Reviewerly/exHarmony huggingface dataset:
Smartphone As a Stand-Alone Device for Rendering, Visualization and Tracking for Use During Product Development in Virtual Reality (VR)

Abstract Product development process makes use of different models, methods and available technologies to validate product requirements at an early stage. Technologies like Virtual Reality (VR) and Augmented Reality (AR) are increasingly finding their application in industry and in the product development process. VR has the potential to support the product evaluation process. However, the costly preparation of VR scenes and the expensive hardware needed for the implementation greatly limits its use in the industry. Normally, the different VR content development and rendering softwares are application specific and offer very limited interoperability or reuse of created content. In addition, the needed hardware usually consists of dedicated visualization computers, tracking sensors, interaction devices and requires a fixed installation space. The motivation for the work presented in this paper is to reduce the preparation effort, hardware cost and remove the need for a fixed installation usually required by VR. The High-End smartphones of today from almost all the manufacturers can facilitate the development and stand-alone execution of VR and AR applications. However, the major application of Smartphone Virtual Reality (SVR) is mainly in the entertainment industry i.e. VR-gaming or visualization applications etc. VR until today is thought to be a visualization technology that requires special hardware for its implementation. There are hardly any applications of SVR in the industry today mainly because of two reasons. First, the position tracking against the user movement in the virtual scene is not readily available in SVR. It is usually achieved by attaching additional tracking devices to smartphones. Secondly, the smartphones themselves are not perceived as high-performance devices suitable for industrial simulations. This paper discusses a method for efficient preparation of VR-scene along with their behavior simulations and presents a novel SVR application that eliminates the need for expensive VR hardware. The challenges and the requirements from the SVR along with the possible solutions are discussed in detail. The presented SVR application includes product behavior simulation and wireless communication with a laptop. An example product along with its behavior simulation sums-up the application of SVR in the product development process. The position tracking in SVR is implemented and an experiment compares the precision of this position tracking with the tracking achieved by a Head Mounted Display (HMD). The experimental setup along with the achieved results are also discussed. A user survey conducted for the smartphone VR application and the feedback of this survey is also presented in this paper.
Background by Design: Listening in the Age of Streaming

By first revisiting the design of Amsterdam's famed 'Concertgebouw', this article reflects on the implications of streaming services on current and future cultures of listening, and on the agency and autonomy of practicing musicians.
Cross-Family Similarity Learning for Cognate Identification in Low-Resource Languages

We address the problem of cognate identification across vocabularies of any pair of languages.In particular, we focus on the case where the examined languages are low-resource, to the extent that no training data whatsoever in these languages, or even closely related ones, is available for the task.We investigate the extent to which training data from another, unrelated language family can be used instead.Our approach consists of learning a similarity metric from example cognates in Indo-European languages and applying it to low-resource Sami languages of the Uralic family.We apply two models, following previous work: a Siamese convolutional neural network (S-CNN) and a support vector machine (SVM), and compare them with a Levenshtein distance baseline.We test performance on three Sami languages and find that the S-CNN outperforms the other approaches, suggesting that it is better able to learn such general characteristics of cognateness that carry over across language families.We also experiment with fine-tuning the S-CNN model with data from within the language family in order to quantify how well this model can make use of a small amount of target-domain data to adapt.
Virtual and Augmented Reality in the Teaching and Learning of Programming: A Systematic Mapping Study

As programming becomes a fundamental course in several areas of knowledge and a desirable competence in different sectors of society, many teaching and learning approaches have been developed to support students and instructors. This study aims to understand and characterize the research area that merges Virtual (VR) and Augmented Reality (AR) with the teaching and learning of programming, since both VR and AR have become popular and affordable nowadays. To achieve these goals, we conducted a systematic mapping study. The results reinforce the adoption of VR and AR as tools to support the visualization and practice of programming concepts, as well as to increase students' motivation and engagement. Results also showed gaps, mainly in relation to the use of mobile devices and immersive VR.
Experimental Analysis of Single Mode Switching Techniques in Augmented Reality

This paper presents an empirical evaluation of mode switching techniques for Augmented Reality (AR) headsets. We conducted a quantitative analysis exploring five techniques for switching between modes: a hardware button press, 3D virtual button press, non-preferred hand, reach depth, and voice. Results from our study support the benefits of non-preferred mode switching, showing non-preferred and depth mode switching to be faster than voice and the virtual button techniques. Depth, however, had significantly more errors compared to the other techniques. Our work lays a foundation for developers to design new mode switching techniques and guides the design of current hardware solutions around choosing techniques that best compliment application use.
Variational Conditional GAN for Fine-grained Controllable Image Generation

