Reviewerly/exHarmony huggingface dataset:
A comprehensive framework for visual quality assessment of light field tensor displays

In recent years, light field technology has attracted the interest of academia and industry, thanks to the possibility of rendering 3D scenes in a more realistic and immersive way. In particular, light field displays have been consistently investigated for their ability to offer a glass-free 3D viewing experience. Among others, tensor displays represent a promising way to render light field contents. However, only a few prototypes of such type of displays have been implemented and are available to the scientific community. As a direct consequence, the visual quality of such displays has not been rigorously investigated. In this paper, we propose a new framework to assess the visual quality of light field tensor displays on conventional 2D screens. The multilayer components of the tensor displays are virtually rendered on a typical 2D monitor through the use of a GUI, and different viewing angles can be accessed by simple mouse interactions. Both single and double stimulus methodologies for subjective quality assessment of light field contents are supported in this framework, while the total time of interaction is recorded for every stimulus. Results obtained in two different laboratory settings demonstrate that the framework can be successfully used to perform subjective quality assessment of different compression solutions for light field tensor displays.
Investigation on N-Gram Approximated RNNLMs for Recognition of Morphologically Rich Speech

Recognition of Hungarian conversational telephone speech is challenging due to the informal style and morphological richness of the language. Recurrent Neural Network Language Model (RNNLM) can provide remedy for the high perplexity of the task; however, two-pass decoding introduces a considerable processing delay. In order to eliminate this delay we investigate approaches aiming at the complexity reduction of RNNLM, while preserving its accuracy. We compare the performance of conventional back-off n-gram language models (BNLM), BNLM approximation of RNNLMs (RNN-BNLM) and RNN n-grams in terms of perplexity and word error rate (WER). Morphological richness is often addressed by using statistically derived subwords - morphs - in the language models, hence our investigations are extended to morph-based models, as well. We found that using RNN-BNLMs 40% of the RNNLM perplexity reduction can be recovered, which is roughly equal to the performance of a RNN 4-gram model. Combining morph-based modeling and approximation of RNNLM, we were able to achieve 8% relative WER reduction and preserve real-time operation of our conversational telephone speech recognition system.
Efficient Detection and Quantification of Timing Leaks with Neural Networks

Detection and quantification of information leaks through timing side channels are important to guarantee confidentiality. Although static analysis remains the prevalent approach for detecting timing side channels, it is computationally challenging for real-world applications. In addition, the detection techniques are usually restricted to “yes” or “no” answers. In practice, real-world applications may need to leak information about the secret. Therefore, quantification techniques are necessary to evaluate the resulting threats of information leaks. Since both problems are very difficult or impossible for static analysis techniques, we propose a dynamic analysis method. Our novel approach is to split the problem into two tasks. First, we learn a timing model of the program as a neural network. Second, we analyze the neural network to quantify information leaks. As demonstrated in our experiments, both of these tasks are feasible in practice—making the approach a significant improvement over the state-of-the-art side channel detectors and quantifiers. Our key technical contributions are (a) a neural network architecture that enables side channel discovery and (b) an MILP-based algorithm to estimate the side-channel strength. On a set of micro-benchmarks and real-world applications, we show that neural network models learn timing behaviors of programs with thousands of methods. We also show that neural networks with thousands of neurons can be efficiently analyzed to detect and quantify information leaks through timing side channels.
The Method of Cutting Image of Vehicle Face based on Haar Feature and Improved Cascade Classifier

Abstract A method of car face image segmentation based on haar-like features and improved cascade classifier was proposed to solve the problems of insufficient classification recognition performance and too long training time of the current classifier. First, the haar-like features of the image are calculated based on integral graph. Then the extracted feature values are input into the cascade classifier for training. Finally, the trained classifier was used to detect the vehicle face and segment the image of the vehicle face. In this paper an adaptive threshold setting method is proposed. The experimental results show that the method has the better performance than the traditional methods, and has better application prospects.
Performance Evaluation of Big Data Processing at the Edge for IoT-Blockchain Applications

