Reviewerly/exHarmony huggingface dataset:
PWA and TWA: Recent Development Trends

The continuous growth of the computing power of mobile devices and their relative low cost are some of the reasons for their great expansion. Currently, for example, in Argentina 9 out of 10 people own a smartphone. This trend, locally and globally, has been accompanied by the evolution of the software industry for these devices with the leadership of two operating systems: Android and iOS. This segmentation has generated the need to have mobile applications for both platforms and consequently different development approaches have emerged in response to this need.This paper presents the analysis of a recently emerged approach to mobile application development, called Progressive Web Applications or PWA, as a novel alternative to existing approaches. Additionally, the basics of TWA (Trusted Web Activities), used as a complement to PWA, are also introduced.
Slightly Above the Syrian Refugees: Media Representation of Street Vendors in Turkey

Does the media through its news convey us their categorisation, have particular lexical choices, give less space or voice to the actors regarding the news that mentioned street vendors in Turkey? The research question of this study is that whether or not the print media covers street vendors in their news? What is the tone of these news articles? In which pages do they cover? What are the news themes related with street vendors and who are the main actors in this news? This chapter presents a systematic study of 100 news articles which were published between 2016 and 2018 in Turkey’s mainstream, popular newspaper Hürriyet. The 100 news which included the word ‘street vendor’ in the texts were selected from the Hürriyet’s database and categorised and the content of the news articles were analysed. Findings of the of 100 news articles which mentioned ‘street vendor’ were researched and analysed totally. The analysis already reveals that the word choice of the news articles regarding street vendors are often conflict stories between the vendors and municipality police forces namely ‘Zabıta’ in Turkey. In sum, there were 19 news articles that referred street vendors with positive tone and wording, but 68 news articles still depicted street vendors in negative framing and use negative attribution in the text of the news. Totally 16 news articles included by-line and the rest of the 84 news articles did not include the journalist’s name and were covered as anonym. Out of the 100 news articles only a handful focussed on the advocacy and rights of street vendors.
Prop-Based Egocentric and Exocentric Virtual Object Storage Techniques

As Virtual and Augmented Reality tasks become increasingly complex, there is a need to organize virtual information within the virtual environment better. Users may need to organize their virtual objects or tools by hiding them until usage is needed or carry with them to another location. For these scenarios, there may be benefits to associate or store virtual objects in relation to one’s avatar (Egocentric) or in relation to the other virtual objects in the environment (Exocentric). This research presents these techniques and results of a user study comparing the effects of Egocentric and Exocentric Virtual Object Storage Techniques on cognition in a virtual environment.
Hybrid sampling method for autoregressive classification trees under density-weighted curvature distance

To improve human action recognition performance of visual data, we proposed Hybrid Sampling Ensemble Learning method. 1)define the curvature distance distribution to fit discrete images into continuous expandable surfaces. 2)obtain density-weighted curvature distance of the images through class imbalance adjustment. 3)use auto-regressive classification tree strategy by utilizing the hierarchical regress of the filtered images. The classification performance of the ensemble learning method is not as good as the deep learning framework. However, the parameters are interpretable, and the construction of the classification framework is simple. In short, the proposed ensemble model is robust and controllable.
A novel comprehensive database for arabic and english off-line handwritten digits recognition

&lt;span&gt;The recognition of Arabic handwritten is received at the same interest as other Latin languages. In Optical Character Recognition (OCR), handwriting Arabic recognition is considered as one of the critical and difficult tasks in the various scientific area. The main issues of this matter were due to the lack of public Arabic handwriting databases and the cursive nature of Arabic writing. In this paper, a new benchmark database is built for the Arabic and English off-line handwritten digits Recognition. The original form is divided into three groups: Arabic digits, English digits, and word Arabic digits which written five times by 100 different academic staff and students of university writers. Our database contains 14500 images; divided into two subsets of training and testing to help researchers through evaluating and comparing results obtained from their systems. &lt;/span&gt;
Axillary lymph node metastasis status prediction in ultrasound image using convolution neural network

For patients with early-stage breast cancer, the axillary lymph node (ALN) metastasis status is one of the important indicators in breast cancer staging and prognosis. In this study, a computer-aided prediction (CAP) system based on the ultrasound image using the deep learning method to determine the ALN status in breast cancer. In this study, the US imaging database contained 153 malignant tumor images which confirmed by histologically examine, and either SNB or ALND confirmed the axillary metastasis status. The Mask R-CNN method is used to indicate the tumor location and extract the tumor region. After the tumor region segmentation process, we obtained the surrounding tissue region (1 mm and 2 mm), which might include implicit information of the tumor metastasis mechanism. Finally, the convolution neural network (CNN)-based classifier is used to predict the ALN metastasis status using segmented images. In the experiments, the results show that the combined region (tumor with 2 mm surrounding tissue) image has the highest predict performance. The accuracy, sensitivity, specificity, and the area index (Az value) under the receiver operating characteristic (ROC) curve for the CAP system were 77.12%, 66.10%, 84.04%, and 0.7592 for using combined region (tumor with 2 mm surrounding tissue) images. These results indicated that the proposed CAP system can be helpful to determine the ALN status in patients with early-stage breast cancer.
Video super-resolution with phase-aided deformable alignment network

