Reviewerly/exHarmony huggingface dataset:
Blood Bank Monitoring and Blood Identification System Using Iot Device

To guarantee blood is ok for patient use, it is important to continually keep up ideal temperature. Blood donation center checking decreases squander as well as offers thorough help of stock levels to guarantee patients get blood when they need it. RFID improvement energize modified far off ID using electronic uninvolved and dynamic names with suitable per clients. The principal portion comprise Temperature and moistness sensor, IR sensor hubs which is introduced in rack of blood donation center, and the IoT Module and furthermore interfaced with PIC microcontroller. Second portion comprises of Wi-Fi module for information move to the worker and third section is showing the situation with accessible blood stock utilizing RFID tag. All the constant status identifies with the accessible blood supply of the blood donation center is shown on website page, so the blood searcher can get the blood from their closest blood donation center. Adaptable and with low expectations to absorb information, a robotized natural observing framework archives temperature as well as stickiness to guarantee the security of blood and blood accessibility can be distinguished by utilizing RFID tag through cloud with assistance of IoT module. The persistent checking and programmed update of temperature, mugginess and blood pack in IoT. The IR sensors are utilized to include the/OUT individual entering inside the blood donation center and furthermore refreshed to the cloud utilizing IoT module.
On the properties of the classifier in case of normal blood parameters

A mathematical model is described and implemented, intended for the numerical study of the ability of the statistical classification method to interpolate and extrapolate. The classifier developed by the authors is based on the polynomial-regression approach and has probabilistic estimates. It is used to assess the state of human health based on the parameters of laboratory analysis of peripheral blood. The blood base is considered with normal parameters.
Social Networks as a Means of Monitoring Students’ Progress

In the era of the digital educational environment, where each participant of the educational process is actively involved in its development, the Internet and its services have become a popular tool. Open education network tools are defined as ICT tools that ensure the formation and maintenance of network electronic information resources of an open education environment, the implementation of design technologies, and the use of open pedagogical systems. The article aims to reveal methods of monitoring and analyzing students’ learning achievements in higher education institutions using social networks. The main research method was a survey. The article justifies the purposefulness and efficiency of the use of social networks in the process of monitoring and analyzing students’ progress for new learning achievements. The study concludes that the use of social networks for monitoring and analyzing students’ progress ensures the individualization of the learning process and increases the effectiveness of monitoring learning achievements.
Online learning issues in Russian universities

There is considerable uncertainty as to the way we lead our lives. We face global changes concerning every single aspect of our lives: economy, digital technologies, cultures, languages, and even our health and today not mentioning all the catastrophic consequences of the pandemic and economic slowdown. Institutions are responding to the current circumstances with a shift to online learning. While most universities have urgently switched to online learning and benefit from using educational and videoconferencing platforms, the issues caused by online education under these circumstances are still to be studied and carefully analysed. This article problematizes the influence of online learning and reveals a range of pitfalls arising in the process of online education as well as a detailed analysis of these issues. It was found out that the online learning process is affected by a wider range of reasons than we expected. The interrogative method was used as the main measurement tool. The evaluation studies were carried out at the university on its use of an online education system as a forced response to the current worldwide pandemic. Among revealed issues, we established the following: students’ ability to learn self-directly, assessment tools used by teachers to evaluate the knowledge, timely teacher’s feedback, teachers’ core competencies and personal qualities, technical, health issues, and some others. The global events caused a situation when universities had to reconsider the way they deliver education to people. The goal is to make it more expedient, accessible, and personalized. The information in the paper can contribute to the quality improvement of online education and learning success.
Análise de Dados em Redes Sem Fio de Grande Porte: Processamento em Fluxo em Tempo Real, Tendências e Desafios

