Reviewerly/exHarmony huggingface dataset:
Blockchain-Based Medical Record Management with Biofeedback Information

Blockchain is a new emerging technology of distributed databases, which guarantees the integrity, security and incorruptibility of data by means of the cryptography. Such features are suitable for secure and reliable data storage. This chapter investigates the blockchain-based architecture with applications to medical health record or biofeedback information management. This framework employs the smart contract to establish a medical record management system to ensure the privacy of patients. Moreover, the blockchain technique accelerates the medical record or information exchange such that the cost of human resource is significant reduced. All patients can manage their individual medical records and information easily in the different hospitals and clinics. They also have the privilege to deal with and authorize personal medical records in the proposed management framework.
Self-Supervised Video Representation Using Pretext-Contrastive Learning.

Pretext tasks and contrastive learning have been successful in self-supervised learning for video retrieval and recognition. In this study, we analyze their optimization targets and utilize the hyper-sphere feature space to explore the connections between them, indicating the compatibility and consistency of these two different learning methods. Based on the analysis, we propose a self-supervised training method, referred as Pretext-Contrastive Learning (PCL), to learn video representations. Extensive experiments based on different combinations of pretext task baselines and contrastive losses confirm the strong agreement with their self-supervised learning targets, demonstrating the effectiveness and the generality of PCL. The combination of pretext tasks and contrastive losses showed significant improvements in both video retrieval and recognition over the corresponding baselines. And we can also outperform current state-of-the-art methods in the same manner. Further, our PCL is flexible and can be applied to almost all existing pretext task methods.
Musical Brush: Exploring Creativity Through an AR-Based Tool for Sketching Music and Drawings

The economic growth and social transformation in the 21st century are hardly based on creativity. To help with the development of this skill, the concept of Creativity Support Tools (CST) was proposed. In this paper, we introduce Musical Brush (MB), an artistic mobile application whose main focus is to allow novices to improvise music while creating drawings. We investigated different types of interactions and audiovisual feedbacks in the context of a mobile application that combines music with drawings in a natural way, measuring their impact on creativity support. In this study, we tested different user interactions with real-time sound generation, including 2D drawings, three-dimensional device movements, and visual representations on Augmented Reality (AR). A user study was conducted to explore the support for creativity of each setup. Results showed the suitability of the association of Musical Brush with augmented reality for creating sounds and drawings as a tool that supports the exploration and expressiveness.
Conducting High Impact Research With Limited Financial Resources (While Working from Home)

The Covid-19 pandemic has far-reaching implications for researchers. For example, many researchers cannot access their labs anymore and are hit by budget-cuts from their institutions. Luckily, there are a range of ways how high-quality research can be conducted without funding and face-to-face interactions. In the present paper, I discuss nine such possibilities, including meta-analyses, secondary data analyses, web-scraping, scientometrics, or sharing one’s expert knowledge (e.g., writing tutorials). Most of these possibilities can be done from home, as they require only access to a computer, the internet, and time; but no state-of-the art equipment or funding to pay for participants. Thus, they are particularly relevant for researchers with limited financial resources beyond pandemics and quarantines.
Two-phase relative permeability prediction from capillary pressure data for one Iraqi oil field: A comparative study

In almost all measurements of the fluid flowing in pores media, relative permeability data is essential. Relative permeability curves have an important role in describing two-phase flow in porous media and estimating of immiscible displacement processes efficiency. Calculations of relative permeability are usually derived from laboratory tests utilizing core samples, or from capillary pressure test data. There are several approaches utilized for estimating the relative permeability utilizing the drainage or imbibition capillary pressure curve. In this study, different approaches utilized to measure the oil-water relative permeability from experimental capillary pressure data. The Corey relative permeability model generally accepted as a method for measuring relative permeability curves utilizing capillary pressure data. In this study, the analytical solution relative permeability model was found to be best appropriate model to the experimental wetting and non-wetting-phase relative permeability data. The variations between experimental relative permeability and analytical solution model were almost insignificant in both wetting and non-wetting phases. Measurement of relative permeabilities utilizing the analytical solution model and Corey model for the nonwetting-phase were very similar to the experimental measurements other than the Rose and Bruce model. This study showed that the relative permeability acceptably determined by selecting a suitable model utilizing capillary pressure. Capillary pressure may also be measured in the reverse procedure once the relative permeability data were accessible.
LANGUAGE BARRIER IS THE CAUSE OF STRESS AMONG INTERNATIONAL STUDENTS OF UNIVERSITAS AIRLANGGA

