Miracle-dz/UniFAR-FTUs huggingface dataset:
Scalable, Network-Wide Telemetry with Programmable Switches

Managing modern networks requires collecting and analyzing network traffic from distributed switches in real time, i.e., performing network-wide telemetry. Telemetry systems must be flexible and fine-grained to support myriad queries about the security, performance, and reliability of networks. Yet, they must also scale as the number of queries, link speeds, and the size of the networks increase. Realizing these goals requires balancing the division of labor between high-speed, but resource constrained, network switches and generalpurpose CPUs to support flexible telemetry at scale. First, we present Sonata, a flexible and scalable network telemetry system that uses the compute resources of both stream-processing servers and a single Protocol Independent Switch Architecture (PISA) switch. PISA switches offer both high-speed processing and limited programmability. We show how to execute Sonata’s high-level queries at line rate by first compiling them to PISA primitives. Next, we model the resource constraints of PISA switches to solve an optimization problem that minimizes the load on the stream processor by executing portions of queries directly in the switch. Sonata can support a wide range of monitoring queries and reduces the stream processor’s workload by orders of magnitude over existing telemetry systems. Second, we present Herd, a system for implementing a subset of Sonata queries distributed across several switches. Herd determines network-wide heavy hitters, i.e., flows that consist of many more packets than most others, by counting flows at the switches, without maintaining per-flow state, and probabilistically reporting to a central coordinator. Based on these reports, the coordinator adapts parameters at each switch based on the spatial locality of the flows. Simulations using packet traces show that our prototype can detect network-wide heavy hitters accurately with 17% savings in communication overhead and 38% savings in switch state compared to existing approaches. We then present an algorithm to tune system parameters in order to maximize detection accuracy under switch memory and bandwidth constraints. iii Together, Sonata and Herd provide network operators the ability to execute a set of network-wide telemetry queries from a single interface that combines the strengths of both programmable data planes and general-purpose CPUs.
Determination of news biasedness using content sentiment analysis algorithm

Nowadays, identifying news biases in the social media is one of the most fundamental problems. News bias is a complex process that comprises several dimensions to be taken into account and it is interlinked with social, political and economic problems. In general, news bias has the ability to reflect opinion of people about a topic or government policies and actions. The proposed algorithm develops a system which can detect the biasedness of news topics from different news Websites.This approach automatically collects the news contents from various online news media portals and then consolidates them for the determination of news biasedness. In the experimental study, the news topics are gathered from various Websites of U.S., U.K., and India. For training dataset 3265 news sentences were collected under various news topics from 20 different news Websites. The effectiveness of classification of algorithm is proved by the extensive experimental study. The proposed algorithm provides a method improves the determination of news biasedness, which in turn may help in providing impartial, unbiased and reliable information.
Learning Semantic Correspondences from Noisy Data-text Pairs by Local-to-Global Alignments

Learning semantic correspondences between structured input data (e.g., slot-value pairs) and associated texts is a core problem for many downstream NLP applications, e.g., data-to-text generation. Large-scale datasets recently proposed for generation contain loosely corresponding data text pairs, where part of spans in text cannot be aligned to its incomplete paired input. To learn semantic correspondences from such datasets, we propose a two-stage local-to-global alignment (L2GA) framework. First, a local model based on multi-instance learning is applied to build alignments for texts spans that can be directly grounded to its paired structured input. Then, a novel global model built upon a memory-guided conditional random field (CRF) layer aims to infer missing alignments for text spans which not supported by paired incomplete inputs, where the memory is designed to leverage alignment clues provided by the local model to strengthen the global model. In this way, the local model and global model can work jointly to learn semantic correspondences in the same framework. Experimental results show that our proposed method can be generalized to both restaurant and computer domains and improve the alignment accuracy.
Artificial Intelligence Model for Earlier Prediction of Cardiac Functionalities Using Multilayer Perceptron