Video super-resolution (VSR) aims to restore a high-resolution frame from its corresponding reference frame and a series of neighboring frames in low resolution. Because of the displacement between the reference frame and each neighboring frame, which is caused by the motion of the camera or observed objects, VSR methods usually initially align the neighboring frames with the reference frame. However, the commonly used motion estimation and compensation methods highly depend on the predicted optical flow and they are affected by the lighting change. We propose a phase-aided deformable alignment network (PDAN) to alleviate the above problems. In PDAN, a phase-based method is introduced in the motion estimation subnetwork along with the traditional U-Net structure, which improves the restoration robustness in scenarios of lighting change. A deformable convolutional network is further adopted in the alignment subnetwork to enhance the alignment for objects with an irregular shape and motion distortion, which also avoids the dependence on explicit motion representation accuracy. Moreover, the reconstruction module is optimized for improved restoration. Extensive experiments conducted demonstrate that PDAN achieves state-of-the-art quantitative and qualitative performances.
Usefulness of the endosonography using 25 gauge core needles for immunohistochemistry and molecular testing in lung cancer patients: a feasibility study

needle biopsies can sometimes provide such a sample, but if EBUS -TBNA is not conclusive, mediastinoscopy is still a confirmatory test. 6 The aim of this study was to evaluate efficacy of EBUS/EUS -b-NA using histological 25G core needles in a detailed diagnosis of patients with advanced LC.Patients and methods A single -center prospective observational study was conducted in John Paul II Hospital in Kraków, Poland.Consecutive patients with confirmed or suspected advanced LC were included.Since the procedure is routinely used, the approval of bioethics committee was not required.After the informed consent was obtained, the EBUS/EUS -b-NA procedure was performed under mild conscious sedation with intravenous midazolam (2-5 mg) and fentanyl (0.025-0.1 mg).Flexible videobronchoscopes with a integrated convex ultrasound probe (BF -UC180F, Olympus Medical Systems Corporation, Tokyo, Japan), which can visualize mediastinal and hilar structures, were used for all procedures.The biopsies were performed with 25G core needles (Echo-Tip ProCore Endobronchial HD Biopsy Needle, Cook Endoscopy Inc., Limeric, Ireland) with 2 to 5 needle passes in at least 2 selected areas.Both the smears and the cell blocks were prepared from obtained samples.If the biopsy specimen was positive for LC, it was sent to perform IHC tests: thyroid transcription factor 1 (TTF-1), p40, CD56, Ki67.If non-small cell lung cancer was diagnosed, the specimen was sent to run molecular testing for mutations of epidermal growth factor receptor, translocations of anaplastic lymphoma
Genesis of methodological approaches to definition and organization of university information and education space

The paper presents the analysis of methodological approaches to the origin, formation and development of the ‘information and education space’ concept, and also notes the relevance of its application in contemporary society. The genesis of methodological approaches to the definition of an information and education space concept is based on a theoretical analysis and generalization of the philosophical, historical, pedagogical, sociological and cultural sciences, which made it possible to specify its essential content. It is indicated that the use of media resources, sites, instant messaging services acts as an organizational and pedagogical toolkit, which provides means for optimizing time management, managing on-line and off-line interactions as part of the learning process in present-day universities and colleges. The main purpose of this article is a brief analysis of a system-holistic, mental-emotional, personality-developing, socio-geographical, distance, and local-poster approaches to the essence of information and education space determination; and also the definition of this pedagogical phenomenon. The study has used various methods described in this paper: a hindsight analysis of domestic and foreign experience in the use of state-of-the-art IT solutions; theoretical methods; established pedagogical methods; empirical methods, an educational experiment; programmed learning techniques; interactive methods; forecasting methods; intuitive methods; methods of statistical data processing; methods for the study and generalization of contemporary pedagogical experience in the development of a personal information literacy in education. The result of the introduction of the methodological approaches described by the authors of this article to the organization of the educational information environment of the university was a positive dynamic in the development of information needs, reflection and self-assessment of educational and cognitive activities in the Internet environment, as well as assessing the ability of self-education and self-development among students of various profiles and areas of preparation.
ProsoBox, a Praat Plugin for Analysing Prosody