The Book of Short Courses of the XXXVII Brazilian Symposium on Computer Networks and Distributed Systems (SBRC 2019) addresses current topics which are of interest to the community, such as blockchains, network virtualization, urban computing, packet processing, wireless networks, cryptography and data science. In the first chapter, entitled “Segurança na Internet do Futuro: Provendo Confiança Distribuída através de Correntes de Blocos na Virtualização de Funções de Rede” the authors discuss the fundamentals of blockchain, and relate these concepts to research challenges in security in new generation networks. In the second chapter, “Computação Urbana da Teoria à Prática: Fundamentos, Aplicações e Desafios”, the authors discuss the notion of Urban Computing, which correlates urban sensing, data management and its analysis to provide services with the potential to improve the quality of life of the inhabitants of large urban centers, and present the necessary steps to develop an application in this area. In the third chapter, “Processamento Rápido de Pacotes com eBPF e XDP”, the authors aim to present the Extended Berkeley Packet Filter (eBPF), which is used for the fast processing of network packets. In the fourth chapter, “Análise de Dados em Redes Sem Fio de Grande Porte: Processamento em Fluxo em Tempo Real, Tendências e Desafios”, the authors present the main methods of sampling, data collection and monitoring of wireless networks, and characterize knowledge extraction as a machine learning problem in processing large volumes of data. In the fifth chapter, “Introdução à Criptografia para Administradores de Sistemas com TLS, OpenSSL e Apache mod_ssl”, the authors address the use of cryptography by students and information technology professionals with little experience in security and encryption. In the sixth and final chapter, “Introdução à Ciência de Dados: Uma Visão Pragmática utilizando Python, Aplicações e Oportunidades em Redes de Computadores”, the authors present, using the Python language and specific libraries, the most used methods to manipulate and analyze data and extract information, applying them to solve problems in the area of computer networks. The six chapters comprehensively address current topics of the community, being a useful resource for researchers and professionals in the area of computer networks and distributed systems.
Pathdreamer: A World Model for Indoor Navigation

People navigating in unfamiliar buildings take advantage of myriad visual, spatial and semantic cues to efficiently achieve their navigation goals. Towards equipping computational agents with similar capabilities, we introduce Pathdreamer, a visual world model for agents navigating in novel indoor environments. Given one or more previous visual observations, Pathdreamer generates plausible high-resolution 360 visual observations (RGB, semantic segmentation and depth) for viewpoints that have not been visited, in buildings not seen during training. In regions of high uncertainty (e.g. predicting around corners, imagining the contents of an unseen room), Pathdreamer can predict diverse scenes, allowing an agent to sample multiple realistic outcomes for a given trajectory. We demonstrate that Pathdreamer encodes useful and accessible visual, spatial and semantic knowledge about human environments by using it in the downstream task of Vision-and-Language Navigation (VLN). Specifically, we show that planning ahead with Pathdreamer brings about half the benefit of looking ahead at actual observations from unobserved parts of the environment. We hope that Pathdreamer will help unlock model-based approaches to challenging embodied navigation tasks such as navigating to specified objects and VLN.
Stochastic programming on cost optimization in geographically distributed Internet data centers

The development of cloud computing has accelerated the growth of internet data centers (IDCs) worldwide. While a large portion of the energy consumption aroused by intense computation introduces great operation expenditure to the IDC enterprises. To manage the overall cost and utilize resources to its full extent, this paper paid attention on the spatio-temporal workload allocation among the geographically distributed IDCs within a cloud, with the guarantee of the workload completion time and the consideration of computing service delay penalty by introducing the cost of inconvenience. Apart from the effort of the workload migration, the spatio-temporal variance of the renewable energies in the data center microgrids (DMGs) is fully considered in this paper. What's more, as the power consumed by the IDCs are mostly converted into heat, waste heat recovery process is embedded in each IDC to demonstrate the effectiveness of the repurposed heat, which would be used by the residential heating demand in the thermal system, on total cost reduction and energy usage efficiency of the whole operating system. Applying real-life data traces of electricity price, renewable energies and heating demand, the extensive evaluations demonstrate that both spatial and temporal complementary effort on supply side and demand side, power and thermal complementary effort could significantly reduce the overall cost for the IDC enterprise.
[Spatial-temporal variations and influencing factors of eco-environment vulnerability in the karst region of Southeast Yunnan, China].

Due to its vulnerability and anthropogenic disturbance, ecological problems in karst areas are prominent, such as vegetation destruction, soil erosion, and rocky desertification. Comprehensive analysis of ecological vulnerability and influencing factors in karst areas can provide scientific support for regional ecological restoration and environmental governance. With Guangnan County, a typical karst region in southeastern Yunnan, as an example, we constructed a karst regional eco-environmental vulnerability assessment index system from the perspective of natural and factitious factors. We used SPCA to assess the ecological vulnerability in 2000, 2010 and 2018. We further analyzed the temporal and spatial variations and explored its influencing factors by using geographic detectors. From 2000 to 2018, the changes of overall fragility were small, but the degree of fragility had been intensifying. The grade of ecological vulnerability was mainly slight fragile. The area with mild, moderate and severe fragility was increasing, while the area of extremely fragile showed no change. Guangnan County had a higher ecological vulnerability in the south of Zhetu-Liancheng-Yangliujing-Banbang, and lower in the north. The spatial agglomeration effect of vulnerability was strong in this county. The north part of Guangnan was vulnerable low-low agglomeration areas, while the southwest and southeast parts were in high-fragility-high agglomeration areas. The implementation of ecological engineering was conducive to the improvement of regional ecological fragility, while the disturbance of human activities further deteriorated ecological fragility. The area proportion of rocky desertification and stratum lithology had stronger influence on ecological fragility of the karst area. The most important influencing factor of the karst ecological fragility was the development of karst carbonate rocks.
A scalable solution to the nearest neighbor search problem through local-search methods on neighbor graphs

