Reviewerly/exHarmony huggingface dataset:
WatchControl: A Control for Interactive Movie Using Continuous Gesture Recognition in Smartwatches

This work proposes the development of a method to allow the use of smartwatch as a control for interactive movies using continuous recognition of gestures in smartwatches. We developed two prototypes to interact with interactive movies. The first one uses gesture recognition on the smartwatch screen. Thus, we created a set of gestures composed of straight lines that represent the actions in the interactive movie. In this prototype, the recognition of gestures is performed by the algorithm of continuous recognition of gestures. In this way, a gesture can be recognized before the user finalizes, and the action is sent quickly to the movie. The second prototype uses a touch of pressure in the smartwatch to control the film. As a study case, we have conducted our user study with the movie "Black Mirror: Bandersnatch", with usability and experience evaluation. A result has shown that the gestures proposed in the method are intuitive and that it has the potential to be used pleasantly and satisfactorily by the users. For 90% of the participants, the use of the smartwatch is more practical and easy to control Netflix compared to the remote control.
Numerical Non-Linear Modelling Algorithm Using Radial Kernels on Local Mesh Support

Estimation problems are frequent in several fields such as engineering, economics, and physics, etc. Linear and non-linear regression are powerful techniques based on optimizing an error defined over a dataset. Although they have a strong theoretical background, the need of supposing an analytical expression sometimes makes them impractical. Consequently, a group of other approaches and methodologies are available, from neural networks to random forest, etc. This work presents a new methodology to increase the number of available numerical techniques and corresponds to a natural evolution of the previous algorithms for regression based on finite elements developed by the authors improving the computational behavior and allowing the study of problems with a greater number of points. It possesses an interesting characteristic: Its direct and clear geometrical meaning. The modelling problem is presented from the point of view of the statistical analysis of the data noise considered as a random field. The goodness of fit of the generated models has been tested and compared with some other methodologies validating the results with some experimental campaigns obtained from bibliography in the engineering field, showing good approximation. In addition, a small variation on the data estimation algorithm allows studying overfitting in a model, that it is a problematic fact when numerical methods are used to model experimental values.
Co-creation in Practice I: Co-creating a Digital Neighbourhood Guide (Bremen Osterholz)

Abstract This chapter reports on a co-creation project that was conducted in the city district Bremen Osterholz. A core group of 11 older residents co-created a digital district guide over the duration of ten months. In a first step, the group identified an information gap between existing neighbourhood resources to support older residents and older residents’ awareness about them. The solution proposed was to better inform older adults in order to facilitate social participation in their everyday lives (ranging from consulting services to social encounters and outdoor activities). Members of the core group defined the specific information needs (including relevant attributes for each information category) and collected data for all categories. In addition, focus groups with 80 older residents were conducted with the support of local social care service providers. The focus groups were used partly for collecting information about the district, but also for testing the first prototype. The chapter describes co-creation methods such as cultural probes and data tables. It concludes with lessons learned.
Multi-Referenced Training for Dialogue Response Generation

In open-domain dialogue response generation, a dialogue context can be continued with diverse responses, and the dialogue models should capture such one-to-many relations. In this work, we first analyze the training objective of dialogue models from the view of Kullback-Leibler divergence (KLD) and show that the gap between the real world probability distribution and the single-referenced data’s probability distribution prevents the model from learning the one-to-many relations efficiently. Then we explore approaches to multi-referenced training in two aspects. Data-wise, we generate diverse pseudo references from a powerful pretrained model to build multi-referenced data that provides a better approximation of the real-world distribution. Model-wise, we propose to equip variational models with an expressive prior, named linear Gaussian model (LGM). Experimental results of automated evaluation and human evaluation show that the methods yield significant improvements over baselines.
Calibrating Self-supervised Monocular Depth Estimation

In the recent years, many methods demonstrated the ability of neural networks to learn depth and pose changes in a sequence of images, using only self-supervision as the training signal. Whilst the networks achieve good performance, the often over-looked detail is that due to the inherent ambiguity of monocular vision they predict depth up to an unknown scaling factor. The scaling factor is then typically obtained from the LiDAR ground truth at test time, which severely limits practical applications of these methods. In this paper, we show that incorporating prior information about the camera configuration and the environment, we can remove the scale ambiguity and predict depth directly, still using the self-supervised formulation and not relying on any additional sensors.
Architectural design and manual graphics in the student’s educational activities, their relationship with the mental peculiarities of a future specialist