Advanced education system and globalization are attractive towards the students to move a foreign country for achieving higher education. Indonesia is a multicultural and multi-linguistic country where every year hundreds of students coming to get higher education in Masters and PhD in various disciplines of education which are taught in Indonesian language. There are number of students joining Universitas Airlangga for the purpose of higher education on different programs. The current investigation defines the problem of language barrier is causing of stress in regard of academics among international students. There were 70 international students contributed in current study. The study finds 68% of participants suffering with the stress of language barrier in academics for international students. The findings of current study indicated the mainstream of international students experiencing in adopting the language barrier which is becoming the cause of stress for them. This study also suggested to the administrative stack-holders of Universitas Airlangga to pay attention this issue and need to overcome this major problem by organizing some events within the campus with local students. Such recommendations will help to cope this issue among international students by learning in a lenient and practical way.
THE IMPACT CORPORATE GOVERNANCE ON INTERNET FINANCIAL REPORTING: EMPIRICAL EVIDENCE FROM PALESTINE

The current research investigates whether the difference in the Internet Financial Reporting standard is clarified by corporate governance. A study was carried out on a selection of 48 companies listed on the 2019 Palestine Stock Exchange. An index was also selected from several previous studies to assess the standard of Internet financial reporting. One of the first analytical researches to investigate the relationship between corporate governance and Internet Financial Reporting in Palestine is the latest analysis. Firstly, the scope of disclosure of Internet Financial Reporting in Palestinian businesses appears to be limited. Second, the educational history of boards is greatly related to Internet Financial Reporting. Nevertheless, the board independence coefficient and board audit committee are negligible. Thirdly, an important element in strengthening internet financial reporting standards is a broad audit company. Fourthly, there is a strong positive correlation between the concentration of ownership and financial reporting on the Internet. Companies mainly held by stakeholders are more likely to reveal internet data and to strengthen the reports released. Finally, profitability and market capitalization have a direct connection with Internet Financial Reporting, and Internet Financial Reporting does not justify the composition of the board, board meetings, international investors, and business size.
Mobile Apps and Memes in the Era of COVID-19 Pandemic; Could this Make Quarantine More Acceptable?

Coronavirus disease 2019 (COVID-19) pandemic forces the entire world countries to impose a curfew and closes most of the social organizations; that creates a lot of leisure time and the increasing need to fill this space inside the home. Additionally, it increases the need for achieving daily home tasks. Therefore, mobile apps find their way to control most of the people's life from food delivery up to educational lectures and business meetings. That phenomenon provides a lot of benefits; It not only encourages fighting infection but also helps to reduce economic affection from curfew and continuation of working flow.
Personal Privacy Protection in Big Data Environment Under the New Coronavirus Situation

In early 2020, a massive outbreak of a new coronavirus pneumonia swept across the globe.Governments quickly moved to detect and track and isolate patients and doctors and nurses to effectively prevent the spread of virus and protect the safety of healthy people.Tracking personal trajectories, contact surfaces and other information by means of technology can help us quickly detect the close contacts of the virus.But these scattered data are aggregating together, which is the data of citizens' personal privacy.Once the data are used by malicious elements, it will cause incalculable economic losses and even public crises.Therefore, we must pay close attention to the protection of personal privacy.This paper first introduces the measures adopted by various countries in the world to fight against the epidemic situation, and discusses the problems and risks faced by them.Then, the mature privacy protection technology is analyzed.Finally, the research direction of the privacy protection stage during the epidemic period is proposed.
System design object-based multi-agent network storage (OBMA-MNSS)

Abstract Information superiority is the key to ensure the success of information construction, and information storage system is the basis to obtain information superiority. Based on the analysis of the statistical methods of network value evaluation and the influence of storage system networking on information construction, according to the demand of information construction for data storage application, combined with the latest development of storage technology, an object-based multi-agent military network storage system is proposed. The design goal, design idea and prototype design of the system are given.
Effect of Computer-aided Detection System Use on the Duration of MRI-guided Biopsy of the Breast