Cardiovascular Disease is the silent killer and it is one of the leading cause for global death annually. The percentage of premature death varies from 7% in high-income countries and 43% in low-income countries. It is mainly due to lifestyle changing factors such as obesity, diabetes, etc. While working to reduce earlier deaths, it is revealed, how important the earlier prediction of heart disease is. In the medical field, diagnosing heart disease earlier is a difficult task for medical practitioner since it depends on combining clinical and pathological data. The purpose of this paper is to implement a medical prediction support system for predicting cardiac disease. Deep learning approach based computational model is designed for diagnosis. This proposed system has three main steps. First, a dataset with 13 attributes (13 clinical features) from the website is collected. Second, the datasets are trained using an algorithm called artificial neural network with backpropagation technique. It can have one or more hidden layers in order to get higher accuracy. Finally, Cardiac Function Prediction System (CFPS) which is an interactive GUI is developed, where the user can enter the clinical features and get to know the current status of a patient’s health. This system enhances medical care and reduces the treatment cost. This system will act as a promising tool for the medical practitioner for proper diagnosis.
Research on Opioid Propagation in the United States Based on Artificial Neural Network

Nowadays the United State is going through a national crisis regarding the use of synthetic and non-synthetic opioids. In this paper, firstly, we will describe the spread and characteristic of opioid cases based on Multi-Layer Perceptron (MLP). Then find the possible place where opioid might have started with and predict the future situation. Secondly, we select some most important evaluation indexes by regression analysis. Thirdly, improve the model by using Principal Component Analysis (PCA) to have dimension reduction of the thirteen most important evaluation indexes and construct the comprehensive effect factor as model’s inputs to describe the influence of location and the U.S. Census socio-economic conditions.
Chest X-Ray Image Classification on Common Thorax Diseases using GLCM and AlexNet Deep Features

Image processing has been progressing far in medical as it is one of the main techniques used in the development of medical imaging diagnosis system. Some of the medical imaging modalities are the Magnetic Resonance Imaging (MRI), Computed Tomography (CT) Scan, X-Ray and Ultrasound. The output from all of these modalities would later be reviewed by the expert for an accurate result. Ensemble methods in machine learning are able to provide an automatic detection that can be used in the development of computer aided diagnosis system which can aid the experts in making their diagnosis. This paper presents the investigation on the classification of fourteen thorax diseases using chest x-ray image from ChestX-Ray8 database using Grey Level Co-occurrence Matrix (GLCM) and AlexNet feature extraction which are process using supervised classifiers: Zero R, k-NN, Naive Bayes, PART, and J48 Tree. The classification accuracy result indicates that k-NN classifier gave the highest accuracy compare to the other classifiers with 47.51% accuracy for GLCM feature extraction method and 47.18% for AlexNet feature extraction method. The result shows that number of data by class and multilabelled data will influence the classifcation method. Data using GLCM feature extraction method has higher classification accuracy compared to AlexNet and required less processing step.
Detection Method of Fast Flux Service Network Based on Decision Tree Algorithm

Fast Flux service network is the main threats to network security at present. By quickly changing IP address, the location of servers can be hidden and agents can be provided for botnet. In this paper, a detection method of fast flux service network is proposed, which narrows the detection range through pretreatment, then constructs the detection features, and accurately distinguishes FFSN from legitimate network. Finally, decision tree algorithm is used to obtain the ideal results.
The effect of Humanities Education Program on Self- esteem Life Satisfaction of Korea's Elderly

This study was conducted between May and June 2015 at the six senior welfare centers in Chungnam Province, South Korea, to evaluate the effects of a humanities education program on the self-esteem and life satisfaction of the elderly. First, the humanities education program can be regarded as having positively affected the experimental group's self-esteem and life satisfaction. Lifelong education is an active process of growth, restoration, and reconstruction. The learning achieved in adult education is a whole-life process by which the unified and holistic changes in a learner's experience are obtained internally. The foundation of many types of learning requires an existential and reflective understanding of human beings, including understanding ourselves, as much as perceptions of the world. The programs whose main curricula include courses with humanity at their center, such as humanism, human rights, and humanities fill the core of adult education(1-3). Therefore, these education programs are expected to increase the elderly's self- esteem and their levels of life satisfaction, and, correspondingly, humanities education programs targeted at the elderly were launched. For instance, in 2009, an education program was carried out at the Seoul City Senior Welfare Center, funded by Seoul City(4), and today in 2015, programs are underway at senior welfare centers across the nation, with the Senior Welfare Center Association at the core of the movement, that are funded by the Community Chest of Korea. However, very few empirical studies have investigated the effectiveness of humanities education programs. In sum, the effectiveness of humanities education programs has been proven for different groups, but very few studies have looked at their effects among the elderly. In aging Korean society, it is necessary to develop humanities education programs for seniors and to evaluate their effectiveness in order to change the distorted images of the elderly. The aim is to enhance their abilities so that they can live among us as productive "senior citizens"rather than as "just old people. "In line with this, this study
Prediction of student academic performance using Moodle data from a Further Education setting.