In this paper, we propose a novel variational generator framework for conditional GANs to catch semantic details for improving the generation quality and diversity. Traditional generators in conditional GANs simply concatenate the conditional vector with the noise as the input representation, which is directly employed for upsampling operations. However, the hidden condition information is not fully exploited, especially when the input is a class label. Therefore, we introduce a variational inference into the generator to infer the posterior of latent variable only from the conditional input, which helps achieve a variable augmented representation for image generation. Qualitative and quantitative experimental results show that the proposed method outperforms the state-of-the-art approaches and achieves the realistic controllable images.
Automated Text Simplification as a Preprocessing Step for Machine Translation into an Under-resourced Language

In this work, we investigate the possibility of using fully automatic text simplification system on the English source in machine translation (MT) for improving its translation into an under-resourced language.We use the state-of-the-art automatic text simplification (ATS) system for lexically and syntactically simplifying source sentences, which are then translated with two state-of-the-art English-to-Serbian MT systems, the phrase-based MT (PBMT) and the neural MT (NMT).We explore three different scenarios for using the ATS in MT: (1) using the raw output of the ATS; (2) automatically filtering out the sentences with low grammaticality and meaning preservation scores; and (3) performing a minimal manual correction of the ATS output.Our results show improvement in fluency of the translation regardless of the chosen scenario, and difference in success of the three scenarios depending on the MT approach used (PBMT or NMT) with regards to improving translation fluency and post-editing effort.
Extensive Theory Testing Using Case Study

Extensive theory testing refers to the joint conduct of theory testing and extension in a single empirical study. One of the types of extensive theory testing is using case study approach. While quantitative approach to theory testing, such as survey method, focuses on corroborating or falsifying theory, extensive theory testing aims to both corroborate and extend theory at the same time. By employing extensive theory testing, researchers can simultaneously develop deeper understandings of an existing theory situated within a context and generate new insights based on the effort to test the existing theory. Thus, extensive theory testing offers a useful alternative, complementing quantitative approach to theory testing. Although case study has been used for theory testing, the rationale and procedures of extensive theory testing have not been adequately understood. In this paper, we clarify the rationale of extensive theory testing and propose a framework for the conduct of extensive theory testing.
Parameter Optimization Using PSO for Neural Network-Based Short-Term PV Power Forecasting in Indian Electricity Market

Because of developing concern to environmental changes, renewable energy is being looked as a key alternative to the conventional sources. Photovoltaic power is a green and an abundant renewable energy source. Photovoltaic power is dependent upon the solar irradiation which is highly intermittent in nature. So the precise forecasting of PV power is necessary to improve the operation of an electrical grid with the distributed energy resources. This paper proposes a novel hybrid model by combining particle swarm optimization (PSO) and the feed-forward neural network (FFNN) together. Proposed hybrid model is applied to forecast the PV power in Indian electricity market. One-year data consisting of hourly PV power generation, direct radiation, diffused radiation, and ambient temperature from the Indian energy market has been used for PV power forecasting. The developed model is applied for one-week ahead PV power forecasting for winter, summer, rainy, and autumn season, respectively. The performance of the proposed hybrid model outmatches and compared with some recently reported model.
Parameter Estimation for Lattice-Based Cryptosystems By Using Sieving Algorithms

The security of quantum resistant lattice-based cryptosystems mainly depend on the shortest vector problem (SVP) or the closest vector problem (CVP). In this paper, sieving algorithms such as ListSieve, GaussSieve, ProGaussSieve and HashSieve are discussed and compared in terms of their complexity and capabilities. The algorithm of Becker et al., used in LWE-Estimator software, is analyzed by considering HashSieve to generate reliable parameter sets. According to the experimental results, up to 81-bit security level, using HashSieve is comparable with other algorithms. It's shown that for low security levels small key sizes can be obtained.
Exploring cross-lingual word embeddings for the inference of bilingual dictionaries

