Miracle-dz/UniFAR-FTUs huggingface dataset:
Radio over Ethernet (RoE), architecture and design considerations

Introduction: the evolving mobile networks are envisioned to have a flexible and reliable network to meet all design requirements needed for the fifth generation (5G) of networks. The cloud radio access network, as an evolution in mobile networks, changes the traditional architecture of the network by moving the BBUs to the central office. This comes with new challenges that are addressed by new technologies like Radio over Fiber and now Radio over Ethernet, which proposes a technique to transmit all radio data types over a traditional Ethernet based front-haul network. In this paper, we focus on the architecture and the design considerations of Radio-over-Ethernet to have a more flexible and reliable front-haul network in Centralized Radio Access Network.Material and method: In this paper we tried to use Ethernet protocol as a universal and public network protocol in radio based networks to make it flexible and reliable. This combination allows us to focus only on the access points and propose a new architecture to encapsulation (retrieve) radio data into (from) an Ethernet frame.Result: This study resulted in a new architecture for radio access networks to disseminate radio data over a reliable network protocol and infrastructure. Some Ethernet header fields was modified and a mapper was included into the model in BBUs to create an adaption between radio and Ethernet infrastructures.Conclusion: The result shows that although this new architecture may apply additional overhead in both information and process, but having an independent front-haul network is a necessity for Centralized Radio Access Network. However if industry implements this architecture and its processes regarding latency requirement, Radio over Ethernet will be a revolution in Centralized Radio Access Network to meet two main key design principles in 5G.
Diagnosis of Melanoma Skin Cancer from Digital Images using Texture Distinctiveness with Neural Network

Incidence rate of skin cancer are increasing day by day. Skin cancer is one of the deadliest forms of cancer but detected earlier can save the life time of the human being. An automated screening system is introduced to identify the presence of skin cancer in advance. In this paper, texture distinctiveness lesion segmentation algorithm is used. Experience and training-based characteristics of back propagation neural network is used with texture distinctiveness lesion segmentation algorithm, for identifying the normal and abnormal portions of skin . The most commonly occurring skin cancer is Melanoma. The proposed system is to diagnose the presence of this skin cancer and its probability of risk as low, medium, high.
Power-Aware Operating System for Interactive System

Power management saves the power by shutting down the idle devices. These devices often serve requests from concurrently running tasks, ordering task execution can adjust the lengths of idle periods and exploit better opportunities for power management. As a increasing awareness of economic and environmental cost of power consumption by interactive systems, has raised an important issue to design not only hardware but also an operating system on an energy efficiency scheme. However, architectural techniques is not the only process which helps in the power reduction but it includes some more procedures. This paper presents an approach of operating system to reduce power consumption. This can be done if the operating system kernel estimates the utilization of a device from each process. If a device is not under use from any of the programs ,the device can be put into lower power state . This paper gives how the scheduling can facilitate power management . This paper presents an approach for using OS to reduce power consumption of input and output devices in laptops ,pc's (interactive systems).
Promoting Intrinsic Motivation with a Mobile Augmented Reality Learning Environment.

In this study the impact of a mobile augmented reality learning environment on motivation, learning effects and cognitive load was tested. Students participated in a two-hour history lesson while using their smartphones to turn static pictures into animations with an Augmented Reality (AR) application. Interest, perceived competence and perceived choice as indicators for intrinsic motivation were assessed. Results of the AR learning group were compared to a non-AR teacher-centered learning environment. The results reveal that augmented reality learning can promote intrinsic motivation and has an impact on history learning. Cognitive load has not been detected as a problem within the AR group.
Study Algorithms which Assessed Quality of the Blurred Images

