Reviewerly/exHarmony huggingface dataset:
Line-Speed and Scalable Intrusion Detection at the Network Edge via Federated Learning

Intrusion detection through classifying incoming packets is a crucial functionality at the network edge, requiring accuracy, efficiency and scalability at the same time, introducing a great challenge. On the one hand, traditional table-based switch functions have limited capacity to identify complicated network attack behaviors. On the other hand, machine learning based methods providing high accuracy are widely used for packet classification, but they typically require packets to be forwarded to an extra host and therefore increase the network latency. To overcome these limitations, in this paper we propose an architecture with programmable data plane switches. We show that Binarized Neural Networks (BNNs) can be implemented as switch functions at the network edge classifying incoming packets at the line speed of the switches. To train BNNs in a scalable manner, we adopt a federated learning approach that keeps the communication overheads of training small even for scenarios involving many edge network domains. We next develop a prototype using the P4 language and perform evaluations. The results demonstrate that a multi-fold improvement in latency and communication overheads can be achieved compared to state-of the-art learning architectures.
The Seismic Hush of the Coronavirus

Scientists are listening for faint natural signals during the quiet of coronavirus lockdowns.
A Deep Learning Architecture for Corpus Creation for Telugu Language

Many natural languages are on the decline due to the dominance of English as the language of the World Wide Web (WWW), globalized economy, socioeconomic, and political factors. Computational Linguistics offers unprecedented opportunities for preserving and promoting natural languages. However, availability of corpora is essential for leveraging the Computational Linguistics techniques. Only a handful of languages have corpora of diverse genre while most languages are resource-poor from the perspective of the availability of machine-readable corpora. Telugu is one such language, which is the official language of two southern states in India. In this paper, we provide an overview of techniques for assessing language vitality/endangerment, describe existing resources for developing corpora for the Telugu language, discuss our approach to developing corpora, and present preliminary results.
Online Threshold Tracking in Cyber-Physical-Human Systems Based on Binary Observations

In this paper, we address the online threshold tracking problem in Cyber-Physical-Human systems (CPHS) based on binary feedback signals from users. We model the CPHS system as a linear discrete time-varying system, and consider both estimation error minimization and user experience in our cost function. Based on dynamic programming (DP), this paper proposes an optimal control which greatly reduces the rate of dissatisfaction while maintaining comparable performance in our simulation of braille display users. Besides, stability analysis is provided to guarantee that the cost is bounded in long-run problems.
Adaptive Mean Filter Technique for Removal of High Density Salt and Pepper (Impulse) Noise

In this paper, the adaptive mean filter technique for removal of high density salt and pepper also termed as impulse noise is presented. It is desired to remove high density impulse noise from the images due to hot pixels produced or generated because of current leakage in the image sensors placed inside the camera. Adaptive mean filter technique processes the images affected with noise using variable filter size that results in better removal of high density impulse noise as compared with fixed filter size. Variable filter size comparatively takes more processing time but results in better evaluation parameters such as signal to noise ratio (SNR), mean absolute error (MBE) and image enhancement factor (IEF). Various experiments were performed on images having different entropy to measure the performance of the presented filter technique. The adaptive mean filter with variable size filters effectively removes high density impulse noise up to density 0.5. The presented adaptive mean filter technique requires simple arithmetic operations that achieve faster processing with less computational overheads. The presented adaptive mean filter technique for removal of high density salt and pepper or impulse noise can be adapted in image acquisition systems.
Author Correction: Analog errors in quantum annealing: doom and hope

An amendment to this paper has been published and can be accessed via a link at the top of the paper.
Iklan Berbayar di Social Media: Sebuah Sistem Pendukung Keputusan

The growth of online economic transaction is experiencing a significant increase recently. One of the massive transactions carried out is through social media platforms. To reach more potential customers, several social media platforms offer paid ad serving services. In utilizing this service, business decision makers often need a decision support system that is currently rarely examined. This research focuses on building a decision support system on how business decision makers can carry out efficient paid advertising campaigns. Two machine learning algorithms are tested and compared in performance to get a robust algorithm to classify the types of posts that are able to reach more potential customers and have more interaction. The result shows that Random Forest is able to achieve an accuracy up to 75% which is better than Support vector machines which only reach 66% accuracy. In addition, Paid ads were found to be less relevant in reaching more potential customers and increase the number of interactions. To provide a guidance in implementing an efficient paid advertising campaign in Facebook, a guidance or decision support system is compiled based on the results of an independent variable weighting. Keyword: social media advertisement, random forest, support vector machine, data mining
Edge Network Resource Synergy for Mobile Blockchain in Smart City