Magnetic resonance imaging (MRI)-guided breast biopsy is a complex and time-consuming procedure. This study aimed to clarify the factors that affect the duration of the procedure.Twenty-eight examinations performed at our institute for 27 lesions detected solely on MRI were analyzed. The correlations between the clinicopathological factors and duration of the procedure were estimated.The needle guidance method was the only factor that significantly affected the duration of the MRI-guided vacuum-assisted breast biopsy (VAB) (p=0.012). The use of a computer-aided detection (CAD) system with grid breast compression plates had significantly shorter durations (62±12 min) than the manual calculation of coordinates with pillar-type compression plates (76±13 min).This preliminary study showed that the use of a CAD system might shorten the duration of MRI-guided VAB.
Stereo Matching Method Based on Combination Characteristic Cost Computing and Unstable Tree Reconstruction Optimization and Its Application in Medical Images

This study aimed to propose a stereo matching method based on the cost calculation of combination feature and reconstruction optimization of an unstable tree. For cost calculation, the improved Census transform was used to calculate the illumination characteristics, the color and gradient operators were used to calculate the color features, and the LBP (Local Binary Pattern) operator was used to calculate the texture features. Then, the initial matching cost was calculated by combining all three features. For cost aggregation, the minimum spanning tree algorithm was improved and the tree aggregation window was reset. For disparity optimization, the stability parameters were constructed to judge the stability of the tree aggregation window, and unstable trees were disassembled, reconstructed, incorporated into the surrounding stable trees. In the reconstruction process, the effects of error disparity ratio difference, brightness difference, and disparity difference were considered comprehensively. The experimental results showed that the performance of the proposed method was obviously better than that of the stereo matching method based on the minimum spanning tree, and was close to that of the two globally optimized stereo matching methods. Further, the method was applied to stereo matching of uterine images, and the depth information was rich in the disparity images, showing that this method could provide a basis for medical diagnosis.
Particularity Beyond Commonality: Unpaired Identity Transfer with Multiple References

Unpaired image-to-image translation aims to translate images from the source class to target one by providing sufficient data for these classes. Current few-shot translation methods use multiple reference images to describe the target domain through extracting common features. In this paper, we focus on a more specific identity transfer problem and advocate that particular property in each individual image can also benefit generation. We accordingly propose a new multi-reference identity transfer framework by simultaneously making use of particularity and commonality of reference. It is achieved via a semantic pyramid alignment module to make proper use of geometric information for individual images, as well as an attention module to aggregate for the final transformation. Extensive experiments demonstrate the effectiveness of our framework given the promising results in a number of identity transfer applications.
Consistency and Comparison of Medical Image Registration-Segmentation and Mathematical Model for Glioblastoma Volume Progression

Tumor volume progression and calculation is a very common task in cancer research and image processing. Tumor volume analysis can be carried out in two ways. The first way is using different mathematical formulas and the second way is using image registration-segmentation method. In this paper an objective application of registration of multiple brain imaging scans with segmentation is used to investigate brain tumor growth in a 3 dimensional (3D) manner. Using 3D medical image registration-segmentation algorithm, multiple scans of MR images of a patient who has brain tumor are registered with different MR images of the same patient acquired at a different time so that growth of the tumor inside the patient's brain can be investigated. Brain tumor volume measurement is also achieved using mathematical model based formulas in this paper. Medical image registration-segmentation and mathematical based method are implemented to 19 patients and satisfactory results are obtained. An advantageous point of medical image registration-segmentation method for brain tumor investigation is that grown, diminished, and unchanged brain tumor parts of the patients are investigated and computed on an individual basis in a three-dimensional (3D) manner within the time. This paper is intended to provide a comprehensive reference source for researchers involved in medical image registration, segmentation and tumor growth investigation.
Yapay Zeka Ağları Kullanılarak Güneş Radyasyonu Değerinin Tahmini

Energy need in our country and in the world is increasing day by day.Due to the limited use of fossil fuels to meet this need, the trend towards renewable energy sources has gradually increased.Solar energy, which is considered as an inexhaustible energy source among renewable energy sources, is the most widely used and studied energy source.In this study, solar radiation value has been modeled by using the data obtained from 10 kW small scale solar power plant established in Selçuklu Region of Konya province.While performing this model, the data recorded over a year from the solar power plant and the data obtained from the general directorate of meteorology were used.In this model, the solar radiation value is estimated by using the temperature value in a small scale solar power plant, the voltage and power values obtained from the PV panel.As a result of the modeling, it was seen that the modeling was performed with 86% accuracy.
Re-performing Design