No-reference measurement of blurring artifacts in images is a difficult problem in image quality assessment field. In this paper, we present a no-reference blur metric to measure the quality of the images. These images are degraded using Gaussian blurring. Suggestion method depends on developing the Mean of Locally Standard deviation and Mean of the image (MLSD) model, this method is called Blur Quality Metric (BQM) and it calculates from numerical integral of the function in this model. And the BQM is compared with the No-reference Perceptual Blur Metrics (PBM) and the Entropy of the First Derivative (EFD) Image; the BQM is a simple metric and gives good accuracy in metrics the quality for the Gaussian blurred image if it compared with another algorithms.
Public and Private in South Korea's Education Reform Vocabulary: An Evolving Statist Culture of Education Policy

Statism is a political economy that prevails in many East Asian countries. This paper explores its negative role in South Korea’s education reform since the restoration of civilian democracy in 1993. It takes note of South Koreans’ aberrant use of the terms ‘public education’ and ‘private education’ and the frame of reference for policy discourses based thereupon. It then shows how this frame of reference restricts the grasp of structural educational problems and the practical context in which to explore policy measures for what the policy makers pursue, liberalisation and diversification. Finally, it relates the aberrant use of the terms to a statist culture that has evolved through the years of military elite’s developmental policy and continues to determine the scope of discourses in a post-military era. By doing this, the paper seeks to expand the political economy discourses of statism and institutionalism in the field of education. Political economy of education, statism, institutionalism, private education, public education, education reform, Korea
Retos y perspectivas en el ecosistema de la información biomédica

Los nuevos flujos informativos y el derrumbe del monopolio periodistico en la intermediacion con el publico han creado oportunidades y disfunciones en la informacion biomedica. A los problemas de la exageracion y la comercializacion, se han anadido los relacionados con la falta de filtros y la difusion de la mala ciencia. En este articulo se discuten los retos que plantean estos problemas y las perspectivas en la investigacion academica, la comunicacion y el periodismo.
Artifact reduction using segmentation constrained RPCA for CT

In this study, we aim to separate the ghost artifacts from the limited angle CT image by using Robust Principle Component Analysis (RPCA) and thus improve the reconstructed CT images. Conventionally, RPCA method separates the foreground and the background. Often, the background is assumed as static or quasi-static. When applied to limited angle CT images, the artifacts are considered as quasi-static background whereas the anatomical structures are considered foreground. Thus, RPCA is performed to segment the foreground from the background. Finally, different post-reconstruction de-noising parameters are applied to each foreground and background to remove the artifact effectively.
Waveform Flexibility for Network Slicing

We discuss the idea of waveform flexibility and resource allocation in future wireless networks as a promising tool for network slicing implementation down to the lowest layers of the OSI (Open Systems Interconnection) models. In particular, we consider the possibility of cognitively adjusting the shape of the waveform to the requirements associated with various network slices. Moreover, such an adjustment of waveform shape is realised jointly with the selection and allocation of the appropriate frequency bands to each slice. In our approach, the definition of the waveform, as well as the assignment of resources, is done based on the information about the surrounding environment and each slice requirement stored in a dedicated context-information database. In this paper, we present the key concept of waveform flexibility for network slicing, the proposed algorithm for waveform selection and resource allocation among slices, and the achieved simulation results.
Appropriate Complementary Feeding Practices and Associated Factors Among Caregivers of Children Age 6-23 Months in Rural Areas of Hunan Province, China: A Community-based Cross-sectional Study

