Reviewerly/exHarmony huggingface dataset:
Identification and Separation of Distinct Subpopulations of Hematopoietic Stem Cells and Multipotent Progenitors By Deep Learning

Hematopoietic stem cells (HSCs) and multipotent progenitors (MPPs) are crucial for maintaining lifelong hematopoiesis. Several decades of successful clinical HSC transplantation have demonstrated the therapeutic importance of HSCs and MPPs. For basic research, HSCs and MPPs are routinely analyzed and separated by fluorescence-activated cell sorting (FACS) based on cell surface marker staining, a procedure that requires incubation with various antibodies and expensive laser-equipped flow cytometers. Both antibody staining and laser impair cell viability and stem cell activity. To develop a new staining-free, laser-free method for predicting and separating subpopulations of HSCs and MPPs, we used a deep learning approach that can extract minutiae from large-scale datasets of long-term HSCs (LT-HSCs), short-term HSCs (ST-HSCs), and multipotent progenitors (MPPs) to distinguish subpopulations of hematopoietic precursor solely based on their morphology. Remarkably, our deep learning model can achieve predictable identification of subsets of HSCs with at least 85% accuracy; this demonstrates for the first time that HSC subpopulations can be differentiated solely based on their morphology exhibited in light microscopic images through deep learning. We anticipate that our robust and accurate deep-learning-based platform for hematopoietic precursors will provide a basis for the future development of a next-generation cell sorting system.
Three-Dimensional Transparent Soil Imaging and Processing

Non-intrusive observation of the spatial deformation field of the soil has been a difficult problem for laboratory model test testing. Based on the advantage that the visualization test method of transparent soil can observe the two-dimensional (2D) deformation inside the soil, this chapter described a new automatic tomographic scanning measuring device to observe the three-dimensional (3D) spatial deformation of the soil inside the transparent soil. A series of 2D laser speckle images of different vertical cross sections before and after deformation were obtained, and an improved 3D reconstruction algorithm was used to reconstruct the 3D displacement field of soil after deformation. Its applications in pile penetration are reported.
Relative Telomeric Length of Bone Marrow Cells at Diagnosis and Its Association with Minimal Residual Disease in Juvenile Acute Lymphoblastic Leukemia

Introduction: Identification of prognostic factors at diagnosis, risk stratification and the analysis of minimal residual disease (MRD) may predict treatment outcomes for children diagnosed with Acute Lymphoblastic Leukemia (ALL). A possible prognostic indicator is bone marrow mononuclear cells telomeric length at diagnosis. Currently, the best predictor of treatment response and disease-free survival is the analysis of MRD. Objectives: To investigate the association between bone marrow mononuclear cells telomeric length in children and adolescents with ALL at the time of diagnosis and the assessment of MRD on days 15, 33 and 78 after initiation of treatment. Methods: This study included patients aged 1 to 18 years, diagnosed with ALL and treated at Hospital da Criança de Brasília José Alencar. The telomeric length was determined by real-time polymerase chain reaction (qPCR) from a sample of bone marrow aspirate collected at the diagnosis. MRD was performed by flow cytometry immunophenotyping on days 15, 33, and 78 of chemotherapy. Risk stratification was determined according to specifications from BFM 2009 protocol. Results: 79 subjects were included in the study. Patients with negative MRD on D33 had significantly longer telomeric length (p = 0.02) when compared with those with positive MRD on D33. Patients classified as high risk had a higher frequency of positive MRD on D33 (p<0.01). Only positive DRM on D33 was a predictor of shorter telomeric length at diagnosis. Telomeric length at diagnosis was not related to D15 or D78 MRD results. Conclusion: This study suggests that longer telomeric length may be associated with a better prognosis, since these patients have negative MRD on D33. This result demonstrates that the assessment of the telomeric length of bone marrow cells from pediatric patients with ALL at the time of diagnosis can be a potential prognostic factor.
Professional Practices of Methodical Work in Higher Education

Introduction. In the article the peculiarities of scientific and communication practices of the methodologists in interaction with the university teachers are considered on the material of the analysis of the ITMO.Expert methodological community activity. Methodology and sources. The theoretical framework of the research is institutional and structural-functional approaches to the study of university activities. The method used for collecting empirical data presented in the article is a content analysis of the co-messages sent by the participants of the online intensive “Personalized Learning Technologies” on the Zoom-conference, which was held on August 23–27, 2021. The research hypotheses are the following: discussions of new pedagogical practices and new design formats initiated by university methodologists provoke teachers' skepticism, distrust, and resistance. Also teachers may demonstrate vulnerability in discussing new approaches in education. Results and discussion. Testing the hypothesis that discussing new pedagogical practices and approaches will cause skeptical attitudes and distrust on the part of teachers was not confirmed. The patterns of distrust accounted for less than 5 % of the total array of messages. The hypothesis of teachers (especially beginners) being vulnerable when discussing new approaches, a manifestation of which was their refusal to discuss their own pedagogical experience and the problems encountered, was partially confirmed. At the same time due to the organizational and communicative actions of the methodologistsorganizers of the intensive there was formed an atmosphere of trust and safety, so the participants had an opportunity to share their experience or ask questions without being judged by their colleagues. Conclusion. The activity of the pedagogical designer is both an educational activity, aimed at teaching and promoting new educational technologies in teaching, and an organizational and managerial activity, aimed at stimulating the activity of teachers in developing and modeling a variety of knowledge formats. Many researchers suggest characterizing pedagogical design as a sociocultural activity. When designing educational experience and new educational products, methodologists help develop pedagogical traditions and reconsider norms.
Introduction to the Special Issue on Pattern Recognition-driven User Experiences (PRUE)