We describe four systems to generate automatically bilingual dictionaries based on existing ones: three transitive systems differing only in the pivot language used, and a system based on a different approach which only needs monolingual corpora in both the source and target languages. All four methods make use of cross-lingual word embeddings trained on monolingual corpora, and then mapped into a shared vec- tor space. Experimental results confirm that our strategy has a good coverage and recall, achieving a performance comparable to to the best submitted systems on the TIAD 2019 gold standard set among the teams participating at the TIAD shared task.
Using the Method of Expert Assessments in the System for Monitoring the Quality of Professional Development of Specialists in the Field of Professional Education

The problem of using expert assessments in the system for monitoring the quality of professional development of specialists in the field of professional education is highlighted. It has been established that high demands on the quality of education in general, and the advanced training the system, in particular, require the involvement of specialists who have special knowledge and qualifications in relevant fields and can objectively assess the quality indicators of advanced training. Expert and analytical work, on the one the hand will contribute to increasing the efficiency of structural units of vocational education institutions, and on the other, reducing the risk of making ineffective management decisions. The scientific research of scientists on the relationship between the concepts of monitoring, control and expertise is considered. The analysis of general and additional (increased) requirements for the qualifications of potential experts are carried out, the indicators of expert competency are calculated, and the quantitative and qualitative composition of the expert the group is determined.
Affluence boosted intelligence? How the interaction between cognition and environment may have produced an eighteenth-century Flynn effect during the Industrial Revolution

Abstract Cognition played a pivotal role in the acceleration of technological innovation during the Industrial Revolution. Growing affluence may have provided favourable environmental conditions for a boost in cognition, enabling individuals to tackle more complex (industrial) problems. Dynamical systems thinking may provide useful tools to describe sudden transitions like the Industrial Revolution, by modelling the recursive feedback between psychology and environment.
Annotation Guideline No. 5: Annotation Guidelines for Narrative Levels and Narrative Acts

The annotation guidelines for narrative levels and narrative acts have been developed in conjunction with my master thesis, the goal of which is to distinguish plot relevant settings from rather mentioned spaces in literary texts. For this, the determination of narrative levels is a requirement to precisely classify settings. Nevertheless, the guidelines itself were developed independently from the spatial classification task.
Gaussian Process Priors for View-Aware Inference

While frame-independent predictions with deep neural networks have become the prominent solutions to many computer vision tasks, the potential benefits of utilizing correlations between frames have received less attention. Even though probabilistic machine learning provides the ability to encode correlation as prior knowledge for inference, there is a tangible gap between the theory and practice of applying probabilistic methods to modern vision problems. For this, we derive a principled framework to combine information coupling between camera poses (translation and orientation) with deep models. We proposed a novel view kernel that generalizes the standard periodic kernel in SO(3). We show how this soft-prior knowledge can aid several pose-related vision tasks like novel view synthesis and predict arbitrary points in the latent space of generative models, pointing towards a range of new applications for inter-frame reasoning.
Optimal Tracking Control for Discrete-time Systems with Time-delay Based on the Preview Control Method

Abstract In this paper, the optimal tracking control problem for discrete-time with state and input delays is studied based on the preview control method. First, a transformation is introduced. Thus, the system is transformed into a non-delayed system and the tracking problem of the time-delay system is transformed into the regulation problem of a non-delayed system via processing of the reference signal. Then, by applying the preview control theory, an augmented system for the non-delayed system is derived, and a controller with preview function is designed, assuming that the reference signal is previewable. Finally, the optimal control law of the augmented error system and the optimal control law of the original system are obtained by letting the preview length of the reference signal go to zero.
Mining Journals to the Ground: An Exploratory Analysis of Newspaper Articles

In this work, we propose an exploratory approach to analyze the categorization, spread and replication of news content in journalistic web portals. With an increasing number of possible sources, these analyses may aid on identifying reliable content sources from which people may extract information. Our process involves web scraping news portals to gather data, representing them using several Natural Language Processing techniques, and evaluation through Clustering and Similarity analysis. We have represented news articles using Bag of Words, TF-IDF, Paragraph Vector and Set of Named Entities, and applied Cosine and Jaccard similarity measures to compare them. In categorization we have used DBSCAN and Hierarchical Agglomerative clustering algorithms. Our results show that many articles may be placed in more than one category in order to reach their desired audience. The most relevant news in the observed time period were identified in multiple portals; and we have also identified similar behavior among the analyzed representations when evaluating spreading and replication.
SOA Integration for E-Marketplace