BackgroundThis study aimed to evaluate the quality and associated factors that potentially influence complementary feeding practices among Chinese caregivers living in Hunan, a rapidly developing province in China. MethodsThree indicators( minimum dietary diversity(MDD), minimum meal frequency(MMF) and minimum acceptable diet(MAD)) were used to evaluate the rationality of complementary feeding. A multi-factor unconditional logistic regression model was employed to analyzeanalyse the relevant factors of complementary feeding. ResultsThe eligibility rates of MDD, MMF and MAD for children aged 6-23 months were 62.3%, 75.1% and 44.0%, respectively. The ineligible OR values of MDD for children aged 12-17 and 18-23 months were 0.238 and 0.103, respectively, and those of MAD were 0.425 and 0.376, respectively, compared with those for children aged 6-11 months. Compared children whose mothers moved out for work, the ineligible MMF for children whose mothers were not migrant workers was 2.069. Compared with parents who scored less than 60 in knowledge and attitude towards nutrition and health, the ineligible OR values of MDD, MMF and MAD for children whose parents obtained 60-79 were 0.653, 0.689 and 0.668, respectively, and those for children whose parents obtained 80-100 were 0.543, 0.428 and 0.496, respectively (all P values <0.05). ConclusionThe complementary feeding of children in rural areas of Hunan Province needs to be improved. Younger month-age, mothers’ not going out to work and parents’ lacking in knowledge about nutrition and health are high risk factors for poor complementary feeding.
Alice Turk and Stefanie Shattuck-Hufnagel (2020). Speech timing: implications for theories of phonology, phonetics, and speech motor control. (Oxford Studies in Phonology and Phonetics 5.) Oxford: Oxford University Press. Pp. xv + 370.

There is increasing awareness that the temporal dimension of speech, in particular the relative timing of speech movements, contains rich information about phonological structure. Relating abstract phonological structure to the temporal unfolding of realistically variable speech data remains a major interdisciplinary challenge. It is this challenge that is taken up in Speech timing: implications for theories of phonology, phonetics, and speech motor control, henceforth Speech timing. The book has eleven chapters, including a short introduction and a conclusion. The main proposal – a sketch of a model mapping phonological representations to continuous movements of articulators – comes in the second half of the book, particularly in Chapters 7 and 10. The second half also includes chapters on optimisation (Ch. 8: ‘Optimization’) and general mechanisms for timing (Ch. 9: ‘How do timing mechanisms work? ’). These provide a unique synthesis of speech and nonspeech literature, which is highly accessible for linguists, and serves to motivate aspects of the main proposal. The first half of the book provides a description and critique of the theory of Articulatory Phonology, developed in the Task Dynamics framework (AP/TD) (e.g. Browman & Goldstein 1986, Saltzman & Munhall 1989). On the view of the authors:
Heart failure risk prediction and medicine recommendation system using exploratory analysis and big data analytics

With the ever increasing population of the world, diseases and their possibilities are also increasing at an alarming rate. As time passes by, diagnosing diseases and providing appropriate treatment at the right time has become quite a challenge. Heart diseases, for one, have been a major cause of death worldwide. Therefore, this research has been focused on finding an efficient way to predict the chances of a heart failure and accordingly, recommend appropriate medicines to aid cardiologists in quicker decision making. The research includes finding the correlations or associations between the various attributes of the dataset by utilizing the standard techniques of exploratory analysis and hence using the attributes suitably to predict the chances of a heart failure, as well as the medicine recommendations. A comparative study has also been included which shows the various attained accuracy rates of different machine learning algorithms including Logistic Regression, Naïve Bayes, Decision Tree, Linear SVC, Random Forest, and Gradient Boosting Classifier. The Apache Spark framework has been used in order to make the system capable of handling big data.
AI: from deep learning to in-memory computing

In the past few years, Artificial Intelligence (AI) has been a subject of intense media hype. Machine learning, deep learning (DL), and AI come up in countless articles, often outside of technology-minded publications. As the AI hype keeps growing, it is important to be able to recognize the signal in the noise, to tell apart world-changing developments from what are merely over-hyped press releases. This paper tries to explain how deep learning is working and how GPU (Graphic Processing Unite) can make it a reality. Finally, in-memory computing (IMC) for DL is introduced to point out future high performance and low power DL hardware development direction.
A Computational Model of Context‐Dependent Encodings During Category Learning