Abstract The article presents the results of the scientific studies of the plasticity / rigidity of thinking among the bachelors of the Academy of Architecture and Arts, receiving training in the specialties: architecture, design of the architectural environment. In the field of modern architectural design, there is a process of reanimating the importance of hand-made graphics as an indispensable component of professional competence, which cannot but affect the content of training programs in training the specialists in this field. The author proves the hypothesis that there is a relationship between the abilities of students to use manual graphics in the design of projects with such a property of thinking as creative flexibility. The deliberate development of this ability through the educational tools will optimize the students’ assimilation of basic professional competencies in the field of architecture and design, increase the competitiveness of a university graduate.
PENERAPAN MODEL PEMBELAJARAN PANEL DISCUSSION DALAM PEMBELAJARAN KAIWA

This study aimed to enhance learners Japanese speaking skills and to developed a learning model that suitable for kaiwa course in a huge class (number of participants were more than 20 people). This research was conducted with descriptive research method to describe kaiwa class situation conducted with panel discussion model. Data collection conducted with survey questionnaire and some short interview. Research subjects was the final grade students of Prodi Sastra Jepang, Universitas Komputer Indonesia. The result of survey questionnaire showed that panel discussion model gives more opportunity to all kaiwa class participants to speak Japanese and express their own current research idea than the traditional way of kaiwa learning model. In addition, learners also showed a positive attitude after following the kaiwa class conducted with the panel discussion model, in term of the development of their own current research direction. Research subject stated that they gained a lot of valuable information about their current research from their classmates through question answer sessions. Panel discussion learning model also encourages learners to be more active in the classroom and focusing the class into a Students Centered Learning (SCL) situation.
The Quality of Aesthetic Development in The Context of An Art Festival as A Condition for The Overall Development of Children and Adolescents

The article examines the problem of the aesthetic development of children and adolescents as a fundamental condition for the quality of education and the modern approach to educational process organization in the context of interaction of general and additional education. The authors disclose this direction of research from the point of a long-term experimental study conducted by researchers at the Institute of Art Education and Cultural Studies of the Russian Academy of Education in the period from the middle of the 20th and the beginning of the 21st century. The analysis of materials of a substantial scientific study of the problem of measuring the aesthetic development of children on the example of Russia and the studies by the leading foreign scientists is carried out. The authors raise the issues related to solving this problem in modern educational organizations and schools. The article identifies the modern pedagogical principles and conditions for the implementation of the basics of aesthetic development including the aesthetic perception of the works of art by children of different ages in the practice of children’s educational organizations. The resolution of the problem of high-quality modern education is found in creating a well-organized subject and spatial environment, changing all the components of education and upbringing, and highlighting the fundamental cultural core in them.
The Impact of Principal Leadership Style on Teachers’ Happiness and Consequently Their Self Efficacy

This article assumes that a school principal is the most important figure in every school. It also assumes that principal style of leadership is significant factor affecting teachers’ happiness at school and their job satisfaction. Based on these assumptions, the article examines the impact of principal leadership style on teachers’ happiness and through this variable on their self-efficacy. The study examines the influence of each the three major leadership styles: transformational, rewarding and avoiding on teachers’ happiness. Teachers’ happiness is measured through the commitment of a teacher to the school objectives, where commitment is measured through his/her compliance with the school goals, identification with the school staff and internalization of the school values. Finally, self efficacy is measured through the degree of success in realizing the school goals and missions. The findings of the study point to a positive impact of the transformational and to a less extent rewarding leadership styles on teachers’ happiness and self efficacy.
Reducing false-positive biopsies with deep neural networks that utilize local and global information in screening mammograms

Breast cancer is the most common cancer in women, and hundreds of thousands of unnecessary biopsies are done around the world at a tremendous cost. It is crucial to reduce the rate of biopsies that turn out to be benign tissue. In this study, we build deep neural networks (DNNs) to classify biopsied lesions as being either malignant or benign, with the goal of using these networks as second readers serving radiologists to further reduce the number of false positive findings. We enhance the performance of DNNs that are trained to learn from small image patches by integrating global context provided in the form of saliency maps learned from the entire image into their reasoning, similar to how radiologists consider global context when evaluating areas of interest. Our experiments are conducted on a dataset of 229,426 screening mammography exams from 141,473 patients. We achieve an AUC of 0.8 on a test set consisting of 464 benign and 136 malignant lesions.
Teleporting an unknown quantum state to a photon with prior quantum information