Neural Style Transfer (NST) comprises a class of computer vision methods that manipulate digital images to reformulate the visual content of one input image adopting visual features of an another image set. Artistic NST is the particular case of NST where the visual features are extracted from painting images. The combination of Cycle-Consistent Adversarial Networks (CycleGANs) with Fast and Adaptive Bidimensional Empirical Mode Decomposition (FABEMD) is proposed to adopt the specific artist’s style on images effectively, where the cycle-consistency loss is modified to incorporate texture information by estimating the corresponding Bidimensional Intrinsic Mode Functions (BIMFs). An adaptive approach for identifying the optimal BIMF number that must be considered in order to manipulate the required amount of the involved texture, is proposed. For this purpose, the computation of a metric is considered for each BIMF to characterise the texture of each image or major intensity alterations at local scale. Experimental results reveal that adaptive comixture of texture features comprises an efficient approach in such artistic applications. Qualitative and quantitative results demonstrate that the proposed framework outperforms state-of-the-art (SoA) methods.
Comparing autoencoder-based approaches for anomaly detection in highway driving scenario images

Abstract Autoencoder-based anomaly detection approaches can be used for precluding scope compliance failures of the automotive perception. However, the applicability of these approaches for the automotive domain should be thoroughly investigated. We study the capability of two autoencoder-based approaches using reconstruction errors and bottleneck-values for detecting semantic anomalies in automotive images. As a use-case, we consider a specific highway driving scenario identifying if there are any vehicles in the field of view of a front-looking camera. We conduct a series of experiments with two simulated driving scenario datasets and measure anomaly detection performance for different cases. We systematically test different autoencoders and training parameters, as well as the influence of image colors. We show that the autoencoder-based approaches demonstrate promising results for detecting semantic anomalies in highway driving scenario images in some cases. However, we also observe the variability of anomaly detection performance between different experiments. The autoencoder-based approaches are capable of detecting semantic anomalies in highway driving scenario images to some extent. However, further research with other use-cases and real datasets is needed before they can be safely applied in the automotive domain.
The Role of Database Applications of Digital Archive Management in The Era of Society 5.0

We will encounter many technologies in the 4.0 revolution era, such as cloud computing, artificial intelligence, the internet, 5G, digitalization, and big data. All of these technologies have potential in the world of education. Based on information about some of the problems faced by teachers at ASPAPI Member Vocational Schools in the DKI Jakarta area, they must be addressed as soon as possible as a solution to increasing knowledge, abilities, and skills in running database applications on digital archives as one of the development and utilization of information and communication technology in the Industrial Revolution 4.0 era and the 5.0 social era. Therefore, the community service team tries to offer solutions to these problems through the following main activities: 1) increasing the understanding and skills of ASPAPI Member Vocational High School teachers in the DKI Jakarta area, 2) increasing abilities in teaching and learning activities for digital archives, especially the use of database applications. The method used for providing training consists of planning, action execution, observation, and reflection. We generate an application called Dive Apps, where these apps can manage archives with a digitalization system and can also be used by teachers in archival learning.
Predicting Cancer Survival Using Multilayer Perceptron and High-Dimensional SVM Kernel Space

Predicting disease prediction and prognosis have become very easy task with Machine learning models for an inextricable aspect of cancer research aiming at enhancing patient therapy and management. The primary goal of proposed research work is to use Support vector machine, machine learning models and for dealing very accurately for predicting survival time for breast cancer based on clinical data. The study has proposed a solution to the problem in respect to various tumor related characteristic by integrating from dataset about tumour stage, size of tumor, and age at which the diagnosis start is an important major component for utilising for predicting survival time. Haberman's Survival Data Set is dataset describing those subjects who had been provided treatment for breast cancer. The sample taken for research are taken from study which was conducted at University of Chicago's Billings Hospital taking case who were survived after surgery for breast cancer. SVM applied on data set by different options of kernel RBF and linear as well as soft computing techniques are applied to predict the survival rate of patient from dataset. Apart from data standardisation and categorization, the machine learning approaches used in this research work to demonstrate features in terms of predicting how long they survived. Model performance is analysed on breast cancer data is justified by accuracy, support and f1 score. A workflow based on Python-platform has been utilised to support the suggested technique.
Multimedia Streaming with MPEG-DASH