Abstract Although current exemplar models of category learning are flexible and can capture how different features are emphasized for different categories, they still lack the flexibility to adapt to local changes in category learning, such as the effect of different sequences of study. In this paper, we introduce a new model of category learning, the Sequential Attention Theory Model (SAT‐M), in which the encoding of each presented item is influenced not only by its category assignment (global context) as in other exemplar models, but also by how its properties relate to the properties of temporally neighboring items (local context). By fitting SAT‐M to data from experiments comparing category learning with different sequences of trials (interleaved vs. blocked), we demonstrate that SAT‐M captures the effect of local context and predicts when interleaved or blocked training will result in better testing performance across three different studies. Comparatively, ALCOVE, SUSTAIN, and a version of SAT‐M without locally adaptive encoding provided poor fits to the results. Moreover, we evaluated the direct prediction of the model that different sequences of training change what learners encode and determined that the best‐fit encoding parameter values match learners’ looking times during training.
Adaptive Irregular Graph Construction-Based Salient Object Detection

Saliency detection represents a vital pre-processing stage of computer vision. Most existing propagation-based salient object detection methods construct a <inline-formula> <tex-math notation="LaTeX">$k$ </tex-math></inline-formula>-regular graph for saliency propagation. Applying a regular graph to a vast smooth region is potentially prone to unnecessary or prolonged propagation errors, leading to the excessive highlighting of the background regions. To mitigate such problems, we substitute the conventional <inline-formula> <tex-math notation="LaTeX">$k$ </tex-math></inline-formula>-regular graph with an adaptive irregular graph for saliency value propagation, thereby avoiding unnecessary iterations over a vast smooth region. We first perform a clustering analysis based on the smoothness, color, and other features of regions. The new graph boosts an adaptive link density by considering the clustering result. In addition, we propose a seeding strategy for the propagation. Based on our experimental studies of six major benchmark datasets, our method performed favorably against the other state-of-the-art methods, both quantitatively and qualitatively.
A Temporal Analysis of Expectation Management: Evidence from Public Management Earnings Forecasts

We investigate the time trend of expectation management through management earnings forecasts to avoid negative earnings surprise. We find that the percentage of firms missing analyst earnings target before downward management guidance but meeting revised analyst earnings target have more than tripled from 5.5% in 1995 to 20.0% in 2014. We also show increase of both the magnitude of pessimistic management forecast bias and analysts’ tendency to converge to management guidance, two potential explanations for increasing expectation management. Additional analysis indicates that the increase in analyst convergence is more prominent for firms with larger downward bias in previous management forecasts.
FastSyzkaller: Improving Fuzz Efficiency for Linux Kernel Fuzzing

Linux is one of the most popular operating systems and enjoys a large number of subscribers on a world-wide scale. To ensure its stability and reliability, fuzzing is an effective method to trigger crashes hidden in the Linux kernel. In this paper, we apply N-Gram model to extract vulnerable program behaviours and dig out vulnerable patterns to guide the test case generation phase of the traditional fuzzing technique so as to improve the fuzzing efficiency from the Linux system call aspect. The experiment is implemented on the basis of a Google well-known open-source project Syzkaller, a fuzzer that targeted to the Linux kernel and other OS kernels as well. It is an unsupervised coverage-guided kernel fuzzer, set up with a manager, a fuzzer and an executor. This paper proposes FastSyzkaller which combines Syzkaller with N-Gram model in its fuzzer to optimize the test case generation process to improve the fuzzing efficiency. In 4 weeks, FastSyzkaller exposes 29 different crash types. However, Syzkaller only exposes 12 types. FastSyzkaller also produces crashes 3x faster than Syzkaller, also with 3x more unique crashes from the quantity perspective.
Therapeutic Effect of Glycine Max (Soybean) Bioactive Components in Cvd and Obesity

The cardiovascular diseases (CVD) and obesity are amongst the most prominent diseases across the globe. Hyper-lipidemia is an underlying factor in the proliferation of these diseases. Food substances like soybean have been reported for its hypolipidemic property and decreased the risk of obesity and CVD by lowering the lipid concentration in serum profile. Many advanced researches explored that soya bean and soy related food products constitute a significant amount of bioactive components like oligosaccharides, globulin proteins (glycinin, beta-conglycinin), antioxidants, isoflavones (genistein, daidzen, glycitein), phytosterols (sitosterol, sitostanol ,campesterol) and phyto-estrogen. These components have eminent characteristics for the improvement of health that was seen in both humans and animals. After studying the results of different experimental researches, glycine max constituents and its supplements lowered cardiovascular parameters; lipid profile
Study on Development of Mobile Wireless Technologies