As E-Commerce has growth rapidly in Indonesia, it becomes a needs for small and medium enterprise (SMEs) to use couple of E-Commerce domains for business expansion purpose. This action needs a lot of effort and lack of flexibility. This study focuses on using Service Oriented Architecture (SOA) to carry out their online daily business with one another. Despite of using API-Method, the paper presents an integrated information or data that reduces redundancy process. This study is to proposed system architecture which create new solution based on SOA and ESB as the main middleware through internet. It called the SOA integration for e-marketplace. It will integrated all used E-Commerce domains to one platform to manage as Single Source The objective is for interconnecting between different domains and make it become interoperability and manageability. The impact of the interoperability, it could be possible to seller to manage product listing and order management from all domains, and gain customer's satisfaction growth and also fulfil the flexibility demand from SMEs and customers.
Reduced order model of glass plate loaded by low-velocity impact

This article concerns a reduced order model of unconstrained glass plate exposed to low-velocity impact.First, three-parametric model consisting of two masses connected with elastic spring is introduced, its calibration procedure is described, and the simulation of its response to force impulses with different duration is shown.Then a five-parametric variant of the reduced order model is presented, calibrated and tested.Combined with the Hertzian theory of non-adhesive contact, the model allows us to determine the time evolution of contact force for arbitrary mass, stiffness and initial velocity of the impactor.The simulated results are compared to experimentally obtained data and observations about the model properties and accuracy are made.
AI innovations to counter social challenges

Artificial intelligence (AI) is being harnessed to tackle two of the most challenging problems today – the flagrant proliferation of fake news and the increasing invasion of individual privacy. Factmata, which uses AI to fight disinformation and D-ID, which protects identities from facial recognition systems using AI, were two of the ten winners of the 2019 Netexplo awards, presented at UNESCO Headquarters in April.
Moving Human Focus Inference Model for Action Recognition

Spatio-temporal information is crucial to human action recognition. Inspired by the primary visual cortex of the human brain, a new end-to-end action recognition method based on deep learning was proposed to extract pure spatiotemporal information in videos. It is named moving human focus inference model that can capture long-term temporal information with low time costs and eliminates the interference of dynamic background. It obtains the temporal and spatial information in the video from the temporal pathway with the focus block and spatial pathway, respectively. Then the temporal and spatial information are merged as spatio-temporal information by fusion region. Finally, the softmax layer is used to recognize the actions, followed by the bayes inference block that is used to correct the recognition results through the methods of maximum posterior probability inference. The model is evaluated on two challenge datasets: UCF101 and HMDB51. The experimental results show that the approach has better accuracy and generalization ability compared to the state-of-the-art methods.
Independence-aware Advantage Estimation

Most of existing advantage function estimation methods in reinforcement learning suffer from the problem of high variance, which scales unfavorably with the time horizon. To address this challenge, we propose to identify the independence property between current action and future states in environments, which can be further leveraged to effectively reduce the variance of the advantage estimation. In particular, the recognized independence property can be naturally utilized to construct a novel importance sampling advantage estimator with close-to-zero variance even when the Monte-Carlo return signal yields a large variance. To further remove the risk of the high variance introduced by the new estimator, we combine it with existing Monte-Carlo estimator via a reward decomposition model learned by minimizing the estimation variance. Experiments demonstrate that our method achieves higher sample efficiency compared with existing advantage estimation methods in complex environments.
Do Alzheimer’s Patients Appear Younger than Their Ageƒ A Study with Automatic Facial Age Estimation

Facial age estimation from images is a challenging task, especially if the subjects are older, since idiosyncratic variations increase with age, and lifestyle factors have an impact on the appearance. In this paper, we test the hypothesis that the Alzheimer's patients appear younger than their real age. To do this objectively, and to be able to analyze the factors in case the hypothesis holds true, we use automatic age estimation methods for this task. We first propose training and normalization regimes to improve deep learning based facial age estimation. We fine-tune a pre-trained ImageNet model using first the APPA-REAL database and then the UTKFACE database. The experimental results show that the proposed approach predicts older faces more accurately compared to other studies, and improves the mean absolute error for the FG-NET database to 8.14 for the age group 60-69. We then run our approach on a special database collected from Alzheimer's patients and healthy controls, to test our main hypothesis. The database we collected for this purpose contains video recordings of 96 subjects with an age range between 64 and 87. Our findings show that automatic age estimation indeed underestimates the age of AD patients significantly more than the healthy subjects.