Increasingly educational providers are being challenged to use their data stores to improve teaching and learning outcomes for their students. A common source of such data is learning management systems which enable providers to manage a virtual platform or space where learning materials and activities can be provided for students to engage with. This study investigated whether data from the learning management system Moodle can be used to predict academic performance of students in a blended learning further education setting. This was achieved by constructing measures of student activity from Moodle logs of further education courses. These were used to predict alphabetic student grade and whether a student would pass or fail the course. A key focus was classifiers that could predict likelihood of failure from data available early in the term. The results showed that classifiers built on all course data predicted student grade moderately well (accuracy= 60.5%, kappa = 0.43) and whether a student would pass or fail very well (accuracy= 92.2%, kappa=0.79). However, classifiers built on the first six weeks of data did not predict failing students well. Classifiers trained on the first ten weeks of data improved significantly on a no-information rate (p<0.008) though more than half of failing students were still misclassified. The evidence indicates that measures of Moodle activity on further education courses could be useful as part of on an early-warning system at ten weeks.
Mobile anti-phishing: Approaches and challenges

ABSTRACT As technology is constantly evolving, modern conveniences such as mobile devices have changed the way that society performs its daily routines and functions as a whole. Because the evolution of technology has made it almost a requirement for citizens to possess mobile device, this increase in ownership of mobile devices has also brought security issues such as phishing. A phisher intends to obtain information that will aid the hacker by appearing to the unsuspecting owner as a trustworthy entity. The goal of phishing is to fool the owners of mobile devices into giving the attacker sensitive information, such as usernames, passwords, as well as credit and debit card information. In this paper, we provide a taxonomy of mobile anti-phishing techniques on mobile devices and mitigation techniques that are available for use on mobile devices. Our goal is to provide an informative model to identify current solutions in reducing phishing attacks on mobile devices and outline limitations and future challenges.
Representation Learning and Dynamic Programming for Arc-Hybrid Parsing

We present a new method for transition-based parsing where a solution is a pair made of a dependency tree and a derivation graph describing the construction of the former. From this representation we are able to derive an efficient parsing algorithm and design a neural network that learns vertex representations and arc scores. Experimentally, although we only train via local classifiers, our approach improves over previous arc-hybrid systems and reach state-of-the-art parsing accuracy.
Flower Classification with Deep CNN and Machine Learning Algorithms

Development of the recognition of rare plant species will be advantageous in the fields such as the pharmaceutical industry, botany, agricultural, and trade activities. It was also very challenging that there is diversity of flower species and it is very hard to classify them when they can be very similar to each other indeed. Therefore, this subject has already become crucial. In this context, this paper presents a classification system for flower images by using Deep CNN and Data Augmentation. Recently, Deep CNN techniques have become the latest technology for such problems. However, the fact is that getting better performance for the flower classification is stuck due to the lack of labeled data. In the study, there are three primary contributions: First, we proposed a classification model to cultivate the performance of classifying of flower images by using Deep CNN for extracting the features and various machine learning algorithms for classifying purposes. Second, we demonstrated the use of image augmentation for achieving better performance results. Last, we compared the performances of the machine-learning classifiers such as SVM, Random Forest, KNN, and Multi-Layer Perceptron(MLP). In the study, we evaluated our classification system using two datasets: Oxford-17 Flowers, and Oxford-102 Flowers. We divided each dataset into the training and test sets by 0.8 and 0.2, respectively. As a result, we obtained the best accuracy for Oxford 102-Flowers Dataset as 98.5% using SVM Classifier. For Oxford 17-Flowers Dataset, we found the best accuracy as 99.8% with MLP Classifier. These results are better than others’ that classify the same datasets in the literature.
A New Fuzzy-Based Resource Management System for SDN-VANETs

Vehicular ad hoc networks (VANETs) face several technical challenges in deployment and management due to variable capacity of wireless links, bandwidth constrains, high latency and dynamic topology. Cloud, fog, and edge computing are considered a way to deal with these communication challenges. On the other hand, software defined networking (SDN) can be used to provide flexibility, programmability, scalability, and global knowledge as well as new services and features. In this article, the authors propose an intelligent approach for resource management in SDN-VANETs using fuzzy logic. A layered Cloud-Fog-Edge architecture in SDN-VANETs is introduced, which is coordinated by the SDN Controller (SDNC). A fuzzy-based system implemented in SDNC is used to make decisions on the processing layer of the VANETs application data. The decision is made by prioritizing the application requirements and by considering the available connections. The authors demonstrate in simulation the feasibility of the proposed system to improve the management of the network resources.
The development of a digital design workflow for thesurgical obturator