Over the latest couple of decades, Mobile Wireless Communication frameworks have experienced a surprising change. The versatile remote Generation (G) generally implies a change in the possibility of the system, speed, innovation, recurrence, information limit, inactivity and so forth. Every age has a few models, distinctive limits, new procedures and new highlights which separate it from the previous one. The original (1G) portable remote correspondence organize was simple used for voice calls as it were. The second era (2G) is an advanced innovation and upheld content informing. The third-age (3G) versatile innovation gave higher information transmission rate, expanded limit and gave intelligent media reinforce. The fourth era (4G) joins 3G with settled web to encourage remote portable web, which is a progression to versatile innovation and it beat the impediments of 3G. there was an expansion in the data transmission and diminished the expense of assets [1]. 5G remains for fifth Generation versatile innovation and will be disclosed as another turmoil in a convenient market which will change the method for using phones inside the plain high transfer speed. A client will never encounter ever such high regard innovation which will join a wide range of cutting edge highlights and 5G innovation will be the most exceptional and in colossal interest in the coming future.
Efeito da ordem dos exercícios no treinamento de força sobre a pressão arterial pós esforço em idosas hipertensas

O objetivo deste estudo foi analisar o efeito de diferentes ordens de exercícios sobre a pressão arterial (PA) após sessões de treinamento de força (TF). Quinze idosas hipertensas inexperientes em TF foram divididas em dois grupos. Grupo de membros superiores (G1) e grupo membros inferiores (G2). O G1 realizou três séries de 15 repetições submáximas na sequência de exercícios: Supino Reto (SR), Remada Fechada (RF), Tríceps no Puxador (TP) e Rosca Bíceps (RB). A segunda sequência do G1 foi inversa (RB, TP, RF, SR). O G2 realizou quatro séries de 15 repetições submáximas na sequência de exercícios: Leg Press (LP), Cadeira Extensora (CE) e Flexão Plantar (FP). A segunda sequência do G2 foi inversa FP, CE, LP. A PA foi mensurada em repouso e após as sessões de exercícios durante 60 minutos. Para análise dos dados, utilizou-se uma ANOVA de dois caminhos com medidas repetidas e post hoc de Tukey para verificar as diferenças na PA antes e após as sessões de treinamento. Ambos os grupos apresentaram reduções significativas na PA sistólica pós-exercício com a sequência de exercícios do grande para o pequeno grupo muscular G1 (20, 30 e 40 min) e G2 (30, 40, 50 e 60 min). Quando a sequência foi inversa, foram observadas diferenças significativas emalguns momentos G1 (30 e 40 min) e G2 (40 e 50 min). Não foram encontradas diferenças significativas entre os grupos e também para a PA diastólica em qualquer sequência. Os resultados indicam que quando a sequência de exercícios é iniciada do grande para o pequeno grupo muscular, há uma tendência para maior duração do efeito hipotensivo.
An online non-intrusive load monitoring method based on Hidden Markov model

Non-intrusive load monitoring (NILM) can decompose the total power consumption measured by the smart meter into the power consumed by the individual appliances, so as to achieve the purpose of saving energy. In this paper, an improved method of Daubechies9 (DB9) which is a discrete wavelet is proposed, which can effectively remove the noise of the low-frequency components. On this basis, an online NILM method based on Hidden Markov model (HMM) is proposed. The model of load switching can be built using apparent power of transient-state with this method. Besides, the improved forward algorithm which effectively suppressing the data underflow in load classification is proposed. The proposed methods are embedded in the smart meter and can increase the overall recognition rate of the load over 90% in the experiments which prove that they have good applicability.