Internet-of-Things (IoT) utilising sensors is effective in performing continuous monitoring, while Blockchain is ideal in guaranteeing integrity and immutability of these IoT data. There are many challenges in integrating IoT and Blockchain together mainly because IoT devices have limited computational resources, and storage capacity while Blockchain processing incurs high CPU cost and high latency in data transfer. We propose a fully distributed edge computing architecture coupled with an efficient storage system that is based on Non-Volatile Memory express Over Fabrics (NVMeOF) to provide efficient IoT data processing for supply chain management. The data is secured using Blockchain at the edge to ensure traceability, security and non-repudiation in the data. An evaluation of our implementation and performance comparison between NVMeOF and SATA storage interfaces for our IoT-Blockchain architecture is presented.
Errata on “Quantifying Differential Privacy in Continuous Data Release under Temporal Correlations”

Presents revisions to the above named paper.
MODELHealth: Facilitating Machine Learning on Big Health Data Networks

MODELHealth is a platform that aims to facilitate the implementation of Machine Learning (ML) techniques on medical data in order to upgrade the delivery of healthcare services. MODELHealth platform is a "holistic" approach to the implementation of processes for the development and utilization of ML algorithms in many forms, including Neural Networks, and can be used to assist clinical work and administrative decision-making. It covers the entire lifecycle of these processes, from pumping, homogenization, anonymization, and enrichment of the initial data, to the final disposal of efficient algorithms through Application Program Interfaces for consumption by any authorized Information System.
Welding robotic hand-eye calibration method based on structured light plane

The calibration of the relationship between the welding robot hand and the camera is an important issue in the process of industrial welding intelligentization. For the welding robot with end-mounted line structured light, this paper proposes a novel method of robot hand-eye calibration based on structured light plane. Firstly, move the the position of robot to make structured light plane intersect with the marker points multiple times and the image coordinates of the marker points are obtained. Then construct and solve linear equations to obtain the least squares solution of the calibration matrix. Finally, use the algorithm of integrated learning method bagging to obtain the average of the least squares solution. Compared with the traditional complicated calibration method, this method doesn't need a specific standard workpiece or calibration board which makes the calibration process more convenient. The results of experiment show that the calibration three-axis measurement accuracy is within ±0.20mm, which meet the requirements of industrial field welding precision.
USING SCIENTIFIC E-CONFERENCES FOR THE RESEARCH COMPETENCE DEVELOPMENT: STUDENTS’ POINT OF VIEW

The article discusses the methodology of using a scientific electronic conference for the development of research competence of university students. A model of the use of a scientific electronic conference for the development of students' research competence was developed. It includes the following components: purpose, procedural, content-technological, diagnostic. It is proposed to use a process approach for the development of research competence through electronic scientific conferences. The purpose, activities, means, forms, methods and research skills that will be formed as a result of each process were determined. The system of indicators was developed to determine the level of research competence for such constituents as: methodology, information search, problem solving, quantitative analysis and communication. The types of activities occurring at each stage of the process are determined, in particular, the means and electronic content used for their implementation, the forms and methods of efficient use of electronic means, competences that are formed during the implementation of the model. Stages of students’ preparation for and participation in an electronic conference in cooperation with lecturers and experts (reviewers) that will form not only the research competence of students, but also their digital competence (digital literacy) are presented. The efficiency of using scientific electronic conferences as an instrument for the development of students' research competence has been proved. In particular, the results of the student survey on the development of constituents of research competence are presented. The students of the National University of Life and Environmental Sciences of Ukraine and Borys Grinchenko Kyiv University who have an intermediate and high level of research competence have noted the positive impact of lead-up and participation in scientific e-conferences on the development of students’ research competence.
Domain-Specific Language for Infrastructure as Code

With increasing number of cloud providers and offered cloud services, the need of effective deployment and portability of software application infrastructures in the cloud environments is becoming more essential. The paper provides a method of unified description and creation of infrastructures, including hardware and software requirements. It describes the developed Ansible module which deploys the required infrastructure in specified cloud environment depending on specific description. The module uses model and language of the TOSCA standard (Topology and Orchestration Specification for Cloud Applications) to describe the relevant node requirements of infrastructure and orchestration tool Ansible to deploy infrastructure in cloud environment. The paper also provides instructions to add new provider support in developed module and describes the maps between TOSCA elements and resources provided by OpenStack and Amazon. With increasing number of cloud providers and offered cloud services, the need of effective deployment and portability of software application infrastructures in the cloud environments is becoming more essential. The paper provides a method of unified description and creation of infrastructures, including hardware and software requirements. It describes the developed Ansible module which deploys the required infrastructure in specified cloud environment depending on specific description. The module uses model and language of the TOSCA standard (Topology and Orchestration Specification for Cloud Applications) to describe the relevant node requirements of infrastructure and orchestration tool Ansible to deploy infrastructure in cloud environment. The paper also provides instructions to add new provider support in developed module and describes the maps between TOSCA elements and resources provided by OpenStack and Amazon.
The Mediating Effects of Academic Performance between Screen Time, Executive Function Difficulty and School Adjustment