Blockchain has broad application prospects in Smart City, and the technical characteristics of the blockchain itself can solve the problems of mistrust of network resource production relations and unfair distribution of revenue. However, most of the devices in Smart City are mobile devices with insufficient resources. The demand for computing power of the mining process cannot be met. For this, we introduce mobile edge computing, deploy edge servers on the edge side, and provide resources for mobile devices. We build an edge network resource allocation model for mobile blockchain to realize the effective application of blockchain technology in mobile environments. The resources required by the mining process can be obtained from neighboring resource sharing devices or edge servers. The resource allocation between adjacent devices can be modeled as a two-way auction model, and the Bayesian- Nash equilibrium is solved to determine the optimal price, while considering the trusted value of the device; the process of the mobile device acquiring resources from the edge server can be modeled as a two-stage Stackelberg game. Finally, simulation experiments show that this mechanism achieves a higher personal utility than an existing model that only considers requesting resources from an edge server.
Political Advertising Dataset: the use case of the Polish 2020 Presidential Elections

Political campaigns are full of political ads posted by candidates on social media. Political advertisements constitute a basic form of campaigning, subjected to various social requirements. We present the first publicly open dataset for detecting specific text chunks and categories of political advertising in the Polish language. It contains 1,705 human-annotated tweets tagged with nine categories, which constitute campaigning under Polish electoral law. We achieved a 0.65 inter-annotator agreement (Cohen’s kappa score). An additional annotator resolved the mismatches between the first two annotators improving the consistency and complexity of the annotation process. We used the newly created dataset to train a well established neural tagger (achieving a 70% percent points F1 score). We also present a possible direction of use cases for such datasets and models with an initial analysis of the Polish 2020 Presidential Elections on Twitter.
Representation of the Knowledge and Fuzzy Reasoning

In this paper, we have described with certain detail some theories of the fuzzy logic, and their application to the artificial intelligence like scheme of representation of the knowledge, like base of new reasoning models, and also like effective means of approaching the problem of the linguistic classification of variables. After a fleeting representation of the nature and reach of the fuzzy sets, we enter of full in their characterization and nomenclature. Subsequently we approach the problems of the fuzzy relationships, and we propose the formulation of Zadeh for the representation of knowledge of the type: If x is A, Then y is B. This allows us to define the generalized modus ponens as inferential mechanism of the fuzzy systems. Finally, it is mentioned some of the reasoning ways that can be found in the fuzzy systems.
Autosegmental Neural Nets: Should Phones and Tones be Synchronous or Asynchronous?

Phones, the segmental units of the International Phonetic Alphabet (IPA), are used for lexical distinctions in most human languages; Tones, the suprasegmental units of the IPA, are used in perhaps 70%. Many previous studies have explored cross-lingual adaptation of automatic speech recognition (ASR) phone models, but few have explored the multilingual and cross-lingual transfer of synchronization between phones and tones. In this paper, we test four Connectionist Temporal Classification (CTC)-based acoustic models, differing in the degree of synchrony they impose between phones and tones. Models are trained and tested multilingually in three languages, then adapted and tested cross-lingually in a fourth. Both synchronous and asynchronous models are effective in both multilingual and cross-lingual settings. Synchronous models achieve lower error rate in the joint phone+tone tier, but asynchronous training results in lower tone error rate.
Optimal Allocation Model of Enterprise Human Resources Based on Particle Swarm Optimization

In order to realize the optimization of enterprise human resources optimal allocation management and improve the efficiency of enterprise human resources management, an enterprise human resources optimal allocation model based on particle swarm optimization is designed, and an enterprise human resources optimal scheduling and adaptive allocation method based on particle swarm optimization is proposed. The method of large data fusion of enterprise human resources is adopted to construct a database model for optimal allocation of enterprise human resources, online analytical processing tools are used to construct a table model for optimal allocation of enterprise human resources in the form of reports, statistical regression analysis methods are adopted to perform multidimensional information fusion and feature extraction in the process of optimal allocation of enterprise human resources, particle swarm optimization evolutionary control methods are adopted to perform information retrieval and multidimensional data loading for optimal allocation of enterprise human resources, using the big data fusion method, the disturbance analysis of enterprise human resources management decision-making is carried out, and the weight analysis is carried out according to the stagnation of evolution. The non-cooperative game model of enterprise human resources management decision-making is constructed to improve the fuzzy decision-making ability of human resources system. The simulation results show that the information fusion ability of the optimized allocation of enterprise human resources using this method is better, and the optimized allocation ability of enterprise human resources is stronger. It realizes the optimized allocation and detection of enterprise human resources, and has good application value in the optimized allocation and management of enterprise human resources.
Recurrent Neural Network Assisted Equalization for FTN Signaling