This contribution presents a Praat plugin for automatic prosodic analysis called ProsoBox. It consists of a collection of tools ran successively: stylization of f0, automatic segmentation of the sound into speech segments, automatic detection of prominent syllables, prosodic report. It produces various output formats: modification of the TextGrid with additional tiers; tables; dynamic or static graphical visualizations. ProsoBox is compared to other automatic tools for prosodic analysis to make clear the specificity of each tool.
PEQ: Scheduling Time-Sensitive Data-Center Flows using Weighted Flow Sizes and Deadlines

We propose a deadline-aware flow scheduling scheme, called Preemptive Efficient Queuing (PEQ), which takes both the deadlines and sizes of the flows into account for efficient flow scheduling of flows in a data-center network in this paper. PEQ benefits from evaluating the deadlines and flow sizes of flows to improve application throughput. Our results show that PEQ outperforms the state-of-the-art deadline-aware transport schemes in terms of the application throughput and average flow completion time, under traffic with different deadline distributions. Results also show that PEQ yields a near-optimal application throughput in cases where short flows have long deadlines and long flows have strict deadlines. In other words, PEQ attains higher performance than the counterpart schemes on traffic with broader distribution of deadlines and flow sizes.
Post-Quantum Multi-Party Computation in Constant Rounds.

We obtain the first constant-round post-quantum multi-party computation protocol for general classical functionalities in the plain model, with security against malicious corruptions. We assume mildly super-polynomial quantum hardness of learning with errors (LWE), and quantum polynomial hardness of an LWE-based circular security assumption. Along the way, we also construct the following protocols that may be of independent interest. (1) Constant-round zero-knowledge against parallel quantum verifiers from quantum polynomial assumptions. Here, we develop a novel parallel no-cloning non-black-box simulation technique. This uses as a starting point the recently introduced no-cloning technique of Bitansky and Shmueli (STOC 2020) and Ananth and La Placa (ePrint 2019), which in turns builds on the classical non-black-box technique of Bitansky, Khurana and Paneth (STOC 2019). Our approach relies on a new technical tool, spooky encryption for relations computable by quantum circuits, that we also construct. (2) Constant-round post-quantum non-malleable commitments from mildly super-polynomial quantum hardness of LWE. This is the first construction of post-quantum non-malleable commitments in the plain model, and is obtained by transforming the construction of Khurana and Sahai (FOCS 2017) to obtain post-quantum security. We achieve quantum security by building a new straight-line non-black-box simulator against parallel verifiers that does not clone the adversary's state. This technique may also be relevant to the classical setting.
Large divergence fiber-coupled single-mode light source for laser interferometer

In this study, a large divergence fiber-coupled single-mode light source is presented for laser interferometer. The relationship between the fiber core diameter, numerical aperture and divergence of the fiber-coupled light sources is studied theoretically and experimentally. The guiding fiber of the large divergence fiber-coupled (LDFC) single-mode light source is then produced by fusion splicing the standard single-mode fiber (Nufern 630-HP) with the germanium-doped high numerical aperture (NA) few-mode fiber. The He–Ne laser coupled into the guiding fiber is used as the light source of the laser interferometer. The LDFC single-mode light source achieves the divergence angle of 12.7°and the beam quality factor of M2∼1.2. For comparison, we take the interferograms when different guiding fibers are used for the He–Ne​ laser. The background variation of the interferogram formed by the LDFC single-mode light source is 1/7 the variation of the interferogram formed by the standard single-mode fiber-coupled (Nufern 630-HP) laser. The experimental results indicate that the background uniformity of interferogram can be improved obviously by using the LDFC single-mode light source.
Benchmarking Multidomain English-Indonesian Machine Translation

In the context of Machine Translation (MT) from-and-to English, Bahasa Indonesia has been considered a low-resource language, and therefore applying Neural Machine Translation (NMT) which typically requires large training dataset proves to be problematic. In this paper, we show otherwise by collecting large, publicly-available datasets from the Web, which we split into several domains: news, religion, general, and conversation, to train and benchmark some variants of transformer-based NMT models across the domains. We show using BLEU that our models perform well across them , outperform the baseline Statistical Machine Translation (SMT) models, and perform comparably with Google Translate. Our datasets (with the standard split for training, validation, and testing), code, and models are available on https://github.com/gunnxx/indonesian-mt-data
Multi-Layer Auto Resonance Network for Robotic Motion Control