The purpose of this study was to verify the mediating effects of academic performance on the relationship between screen time, executive function difficulty, and school adjustment. This study analyzed secondary data of the 8th Panel Survey on Korean Children. The 8th Panel Survey was the first measurement of children’s academic performance, executive function difficulty, and school adjustment through large sampling. Accordingly, it is possible to provide a large amount of data that can verify the relationship between them. The respondents were parents and teachers of students. The total number was 1,031. Data were analyzed by descriptive, t-test, analysis of variance, Pearson correlation, and three-steps multiple regression statistics. As a result, screen time has a full mediating effect on school adjustment through academic performance. The executive function difficulty has a partial mediating effect on school adjustment through academic performance. The results of this study will contribute to the development of the school adjustment improvement program.
Rapid Prototyping Formal Systems in MMT: 5 Case Studies

Logical frameworks are meta-formalisms in which the syntax and semantics of object logics and related formal systems can be defined. This allows object logics to inherit implementations from the framework including, e.g., parser, type checker, or module system. But if the desired object logic falls outside the comfort zone of the logical framework, these definitions may become cumbersome or infeasible. Therefore, the MMT system abstracts even further than previous frameworks: it assumes no type system or logic at all and allows its kernel algorithms to be customized by almost arbitrary sets of rules. In particular, this allows implementing standard logical frameworks like LF in MMT. But it does so without chaining users to one particular meta-formalism: users can flexibly adapt MMT whenever the object logic demands it. In this paper, we present a series of case studies that do just that, defining increasingly complex object logics in MMT. We use elegant declarative logic definitions wherever possible, but inject entirely new rules into the kernel when necessary. Our experience shows that the MMT approach allows deriving prototype implementations of very diverse formal systems very easily and quickly.
Learning from Imbalanced Data: A Comparative Study

Learning from imbalanced data is a great challenge when we use machine learning techniques to solve real-world problems. Imbalanced data can result in a classifier’s sub-optimal performance. Moreover, the distribution of the testing data may differ from that of the training data, thus the true mis-classification costs is hard to predict at the time of learning. In this paper, we present a comparative study on various sampling techniques in terms of their effectiveness in improving machine learning performance against class imbalanced data sets. In particular, we evaluate ten sampling techniques such as random sampling, cluster-based sampling, and SMOTE. Two widely used machine learning algorithms are applied to train the base classifiers. For the purpose of evaluation, a number of data sets from different domains are used and the results are analysed based on different metrics.
Training Invertible Neural Networks as Autoencoders

Autoencoders are able to learn useful data representations in an unsupervised matter and have been widely used in various machine learning and computer vision tasks. In this work, we present methods to train Invertible Neural Networks (INNs) as (variational) autoencoders which we call INN (variational) autoencoders. Our experiments on MNIST, CIFAR and CelebA show that for low bottleneck sizes our INN autoencoder achieves results similar to the classical autoencoder. However, for large bottleneck sizes our INN autoencoder outperforms its classical counterpart. Based on the empirical results, we hypothesize that INN autoencoders might not have any intrinsic information loss and thereby are not bounded to a maximal number of layers (depth) after which only suboptimal results can be achieved.
Template-free Data-to-Text Generation of Finnish Sports News

News articles such as sports game reports are often thought to closely follow the underlying game statistics, but in practice they contain a notable amount of background knowledge, interpretation, insight into the game, and quotes that are not present in the official statistics. This poses a challenge for automated data-to-text news generation with real-world news corpora as training data. We report on the development of a corpus of Finnish ice hockey news, edited to be suitable for training of end-to-end news generation methods, as well as demonstrate generation of text, which was judged by journalists to be relatively close to a viable product. The new dataset and system source code are available for research purposes.
The Law of Armed Conflict Issues Created by Programming Automatic Target Recognition Systems Using Deep Learning Methods