Nearest neighbor search is a powerful abstraction for data access; however, data indexing is troublesome even for approximate indexes. For intrinsically high-dimensional data, high-quality fast searches demand either indexes with impractically large memory usage or preprocessing time. In this paper, we introduce an algorithm to solve a nearest-neighbor query q by minimizing a kernel function defined by the distance from q to each object in the database. The minimization is performed using metaheuristics to solve the problem rapidly; even when some methods in the literature use this strategy behind the scenes, our approach is the first one using it explicitly. We also provide two approaches to select edges in the graph’s construction stage that limit memory footprint and reduce the number of free parameters simultaneously. We carry out a thorough experimental comparison with state-of-the-art indexes through synthetic and real-world datasets; we found out that our contributions achieve competitive performances regarding speed, accuracy, and memory in almost any of our benchmarks.
The review of software cost estimation model: SLIM

Software cost estimation is the process of predicting the amount of effort required to build a software system.Models provides one or more mathematical algorithms that compute cost as a function of a number of variables, size is primary cost factor in most models and can be measuring using lines of code (LOC).The models should be used to estimate the cost of software, SLIM is a useful model to estimate the cost and it is a good model for big projects, also this model required some parameters to generate an estimation.Software cost estimation always characterized one of the biggest challenges in Computer Science for the last decades.Because time and cost estimate at the early stages of the software development are the most difficult to obtain, and they are often the least accurate.Traditional algorithmic techniques such as regression models, Software Life Cycle Management (SLIM), require an estimation process in a long term.
HyperNOMAD: Hyperparameter optimization of deep neural networks using mesh adaptive direct search

The performance of deep neural networks is highly sensitive to the choice of the hyperparameters that define the structure of the network and the learning process. When facing a new application, tuning a deep neural network is a tedious and time-consuming process that is often described as a “dark art.” This explains the necessity of automating the calibration of these hyperparameters. Derivative-free optimization is a field that develops methods designed to optimize time-consuming functions without relying on derivatives. This work introduces the HyperNOMAD package, an extension of the NOMAD software that applies the MADS algorithm [7] to simultaneously tune the hyperparameters responsible for both the architecture and the learning process of a deep neural network (DNN). This generic approach allows for an important flexibility in the exploration of the search space by taking advantage of categorical variables. HyperNOMAD is tested on the MNIST, Fashion-MNIST, and CIFAR-10 datasets and achieves results comparable to the current state of the art.
High-level features for resource economy and fast learning in skill transfer

Abstraction is an important aspect of intelligence which enables agents to construct robust representations for effective decision making. In the last decade, deep networks are proven to be effective due to their ability to form increasingly complex abstractions. However, these abstractions are distributed over many neurons, making the re-use of a learned skill costly. Previous work either enforced formation of abstractions creating a designer bias, or used a large number of neural units without investigating how to obtain high-level features that may more effectively capture the source task. For avoiding designer bias and unsparing resource use, we propose to exploit neural response dynamics to form compact representations to use in skill transfer. For this, we consider two competing methods based on (1) maximum information compression principle and (2) the notion that abstract events tend to generate slowly changing signals, and apply them to the neural signals generated during task execution. To be concrete, in our simulation experiments, we either apply principal component analysis (PCA) or slow feature analysis (SFA) on the signals collected from the last hidden layer of a deep network while it performs a source task, and use these features for skill transfer in a new target task. We compare the generalization performance of these alternatives with the baselines of skill transfer with full layer output and no-transfer settings. Our results show that SFA units are the most successful for skill transfer. SFA as well as PCA, incur less resources compared to usual skill transfer, whereby many units formed show a localized response reflecting end-effector-obstacle-goal relations. Finally, SFA units with lowest eigenvalues resembles symbolic representations that highly correlate with high-level features such as joint angles which might be thought of precursors for fully symbolic systems.
Towards Fairness-Aware Ranking by Defining Latent Groups Using Inferred Features