Quantum teleportation provides a disembodied way to transfer an unknown quantum state from one quantum system to another. However, all teleportation experiments to date are limited to cases where the target quantum system contains no prior quantum information. Here we propose a scheme for teleporting a quantum state to a quantum system with prior quantum information. By using an optical qubit-ququart entangling gate, we have experimentally demonstrated the new teleportation protocol -- teleporting a qubit to a photon preloaded with one qubit of quantum information. After the teleportation, the target photon contains two qubits of quantum information, one from the teleported qubit and the other from the pre-existing qubit. The teleportation fidelities range from $0.70$ to $0.92$, all above the classical limit of $2/3$. Our work sheds light on a new direction for quantum teleportation and demonstrates our ability to implement entangling operations beyond two-level quantum systems.
The Application of Non-linear Cubic Regression in Rice Yield Predictions

The rice yields have fluctuated in Wonogiri Regency. This occasion happened in 2016-2018. Therefore, a prediction is needed to know whether rice yields will increase or decrease in the following year. The purpose of this study was to apply the polynomial non-linear regression method of third-degree in predicting rice yields. This study utilized the Unified Modeling Language (UML) as the system design, black-box testing as the functional testing, and MSE testing as the validity testing. The computed data was data of 2016-2018. The results showed that the prediction of 2017-2019 using the harvested area model produced more accurate calculations. The harvested area model produced the same MSE value in manual and application calculations, which were 405433,1349 in 2017, 312677,7798 in 2018, and 171183.6347 in 2019. The polynomial non-linear cubic regression is a solution to predict rice yields. The output of the application is the prediction information for rice yields
Estimation of Individual Device Contributions for Incentivizing Federated Learning

Federated learning (FL) is an emerging technique used to train a machine-learning model collaboratively using the data and computation resource of the mobile devices without exposing privacy-sensitive user data. Appropriate incentive mechanisms that motivate the data and mobile-device owner to participate in FL is key to building a sustainable platform for FL. However, it is difficult to evaluate the contribution level of the devices/owners to determine appropriate rewards without large computation and communication overhead. This paper proposes a computation-and communication-efficient method of estimating a participating device's contribution level. The proposed method enables such estimation during a single FL training process, there by reducing the need for traffic and computation overhead. The performance evaluations using the MNIST dataset show that the proposed method estimates individual participants' contributions accurately with 46-49% less computation overhead and no communication overhead than a naive estimation method.
Body-Posture Recognition by Undergraduate Students Majoring in Physical Education and Other Disciplines

Humans are more proficient at processing visual display of body posture when the body is in upright orientation, compared to when inverted (inversion effect). Here we investigated whether extensive exposure or expertise on body posture recognition would affect the efficiency with which body-posture is processed. Using whole-body and piecemeal-body postures as stimuli, we performed two experiments to investigate whether body-posture recognition differed between two groups of participants: undergraduates majoring in physical education (PE) and those in other subjects (non-PE), respectively. These two groups differed significantly in the frequency and intensity of exercise per day and/or accumulated exercise time. In our experiments, following initial presentation of an image of a body posture, participants were shown the same or a different stimulus and were asked to report whether or not they had been previously shown the same image. The orientations of the body postures were also varied between trials. Our results showed that, in Experiment 1, for whole-body posture recognition, both the PE and non-PE groups showed a robust body-inversion effect in terms of both error rate and reaction time (RT), but the magnitude of the body-inversion effect in the RT measure was greater in the PE than the non-PE group. In Experiment 2, for piecemeal-body postures, both groups showed the inversion effect in terms of both error rate and RT measures and the PE group made fewer overall errors than the non-PE group. These cumulative results suggest that a superiority effect exists for PE participants compared with non-PE participants. Our results are generally consistent with the expertise hypothesis.
Towards an Access Regime for Mobility Data

(Forced) access to digital resources requires a legal framework which is at least partly sector-specific. Regarding the important sector of connected mobility, this paper tries to push the quest for such a framework. It analyzes data-specific market conditions in the sector and the need for intervention they generate, as well as potentially helpful legal tools in the GDPR (data portability) and core competition law (e.g. essential facilities doctrine, relative market power, pertinent EU Regulations, new tools in the 10th revision of the German Act Against Restraints of Competition). Based on these findings, the paper develops cornerstones for a regulatory, yet stakeholder-oriented approach, flexibly tuned with contract, competition and data protection law. Not least, participants of connected mobility markets should take up this idea, as they have a lot to contribute to its quality and a lot to lose if inappropriate rules came to be set.
div2vec: Diversity-Emphasized Node Embedding