Deep learning is a method of machine learning which has advanced several headline-grabbing technologies, from self-driving cars to systems recognising mental health issues in medical data. Due to these successes, its capabilities in image and target recognition is currently being researched for use in armed conflicts. However, this programming method contains inherent limitations, including an inability for the resultant algorithms to comprehend context and the near impossibility for humans to understand the decision-making process of the algorithms. This can lead to the appearance that the algorithms are functioning as intended even when they are not. This chapter examines these problems, amongst others, with regard to the potential use of deep learning to programme automatic target recognition systems, which may be used in an autonomous weapon system during an armed conflict. This chapter evaluates how the limitations of deep learning affect the ability of these systems to perform target recognition in compliance with the law of armed conflict. Ultimately, this chapter concludes that whilst there are some very narrow circumstances where these algorithms could be used in compliance with targeting rules, there are significant risks of unlawful targets being selected. Further, these algorithms impair the exercise of legal duties by autonomous weapon system operators, commanders, and weapons reviewers. As such, this chapter concludes that deep learning-generated algorithms should not be used for target recognition by fully-autonomous weapon systems in armed conflicts, unless they can be made in such a way as to understand the context of targeting decisions and be explainable.
Compiling and Filtering ParIce: An English-Icelandic Parallel Corpus

We present ParIce, a new English-Icelandic parallel corpus. This is the first parallel corpus built for the purposes of language technology development and research for Icelandic, although some Icelandic texts can be found in various other multilingual parallel corpora. We map out which Icelandic texts are available for these purposes, collect aligned data and align other bilingual texts we acquired. We describe the alignment process and how we filter the data to weed out noise and bad alignments. In total we collected 43 million Icelandic words in 4.3 million aligned segment pairs, but after filtering, our corpus includes 38.8 million Icelandic words in 3.5 million segment pairs. We estimate that approximately 5% of the corpus data is noise or faulty alignments while more than 50% of the segments we deleted were faulty. We estimate that our filtering process reduced the number of faulty segments in the corpus by more than 60% while only reducing the number of good alignments by approximately 8%.
An efficient storage and service method for multi-source merging meteorological big data in cloud environment

Abstract With the development of the meteorological IoT (Internet of Things) and meteorological sensing network, the collected multi-source meteorological data have the characteristics of large amount of information, multidimensional and high accuracy. Cloud computing technology has been applied to the storage and service of meteorological big data. Although the constant evolution of big data storage technology is improving the storage and access of meteorological data, storage and service efficiency is still far from meeting multi-source big data requirements. Traditional methods have been used for the storage and service of meteorological data, and a number of problems still persist, such as a lack of unified storage structure, poor scalability, and poor service performance. In this study, an efficient storage and service method for multidimensional meteorological data is designed based on NoSQL big data storage technology and the multidimensional characteristics of meteorological data. In the process of data storage, multidimensional block compression technology and data structures are applied to store and transmit meteorological data. In service, heterogeneous NoSQL common components are designed to improve the heterogeneity of the NoSQL database. The results show that the proposed method has good storage transmission efficiency and versatility, and can effectively improve the efficiency of meteorological data storage and service in meteorological applications.
RANCANG BANGUN SISTEM REKOMENDASI DESTINASI WISATA DI BALI

Bali is one of the provinces in Indonesia which is famous for its tourism. Bali is visited by many tourists both local and foreign tourists because it is famous for its culture and natural beauty. The problem experienced by tourists is the difficulty in determining the tourism objects to be visited in accordance with the wishes of these tourists. Difficulty in determining tourism objects is caused by limited information about tourism objects in Bali. The tourist destination recommendation system in Bali can be a solution to provide information about tourism objects in Bali and can provide recommendations on tourism objects to be visited according to the criteria entered by the user. This system is a geographic information system that can provide location information and routes to tourism objects chosen by users. This system is created using the concept of mobile web, so that each device is able to use the system only with a web browser. This system was built using the PHP programming language and MySQL database. Based on the tests that have been done, the system has been running according to its function.