Group fairness in search and recommendation is drawing increasing attention in recent years. This paper explores how to define latent groups, which cannot be determined by self-contained features but must be inferred from external data sources, for fairness-aware ranking. In particular, taking the Semantic Scholar dataset released in TREC 2020 Fairness Ranking Track as a case study, we infer and extract multiple fairness related dimensions of author identity including gender and location to construct groups. Furthermore, we propose a fairness-aware re-ranking algorithm incorporating both weighted relevance and diversity of returned items for given queries. Our experimental results demonstrate that different combinations of relative weights assigned to relevance, gender, and location groups perform as expected.
Quantum Brain Networks: A Perspective

We propose Quantum Brain Networks (QBraiNs) as a new interdisciplinary field integrating knowledge and methods from neurotechnology, artificial intelligence, and quantum computing. The objective is to develop an enhanced connectivity between the human brain and quantum computers for a variety of disruptive applications. We foresee the emergence of hybrid classical-quantum networks of wetware and hardware nodes, mediated by machine learning techniques and brain–machine interfaces. QBraiNs will harness and transform in unprecedented ways arts, science, technologies, and entrepreneurship, in particular activities related to medicine, Internet of Humans, intelligent devices, sensorial experience, gaming, Internet of Things, crypto trading, and business.
Hand-Drawn Electrical Circuit Recognition Using Object Detection and Node Recognition

With the recent developments in neural networks, there has been a resurgence in algorithms for the automatic generation of simulation ready electronic circuits from hand-drawn circuits. However, most of the approaches in the literature were confined to classify different types of electrical components. Only a few of those methods have shown a way to rebuild the circuit schematic from the scanned image, which is extremely important for further automation of netlist generation. This paper proposes (1) a real-time algorithm for the automatic recognition of hand-drawn electronic circuits and (2) subsequently rebuild the circuit schematic based on object detection and circuit node recognition. The automatic recognition employs light weight and popular you only look once version 5 (YOLOv5) for detection of circuit components and a novel Hough transform-based approach for node recognition. Using YOLOv5 object detection algorithm, a mean average precision (mAP0.5) of 98.2% is achieved in detecting the components. The proposed method is also able to rebuild the circuit schematic with 80% accuracy with a near-real-time performance of 0.33 s per schematic generation.
Transfer Learning to Improve Breast Cancer Detection on Unannotated Screening Mammography

Breast cancer ranks in the top half of the division of cancers that prove to be fatal. The early detection of the disease is pivotal for the survival of patients. Recently, advances in deep learning have been used for the detection of breast cancer. These systems are often trained with sizeable medical imaging datasets of screening mammography prepared and annotated by expert radiologists. When developing such models, researchers face difficulty in obtaining the datasets required for training. The study presents a transfer-learning based approach that attempts to address the problems faced by existing breast cancer detection models trained with large annotated screening mammography datasets. Further, these models do not perform well on datasets without such annotations. The study attempts to solve these problems by using a multi-stage approach to train a breast cancer detection model. The initial stage involves training a Convolutional Neural Network (CNN) based model on a large publicly available dataset of annotated breast cancer mammography. The study then uses Transfer Learning to exploit the hierarchical learning properties of CNNs to fine-tune the model on a dataset of unannotated mammograms. An evaluation of the model at detecting breast cancer in unannotated screening mammography resulted in an accuracy of 75% and a reduced false-negative rate, marking an improvement over the benchmarked existing approach. The findings of the study show that transfer learning-based approaches improve the performance of an unannotated screening mammography breast cancer detector. The study also suggests the use of such systems as potential computer-aided diagnosis (CAD) tools.
Detecting Objects from No-Object Regions: A Context-Based Data Augmentation for Object Detection

Data augmentation is an important technique to improve the performance of deep learning models in many vision tasks such as object detection.Recently, some works proposed the copy-paste method, which augments training dataset by copying foreground objects and pasting them on background images.By designing a learning-based context model to predict realistic placement regions, these approaches have been proved to be more effective than traditional data augmentation methods.However, the performance of the existing context model was limited by three problems: (1) The definitions of positive and negative samples generate too much label noise.( 2) The examples with masked regions lose a lot of context information.(3) The sizes (i.e., scale and aspect ratios) of predicted regions are sampled from a prior shape distribution, which leads to a coarse estimation.In this work, we first explore the placement rules that generate realism and effective training examples for detectors.And then, we propose a trainable context model in order to find proper placement regions by classifying and refining dense prior default boxes.We also design a corresponding reasonable generation for training examples by annotating ground truth on free space according to the placement rules.The experimental results on PASCAL VOC show that our approach outperforms the state-of-the-artrelated work.