The design of the current surgical obturator is not based on 3d delineation of the tumour and associated resection margins in CT and/or MR. This results in limited prior insight into the shape and extend of the defect that will be created during the maxillectomy procedure and prevents the presurgical production of a hollow surgical obturator. The surgical obturator is based on the intraoperative combination of a premade baseplate, which covers the defect, and a putty bulb, which fills the defect. The putty bulb is heavy which results in reduced patient comfort. A digital workflow for obturator design aims to improve the obturator production time, reduce surgical time and increase patient comfort. A digital surgical baseplate was successfully planned, printed and evaluated parallel to the traditional design workflow. Evaluation of the relation between the traditional obturator bulb surface and the remaining bony skull segments shows that an undersized hollow obturator bulb leave a distance of four millimetres to the nearest bone segment. This study shows that digital design can aid obturator design and production. However, it also shows the need for the introduction of digital delineation in the presurgical planning stage and illustrates the need for verification of results during and after surgery.
Comparison between EM Algorithm and Multiple Imputation on Predicting Children’s Weight at School Entry

EM Algorithm and Multiple Imputation are widely used methods in dealing with missing data. Although Multiple Imputation always be the favourite choice of researcher due to its accuracy and simple application, but the issue arises whether EM algorithm perform better with several times of imputation. Both methods will be tested using different number of imputations with the help of Amelia and Mice package in R software. The imputed data sets are compared using model averaging with Corrected Akaike Information Criteria (AICC) as model selection Criterion. External validation and mean squared error of prediction (MSE(P)) are used to determine the best imputation method. Gateshead Millennium Study (GMS) data on children weight will illustrate the comparison between EM Algorithm and Multiple imputation. The results show that Multiple imputation performs slightly better compared to EM Algorithm.
Implementing PPM framework to customer’s switching behaviour in dual-channel service supply chain

Dual-Channel Supply Chain (in short DCSC) is a business structure that integrates offline channel and online channels in distributing their products. In DCSC, a consumer may switch easily from one channel to another which can make a conflict between both channels because they have the same potential consumer. This study aims to examine the factors that cause consumers to switch shopping channels in the DCSC structure. Push-Pull-Mooring (PPM) is applied to investigate consumer channel switching behaviour that is implemented in a service company. Push variable includes information searching behaviour, perceived service quality, satisfaction, and perceived price. The pull variable includes switching cost, attitude toward switching, subjective norm. Additionally, mooring variable comprises of alternative attractiveness and responsiveness. The findings indicate that all three variables have a significant effect on switching intention. However, neither of mooring factor on the relationship between push and switching intention nor between pull and switching intention show moderating effects. Partial Least Square-Structural Equation Modelling (PLS-SEM) is applied to analyse survey data from 250 respondents. The result of the interrelationship among variables could be potentially applied in determining operational and tactical strategies to anticipate the volatilities in consumer behaviours.
A Novel Evaluation Method of Transmission Grid Performance in Power Spot Market

Power spot market reform has been the focus of China’s electric power industry restructuring initiative in recent years. The pace of introducing power spot markets in a number of provinces are now being accelerated. In power spot market, the transaction clearing results are closely coupled with power grid dispatching operations, and the power grid structure could deeply affect market members’ earnings. Therefore, there is an urgent need for a method for evaluating transmission power grid performance in the context of power spot market. To this end, this paper presents a novel method for evaluating the operation efficiency of a transmission grid based on locational marginal prices (LMP). Firstly, the main characteristics of power spot market is introduced and the influence mechanism of transmission power grid on the market clearing outcomes is analyzed. The market operation efficiency of the transmission grid is calculated by comparing the market clearing results with or without considering the operational constraints of the transmission grid. Besides, an efficient computing method based on reinforcement learning is proposed to calculate the index. Finally, a case study based on the IEEE-33 bus system shows that this method can quantify the influence of transmission grid operational constraints on the transaction clearing results in spot market. The transmission grid performance index could be useful both in power market trading information disclosure and as a reference index for transmission expansion planning.