Recently, the interest of graph representation learning has been rapidly increasing in recommender systems. However, most existing studies have focused on improving accuracy, but in real-world systems, the recommendation diversity should be considered as well to improve user experiences. In this paper, we propose the diversity-emphasized node embedding div2vec, which is a random walk-based unsupervised learning method like DeepWalk and node2vec. When generating random walks, DeepWalk and node2vec sample nodes of higher degree more and nodes of lower degree less. On the other hand, div2vec samples nodes with the probability inversely proportional to its degree so that every node can evenly belong to the collection of random walks. This strategy improves the diversity of recommendation models. Offline experiments on the MovieLens dataset showed that our new method improves the recommendation performance in terms of both accuracy and diversity. Moreover, we evaluated the proposed model on two real-world services, WATCHA and LINE Wallet Coupon, and observed the div2vec improves the recommendation quality by diversifying the system.
DeltaGAN: Towards Diverse Few-shot Image Generation with Sample-Specific Delta

Learning to generate new images for a novel category based on only a few images, named as few-shot image generation, has attracted increasing research interest. Several state-of-the-art works have yielded impressive results, but the diversity is still limited. In this work, we propose a novel Delta Generative Adversarial Network (DeltaGAN), which consists of a reconstruction subnetwork and a generation subnetwork. The reconstruction subnetwork captures intra-category transformation, i.e., delta, between same-category pairs. The generation subnetwork generates sample-specific delta for an input image, which is combined with this input image to generate a new image within the same category. Besides, an adversarial delta matching loss is designed to link the above two subnetworks together. Extensive experiments on six benchmark datasets demonstrate the effectiveness of our proposed method. Our code is available at https://github.com/bcmi/DeltaGAN-Few-Shot-Image-Generation.
Unsupervised Parallel Corpus Mining on Web Data

With a large amount of parallel data, neural machine translation systems are able to deliver human-level performance for sentence-level translation. However, it is costly to label a large amount of parallel data by humans. In contrast, there is a large-scale of parallel corpus created by humans on the Internet. The major difficulty to utilize them is how to filter them out from the noise website environments. Current parallel data mining methods all require labeled parallel data as the training source. In this paper, we present a pipeline to mine the parallel corpus from the Internet in an unsupervised manner. On the widely used WMT'14 English-French and WMT'16 English-German benchmarks, the machine translator trained with the data extracted by our pipeline achieves very close performance to the supervised results. On the WMT'16 English-Romanian and Romanian-English benchmarks, our system produces new state-of-the-art results, 39.81 and 38.95 BLEU scores, even compared with supervised approaches.
EfficientSeg: An Efficient Semantic Segmentation Network

Deep neural network training without pre-trained weights and few data is shown to need more training iterations. It is also known that, deeper models are more successful than their shallow counterparts for semantic segmentation task. Thus, we introduce EfficientSeg architecture, a modified and scalable version of U-Net, which can be efficiently trained despite its depth. We evaluated EfficientSeg architecture on Minicity dataset and outperformed U-Net baseline score (40% mIoU) using the same parameter count (51.5% mIoU). Our most successful model obtained 58.1% mIoU score and got the fourth place in semantic segmentation track of ECCV 2020 VIPriors challenge.
Dictionary lookup with one genome evolution operation

Summary Given an m ‐length query string q , approximate dictionary lookup searches for strings in a string dictionary D at a distance of 1 to q under some distances. In biological retrieval systems, the distances in such queries are defined by evolution operations on genomes. We consider the approximate dictionary lookup with one genome evolution operation including reversal and transposition, which searches for strings in D that can be generated from q by one reversal or one transposition. When the length of the reversed substring is confined to a constant α &gt;1, we propose an O ( m )‐time approach which uses bits space, where the dictionary D has d strings with totally | D | symbols. If the lengths of the reversals are in a range [ α , β ], the time for query is , and the space is words for any constant ε , in which occ is the number of matched strings. For problems allowing one transposition, when the length of the transposition is fixed to α , the time for a dictionary lookup is , while using words. In the case that the two swapped substrings are of the same length, the time for answering the query is O ( m ), while the space is bits.