properties. We also obtain the convergence rate of the optimal objective values under appropriate assumptions on the distribution. If the data is independently sampled from a distribution with unbounded support, we also prove a consistency result for a modified method that penalizes the dispersion of the archetype points. Our analysis is supported by detailed computational experiments of the archetype points for data sampled from the uniform distribution in a disk, the normal distribution, an annular distribution, and a Gaussian mixture model.
Students’ use of Cloud Storage in their Studies: A Case of a Private University in the Philippines

This paper seeks to determine factors affecting the students’ intention to use and actual use of the cloud storage systems such as Google Drive, iCloud and Microsoft One, etc. using the Unified Theory of Acceptance and Use of Technology. The respondents are students of leading private universities in the Philippines and the data were analyzed using Partial Lease Square – Structural Equation Model (PLS-SEM). PLS-SEM was conducted using SMART PLS software and the results showed that performance expectancy and social influence positively and significantly affects the intention to use cloud storage systems. Hence, the University should maintain its subscription of a cloud storage system and promote maximizing its use because large members of the academic community, the students, intend to use the cloud storage system as they perceive an increase in productivity of their schoolwork and due to increase by the community as well.
Do pre-service teachers feel ready to teach with digital technologies? A study in two teacher training institutions in Costa Rica

This study aims to identify both the level and frequency of digital technology use and perceived self-efficacy levels of pre-service teachers (n = 341). We collected data in Costa Rica through a survey during the 2016–2017 academic year; the survey includes closed-ended items on the use and frequency of digital technologies along with open-ended questions. Findings suggest that a majority of pre-service teachers frequently use digital technologies for both professional and private use and specifically the mobile phone and social media. Results further suggest they find themselves self-efficacious in the use of “traditional” digital technologies that are also used in teacher training by professors/teacher trainers such as laptop, email and video. They are less confident in using mobile phones and social media for teaching even though they use them extensively for their professional development.
Digital Image Watermarking by Fusion of Wavelet and Curvelet Transform

A new hybrid watermarking algorithm using discrete wavelet transform (DWT) and curvelet transform (CT) is implemented in this paper. Curvelet transform is developed to be more efficient than traditional transformations to represent edges along curves. In the proposed work, image watermarking is achieved by applying hybrid transformation and then evaluating the efficacy of the method. Initially, image is decomposed using DWT and then fast curvelet transform is applied on cD (diagonal edges details) sub-band. Secondly, lower coefficients are selected to embed binary-encoded watermark bits. Efficiency of algorithm is tested with different texture images and by varying size of watermark. The effectiveness of proposed algorithm is evaluated by applying attacks like resize, median filtering, and addition of noise. The results shows that, it is sustaining over different texture images even with increase in watermark embedding capacity. The proposed method results in 34.33 dB peak signal-to-noise ratio (PSNR) for high texture image, 47.77 dB for medium texture image, and 48.62 dB for low texture image over maximum size of watermark. It has good robustness against attacks and on large embedding capacity.
Quantum State Tomography of Four-Level Systems with Noisy Measurements

In this article, we investigate the problem of state reconstruction of four-level quantum systems.A realistic scenario is considered with measurement results distorted by random unitary operators.Two frames that define injective measurements are applied and compared.By introducing arbitrary rotations, we can test the performance of the framework versus the amount of experimental noise.The results of numerical simulations are depicted in graphs and discussed.In particular, a class of entangled states is reconstructed.The concurrence is used as a figure of merit to quantify how well entanglement is preserved through noisy measurements.
Secure IoT via Blockchain

Abstract Block chain technology had gained popularity because of its use in crypto-currencies like bitcoin. Today uses of blockchain are growing in number of areas like banking, industries, health centers and even security of IOT. Moreover, the use of IOT is growing exponentially every year with its aim in 5G technologies like e- health, smart homes, distributed intelligence etc. but it faces challenges in security and privacy. The privacy of a user data is at a risk because of its (i.e. IoT) centralized client-server model. This centralized approach of the server poses a serious vulnerability to the data security. This data at the server attracts the attackers to enter into the network and invade through the data and schedule attacks or inject a malware. It indicates that the central architecture of IoT possess a compromised confidentiality, integrity, and security of data which disrupts its use as the widespread adoption of this technology. Therefore, it is essential to evade the hostile centralized server architecture for IoT to enhance its security. It implies a need for decentralized architecture to maintain the data. The data can be kept at the different users without any central control with the help of blocks as suggested by Blockchain. This paper addresses the various security issues in IOT and how block chain helps in solving these issues.