In this paper, we consider the application of neural network for equalization of Faster-than-Nyquist (FTN) Signaling. First, we formulate the detection problem as a supervised regression task in machine learning framework. Then a recurrent neural network (RNN) called Bi-directional long short-term memory (Bi-LSTM) is proposed to characterize the feature of inter-symbol interference (ISI) introduced in FTN Signaling. Moreover, we describe a “mismatch SNR” strategy for building the training Dataset that can effectively help to prevent overfitting. Numerical results prove that the BER performance of the proposed neural network based detector is close to the theoretical optimal maximum likelihood sequence estimation (MLSE) when symbol rate within the Mazo Limit, and Bi-LSTM could be a more realistic scheme compare with MLSE when symbol rate exceeds the Mazo Limit.
A Competitive Deep Neural Network Approach for the ImageCLEFmed Caption 2020 Task

The aim of ImageCLEFmed Caption task is to develop a system that automatically labels radiology images with relevant medical concepts. We describe our Deep Neural Network (DNN) based approach for tackling this problem. On the challenge test set of 3,534 radiology images, our system achieves an F1 score of 0.375 and ranks high, 12th among all systems that were successfully submitted to the challenge, whereby we only rely on the provided data sources and do not use any external medical knowledge or ontologies, or pretrained models from other medical image repositories or application domains.
Exploring Weaknesses of VQA Models through Attribution Driven Insights

Deep Neural Networks have been successfully used for the task of Visual Question Answering for the past few years owing to the availability of relevant large scale datasets. However these datasets are created in artificial settings and rarely reflect the real world scenario. Recent research effectively applies these VQA models for answering visual questions for the blind. Despite achieving high accuracy these models appear to be susceptible to variation in input questions.We analyze popular VQA models through the lens of attribution (input's influence on predictions) to gain valuable insights. Further, We use these insights to craft adversarial attacks which inflict significant damage to these systems with negligible change in meaning of the input questions. We believe this will enhance development of systems more robust to the possible variations in inputs when deployed to assist the visually impaired.
Comparative Analysis of Geometric Transformation Effects for Image Annotation Using Various CNN Models

For image classification and image annotation applications, a convolutional neural network (CNN) has revealed superior accuracies and computationally efficient methods. Researchers are putting significant efforts across the globe to come up with enhanced deep network architectures. However, the latest CNN models are unable to cope with the geometric transformation of images. In the training stage, the state-of-the-art algorithms mostly make use of data augmentation techniques, that expand training samples but it leads to data overfitting. This paper focuses on how geometric transformation affects the annotation performance of various CNNs such as Alexnet, GoogleNet, ResNet50, and DenseNet201 on the Corel dataset. Comparative analysis of these deep learning models for image annotation is evaluated using the F1 score and is presented with the help of a box plot. Based on the obtained results, it is observed that the DenseNet201 model performs better than other models. It is further observed that the ResNet50 model works better for a specific class of images.
Creating Video Summary Using Speeded Up Robust Features

There has been unprecedented growth in video data in the last few years due to advances in imaging and video capturing devices. Management of huge video data is a challenging issue these days. Video summarization provides solution to this issue, in which a few representative frames of video are used instead of the whole video. This saves time for the user and also memory space. In this paper, an effective video summarization technique is proposed using preprocessing and Speeded Up Robust Feature (SURF) algorithm. In the preprocessing stage, redundant frames in the video are eliminated using adaptive local thresholding. SURF algorithm is then used to obtain key points in the candidate frames. Key point matching of adjacent frames is done to decide the key frames. Experiments have been conducted on videos from I2V dataset, open-video project dataset and videos downloaded from YouTube. Experimental results demonstrate the superiority of the proposed method against state-of-the-art approaches. Average compression ratio of 0.98 is achieved which is quite high compared to existing techniques. Also, better results are obtained in terms of precision, recall and F-measure. The video summaries produced using the proposed method match the summaries provided by human observers.
Multitask Models for Controlling the Complexity of Neural Machine Translation

We introduce a machine translation task where the output is aimed at audiences of different levels of target language proficiency. We collect a novel dataset of news articles available in English and Spanish and written for diverse reading grade levels. We leverage this dataset to train multitask sequence to sequence models that translate Spanish into English targeted at an easier reading grade level than the original Spanish. We show that multitask models outperform pipeline approaches that translate and simplify text independently.
Conceptualizing an Agile Team Mindset: Paradoxes and Mindsets in Times of Transition