This chapter focuses on advanced video streaming techniques and details on MPEG-DASH, which stands for “Moving Picture Experts Group – Dynamic Adaptive Streaming over HTTP.” First, the differences of push- vs. pull-based media streaming are described, in which the advantages of pull-based media streaming become evident. Second, the most popular pull-based media streaming protocol Hypertext Transfer Protocol (HTTP) is introduced, and the various types of streaming are described, which include regular HTTP streaming, progressive download, and adaptive streaming over HTTP. Third, details of MPEG-DASH are introduced. In addition, details on the MPEG-DASH Multimedia Presentation Description (MPD) methods are introduced. Next, the video frames (I, P, B frames) and Group of Pictures (GOP) descriptions are provided followed by a YouTube MPEG-DASH operation example and a list of MPEG-DASH features. The major sections of this chapter are listed in the following:
CHALLENGES OF FINANCIAL AND ACCOUNTING TREATMENT OF CRYPTO TOKENS

Initial coin offering (ICO) is an entirely new method of raising funds based on blockchain technology. ICO implies the company's decision to issue cryptographic tokens in exchange for fiat currency (legal means of payment) or cryptocurrencies to a network of early adopters or development team members. The leading idea of this concept is to give ICO startups access to the financial resources necessary for funding their projects. What an initial public offering (IPO) is in traditional finance, ICO is in the decentralized finance world. Yet, unlike actions, issued tokens can have multiple uses and features and perform various functions. The situation is further complicated by the lack of a unique globally accepted classification of crypto tokens. However, in practice, they are most often classified according to their fundamental economic functions. According to this categorization, the following basic types of tokens are distinguished: utility, payment (or currency) and security (or asset) tokens. In addition to the three types listed, some tokens, known as hybrid ones, combine several functions of these three basic categories. It is essential for the company of the issuer of the crypto token to carefully compile the White Paper, synonymous with the prospectus for the initial public offering, and to precisely understand what it offers to investors. The financial and accounting treatment of crypto tokens also depends on the rights and obligations arising from the White Paper, i.e. the issued token type. There are certain conditions under which tokens issued by regulators could be deemed securities, with the necessary reminder that there is no single global view on this significant issue. The paper primarily aims to indicate when and under what conditions crypto tokens can be considered securities, i.e. what are the critical challenges of financial and accounting treatment of crypto tokens.
Pemetaan Penelitian Terkait Keamanan Data di Indonesia

This research aims to be able to find out the tendency to use keywords in research related to data security in Indonesia. This study uses the method of bibliometric studies. The data was taken from a database published by Scopus in 2019 to 2021. Bibliometric data analyzed and visualized using VosViewer. Total research after the limit was generated 2,497 data globally and research related to security data in Indonesia found 31 studies in the form of journal articles. In Indonesia found 13 main keywords used in the study, namely Data Hiding, Data Protection, Data Security, Information Security, Reversible Data Hiding, Steganography, Cloud Computing, Cryptography, Network Security, Pixels, Security, and Security Of Data. Then after analysis was concluded that keyword Cryptography and Network Security are most often used in research related to data security globally and in Indonesia.&#x0D; &#x0D; ABSTRAK &#x0D; Penelitian ini bertujuan agar dapat mengetahui penelitian terkait Keamanan data di Indonesia. Penelitian ini menggunakan metode Studi bibliometrik. Data diambil dari database yang diterbitkan oleh Scopus dalam tahun 2019 hingga 2021. Data bibliometrik yang dianalisis dan divisualisasi menggunakan VosViewer. Hasilnya ditemukan Total penelitian setelah dilakukan limit dihasilkan 2.497 data secara global dan penelitian terkait data security di Indonesia ditemukan 31 penelitian berbentuk artikel jurnal. Di Indonesia ditemukan 13 keyword utama yang digunakan dalam penelitian yaitu Data Hiding, Data Protection, Data Security, Information Security, Reversible Data Hiding, Steganography, Cloud Computing, Cryptography, Network Security, Pixels, Security, Dan Security Of Data. Kemudian setelah dianalisis didapat kesimpulan bahwa keyword Cryptography dan Network Security paling sering digunakan dalam penelitian terkait keamanan data secara global dan di Indonesia.
Energy Aware Job Scheduling and Simulation in a Cloud Datacenter