Graphic design, as a specific research discipline, has been largely underrepresented in academia, with the literature suggesting this is partially due to difficulties in researching its professional practitioners. Acknowledging such hurdles, this article discusses an experimental study that used dramaturgy as a defamiliarising method for uncovering professional graphic designers’ perceptions of stakeholders. The study collected graphic designer narratives from online forums as well as dramaturgically informed interviews with professional practitioners. The graphic designers’ narratives were converted into a script and used to motivate a troupe of trained actors, who re-performed the narratives during a series of performance workshops. The article argues that this use of trained actors as ‘proxy designers’ created a refractive form of defamiliarisation, allowing previously obfuscated narratives about graphic designers’ perceptions of stakeholders to emerge. Presenting the study as a prototype to inform future research into graphic design and other elusive creative practices, the article also cautions that the amount of defamiliarisation used must be evaluated against the desired outcomes.
Neural representation of the visual environment along the continuum from objects to scenes

The visual system has distinctive networks supporting scene processing and object processing. However, in typical experience we perceive a continuum of views between these extremes. How do visual brain responses change as a function of the depicted scale of the space in view? To investigate this question, we created 20 indoor environments (e.g. kitchen, library) using virtual-reality software. All environments had the same physical dimensions, with an extended surface along the back wall (e.g. countertop, desk) containing both central and surrounding objects. For each environment, a series of 15 snapshots were rendered to smoothly sample between a close-up view of the central object on this surface, and far-scale view of the full environment, using logarithmically spaced steps. Brain responses were measured to each position along the continuum using functional magnetic resonance imaging in 12 participants. Within independently-localized scene and object regions, we found evidence for parametric responses: activation smoothly changed as a function of the spatial scale of the depicted view. In a whole-brain analysis, we found mixed evidence for a smoothly mapped continuum across the cortex—some participants showed intermediate regions with preferences to intermediate spatial scales, while others did not. Interestingly, this was in contrast to our localizer runs, where photographs of intermediate-scale spaces consistently activated some regions more than both object and full-scale scene views; however these regions were equally activated by all locations along the object-scene continuum in rendered images. The most consistent finding is that all participants showed extensive cortical territory along the ventral visual stream with either a monotonic increase or decrease in activation when the depicted scale of space is parametrically varied from objects to scenes. More work is needed to understand the individual differences in the mapping of this continuum onto the cortex.
Hyperparameter optimization with REINFORCE and Transformers

Reinforcement Learning has yielded promising results for Neural Architecture Search (NAS). In this paper, we demonstrate how its performance can be improved by using a simplified Transformer block to model the policy network. The simplified Transformer uses a 2-stream attention-based mechanism to model hyper-parameter dependencies while avoiding layer normalization and position encoding. We posit that this parsimonious design balances model complexity against expressiveness, making it suitable for discovering optimal architectures in high-dimensional search spaces with limited exploration budgets. We demonstrate how the algorithm's performance can be further improved by a) using an actor-critic style algorithm instead of plain vanilla policy gradient and b) ensembling Transformer blocks with shared parameters, each block conditioned on a different auto-regressive factorization order. Our algorithm works well as both a NAS and generic hyper-parameter optimization (HPO) algorithm: it outperformed most algorithms on NAS-Bench-101, a public data-set for benchmarking NAS algorithms. In particular, it outperformed RL based methods that use alternate architectures to model the policy network, underlining the value of using attention-based networks in this setting. As a generic HPO algorithm, it outperformed Random Search in discovering more accurate multi-layer perceptron model architectures across 2 regression tasks. We have adhered to guidelines listed in Lindauer and Hutter while designing experiments and reporting results.
Transcendental Syntax iv: Logic Without Systems

A derealistic, system-free approach, with an example: arithmetic.
Deep Metric Learning with Triplet-Margin-Center Loss for Sketch Face Recognition

Sketch face recognition is to match sketch face images to photo face images. The main challenge of sketch face recognition is learning discriminative feature representations to ensure intra-class compactness and inter-class separability. However, traditional sketch face recognition methods encouraged samples with the same identity to get closer, and samples with different identities to be further, and these methods did not consider the intra-class compactness of samples. In this paper, we propose triplet-margin-center loss to cope with the above problem by combining the triplet loss and center loss. The triplet-margin-center loss can enlarge the distance of inter-class samples and reduce intra-class sample variations simultaneously, and improve intra-class compactness. Moreover, the triplet-margin-center loss applies a hard triplet sample selection strategy. It aims to effectively select hard samples to avoid unstable training phase and slow converges. With our approach, the samples from photos and from sketches taken from the same identity are closer, and samples from photos and sketches come from different identities are further in the projected space. In extensive experiments and comparisons with the state-of-the-art methods, our approach achieves marked improvements in most cases.