Recent developments in machine learning and artificial intelligence have evoked interest in many research areas considered as NP Hard. Humanoid motion is one such area. Controlling robots with large number of mechanical joints poses challenges due to non-linearity of displacement, redundant configurations, dynamic user environments, etc. Analytical and approximate solutions to Inverse kinematic problems of typical industrial robots with up to 6 Degrees of Freedom (DoF) have been presented in literature. Humans use more than a hundred joints for locomotion, each with one to three degrees of freedom increasing the complexity beyond comprehension. Therefore algorithmic and even heuristic approaches have not been successful in humanoid structures. Recently, machine learning and artificial intelligence are being used for robotic applications. This paper reports a new type of Artificial Neural Network (ANN) called Auto-Resonance Network (ARN) derived from synergistic control of biological joints. The network can be tuned to any real valued input without any degradation in learning ability. Due to the approximating nature of ARN, neuronal density of the network is low and grows at a linear or low order polynomial rate with input classification. Input coverage of the neuron can be tuned dynamically to match properties of input data. ARN can be used as a part of hierarchical structures to support deep learning applications. As a case study, the network has been used to generate optimal path for locomotion avoiding obstacles. The network can optimize the length of traverse and resolution during training much like a biological system. The network is able to learn without supervision taking cues from the environment.
Multimodal Interaction Design for Public Window Displays: A Case Study

We present a case study of multimodal interaction design for public window displays. Using a classic fairytale as the theme story, a prototype system that integrates mobile, gesture, tangible, touchscreen, and puppet interfaces has been implemented. The preliminary field deployment results demonstrate that our interactive window is well-received, with a significantly extended duration of user's interaction time. We conclude with a discussion of lessons learned and potential new research problems for interactive public window design. We believe our findings are useful in future design for interactive shop windows, theater showcases, and exhibition displays.
Constructing a Bilingual Corpus of Parallel Tweets.

In a bid to reach a larger and more diverse audience, Twitter users often post parallel tweets—tweets that contain the same content but are written in different languages. Parallel tweets can be an important resource for developing machine translation (MT) systems among other natural language processing (NLP) tasks. In this paper, we introduce a generic method for collecting parallel tweets. Using this method, we collect a bilingual corpus of English-Arabic parallel tweets and a list of Twitter accounts who post English-Arabictweets regularly. Since our method is generic, it can also be used for collecting parallel tweets that cover less-resourced languages such as Serbian and Urdu. Additionally, we annotate a subset of Twitter accounts with their countries of origin and topic of interest, which provides insights about the population who post parallel tweets. This latter information can also be useful for author profiling tasks.
Oil Palm Sorting System of Fresh Fruit Bunch (FFB) Using Forward Chaining Algorithm

Abstract Oil palm is the producer of oil and fuel. To get a good quality, it is necessary to sort the fresh oil palm fruit based on the characteristics and criteria of the type of fresh fruit bunches (FFB). This paper aims to carry out the process of sorting the oil palm fruits using the Forward Chaining algorithm to obtain superior quality oil palm fruit based on the characteristics and criteria of fresh oil palm fruit bunches. The sorting phase purpose is to shorten industrial process phase and reduce cost. This sorting mechanism will be developed as a system that can educate oil palm farmers to do sorting before the oil palm goes through the industrial process. The research method uses a forward chaining algorithm by determining 5 criteria and 39 characteristics of fresh oil palm fruit bunches with percentage value determined by using the classical probability calculation. The accuracy of this research is 100%.
Weak vs strong artificial intelligence

It is proposed to distinguish between the traditional, so-called, weak, artificial intelligence (AI) and strong artificial intelligence, or artificial general intelligence (AGI). The latter is not considered as the evolutionary stage of the development of AI, but something else related to the construction of an informal and non-causal phenomenological space, as a kind of opposite to AI. AGI is determined by inverse problems solving on topologies, using analogies of quantum physics and optics, connecting aspects of relativistic theory. It is noted that the AGI continual power is tens of orders of magnitude greater than the AI power, however, for its release, special integration of the both, and direct inclusion of human in the process is required. Already, elements of AGI are helping to accelerate the collective creative process many times over.
Intelligent forecasting in multi-criteria decision-making

In this paper, the subject of the research is the intelligent forecasting in multi-criteria decision-making, in particular, methods of prediction of time series: traditional models of autoregression, smoothing techniques and machine learning methods with the use of artificial neural networks and deep learning.Time series of Amazon share prices from the official sources over the past years serve as a base for exploration.The purpose of the work is to find out the parameters that influence the efficiency and accuracy of models for analysis and forecast the share prices.Such assessment is complicated and vital because various types of criteria, in particular, sales and profitability, market indexes, exchange rates and general trends, etc., usually influence the decision-making process.The methods of research include the consideration and analysis of prediction methods using specific metrics.The relevance of the topic assumes that the accurate prediction at the financial market may contribute to the financial benefits for companies, government and other players on stock exchanges.A comparative analysis of the considered forecasting methods was conducted.It allows choosing the most appropriate intellectual method for increasing the efficiency of a specific share price prediction.The further development of the subject of research includes ensemble-learning methods for neural networks, feature engineering, collecting a more extensive data set for forecasting.