Virtualization technology is used by cloud systems for the users to utilize cloud resources through Virtual Machines.These VM’s process the task requests made by users. Ever since inefficient hardware utilization is the concernfor the future and the environment, efficient work load balancing and allocation of VMs helps to bring down thehardware usage and results to efficient working. That being said, this paper proposes task scheduling frameworkwhere the task will be assigned to a VMs running on the active hosts(servers) through preemption as required andclassification of the cloudlets. The algorithm that we have taken into consideration will categorize the cloudletsinto three distinct types and allocate them a VM based on first come, first served resource time in regards to thatparticular host. This in turn will reduce the energy consumption by having lesser machines running in the activestate meanwhile preserving efficient utilization of the active servers. Such kind of simulations are fairly achievedusing the CloudSim framework
Multiturn dialogue generation by modeling sentence-level and discourse-level contexts

Currently, multiturn dialogue models generate human-like responses based on pretrained language models given a dialogue history. However, most existing models simply concatenate dialogue histories, which makes it difficult to maintain a high degree of consistency throughout the generated text. We speculate that this is because the encoder ignores information about the hierarchical structure between sentences. In this paper, we propose a novel multiturn dialogue generation model that captures contextual information at the sentence level and at the discourse level during the encoding process. The context semantic information is dynamically modeled through a difference-aware module. A sentence order prediction training task is also designed to learn representation by reconstructing the order of disrupted sentences with a learning-to-rank algorithm. Experiments on the multiturn dialogue dataset, DailyDialog, demonstrate that our model substantially outperforms the baseline model in terms of both automatic and human evaluation metrics, generating more fluent and informative responses than the baseline model.
Recovery of historical seismic data from U.S. underground nuclear tests ? Leo Brady Seismic Network.

of regional seismic data of UGTs. Approximately 75% of all UGTs performed by the United States occurred in the predigital era. At that time, LBSN data were transmitted as frequency-modulated (FM) audio over telephone lines to a central location and recorded as analog waveforms on high-fidelity magnetic audio tapes. These tapes have been in dry temperature-stable storage for decades and contain the sole record of this irreplaceable data; full waveforms of LBSN-recorded UGTs from this era were not routinely digitized or otherwise published. We have developed a process to recover and calibrate data from these tapes. First, we play back and digitize the tapes as audio. Next, we demodulate the FM “audio” into individual waveforms. We then estimate the various instrument constants through careful measurement of “weight-lift” tests performed prior to each UGT on each instrument. Finally, these coefficients allow us to scale and shape the derived instrument response of the seismographs and compute poles and zeros. Finally, the result of this process is a digital record of the recorded seismic ground motion in a modern data format, stored in a searchable database. To date, we have digitized tapes from 592 UGTs.
Ratios for Evaluating Full-Text Journal Article Access: A Quantitative Study

This article proposes a methodology for systematically assessing the cost of journal subscriptions. The authors of the paper (hereafter “the researchers”) established ratios comparing the list costs of journal articles as advertised by publishers against the cost per article of journal articles available in aggregated collections in library databases (hereafter “aggregating databases”). The researchers propose that the ratios can be used by libraries wishing to apply a standard methodology for assessing journal packages containing full-text articles. The study may be helpful for those librarians who seek to supplement qualitative information and other quantitative data, such as usage statistics, in order to demonstrate the library's rationale for providing journal access either by purchasing articles directly from a publisher as needed or by continuing to subscribe to an aggregating database. The aggregating databases reviewed in this study included representative aggregating databases commonly associated with the following fields of study: Social Sciences, Humanities, and Health Sciences; however, the methodology described in this article could be applied to other disciplines as well. The results of the study indicated that the ratios for Mean Cost/Package Subscription Price (MC/PSP), Median Cost/Package Subscription Price (MED/PSP) could be used in evaluating journal collections. The researchers suggest that future studies should be conducted to assess resource sharing and the availability of open access resource versions of articles as possible contributing factors to the purchase decisions associated with scholarly journal articles.
Optimizing Machine Learning Decisions with Prediction Uncertainty.

these classifiers can be uncertain, rendering downstream decisions difficult. Herein, we provide a framework for: (1) quantifying and propagating uncertainty in ML classifiers; (2) formally linking ML outputs with the decision-making process; and (3) making optimal decisions for classification under uncertainty with single or multiple objectives.
Deep Generative Models and Inverse Problems

Deep generative models have been recently proposed as modular datadriven priors to solve inverse problems. Linear inverse problems involve the reconstruction of an unknown signal (e.g. a tomographic image) from an underdetermined system of noisy linear measurements. Most results in the literature require that the reconstructed signal has some known structure, e.g. it is sparse in some known basis (usually Fourier or wavelet). Such prior assumptions can be replaced with pre-trained deep generative models (e.g. generative adversarial getworks (GANs) and variational autoencoders (VAEs)) with significant performance gains. This chapter surveys this rapidly evolving research area and includes empirical and theoretical results in compressed sensing for deep generative models.