Teams in large corporations undergoing a transformation towards agile methods and application of the same are often confronted with a set of paradoxical challenges. In order to deal with these paradoxical challenges and make sense of them, a suitable team mindset is needed. To conceptualize such a mindset and study its interactions with the experienced paradoxes, we have accompanied a European large telecommunications corporation undergoing a second agile transformation after having already undertaken a smaller agile transformation in the past. At three points in time over the course of almost a year, we have interviewed 27 team members, Product Owners, Scrum Masters, and associated leaders, resulting in 81 qualitative semi-structured in-depth interviews. Drawing on this data, we have conceptualized the agile team mindset and developed a framework which explains its interactions with paradoxes. Thereby, we show how experienced paradoxes can foster the agile team mindset via integrating and separating sets of mechanisms and vice versa how the agile team mindset can help dealing with the experienced paradoxes. We contribute to research on mindsets, paradoxes, and integrate agile teams as specific team type into team effectiveness research.
Geometrical analysis and entanglement measure of symmetric multiqubit states

We geometrically examine the entanglement in symmetric multiquibt states using the spin coherent states properties. We employ the Majorana representation to examine how coherent (polarized) and unpolarized states can be represented in the Bloch sphere and subsequently to quantify entanglement in multipartite systems. Entanglement can be viewed as a distance separate two points in Bloch sphere and how this notion can be extended for multipartite states in W class. This provides us with an entanglement measure for [Formula: see text]-states analogue to the notion of Concurrence.
A NEW APPROACH TO DIGITALISATION IN EDUCATION FOR THE DEVELOPMENT OF YOUNG TALENTS

Appears in: EDULEARN20 Proceedings Publication year: 2020Pages: 5637-5643ISBN: 978-84-09-17979-4ISSN: 2340-1117doi: 10.21125/edulearn.2020.1479Conference name: 12th International Conference on Education and New Learning TechnologiesDates: 6-7 July, 2020Location: Online Conference
Embedding Dimension of Polyhedral Losses

A common technique in supervised learning with discrete losses, such as 0-1 loss, is to optimize a convex surrogate loss over \reals^d, calibrated with respect to the original loss. In particular, recent work has investigated embedding the original predictions (e.g. labels) as points in \reals^d, showing an equivalence to using polyhedral surrogates. In this work, we study the notion of the embedding dimension of a given discrete loss: the minimum dimension $d$ such that an embedding exists. We characterize d-embeddability for all $d$, with a particularly tight characterization for $d=1$ (embedding into the real line), and useful necessary conditions for d>1 in the form of a quadratic feasibility program. We illustrate our results with novel lower bounds for abstain loss.
PEDAGOGICAL INNOVATION THROUGH ACTIVE LEARNING PRACTICES USING DIGITAL TOOLS - A CASE STUDY IN TOURISM DEGREES

PEDAGOGICAL INNOVATION THROUGH ACTIVE LEARNING PRACTICES USING DIGITAL TOOLS - A CASE STUDY IN TOURISM DEGREES
Bayesian Meta-Regression Model Using Heavy-Tailed Random-effects with Missing Sample Sizes for Self-thinning Meta-data

Motivated by the self-thinning meta-data, a random-effects meta-analysis model with unknown precision parameters is proposed with a truncated Poisson regression model for missing sample sizes. The random effects are assumed to follow a heavy-tailed distribution to accommodate outlying aggregate values in the response variable. The logarithm of the pseudo-marginal likelihood (LPML) is used for model comparison. In addition, in order to determine which self-thinning law is more supported by the meta-data, a measure called Plausibility Index (PI) is developed. A simulation study is conducted to examine empirical performance of the proposed methodology. Finally, the proposed model and the PI measure are applied to analyze a self-thinning meta-data set in details.
Chicken Swarm Optimization - Modifications and Application

This chapter presents a short description of the global version of the Chicken Swarm Optimization (CSO) algorithm. Binary Chicken Swarm Optimization version of the algorithm is used for solving discrete optimization problems in which the search space is discrete and the solution can be represented using a binary vector. The application of chaos theory has as the main objective the improvement of the performance of the CSO algorithm. The objective of the chaotic maps is to solve the problem of generation of values for the random variables which are used when the positions of the chickens are updated and two illustrative maps are the tent map and the logistic map. The values for the configuration parameters of Principal Component Analysis and Correlation Filter were selected after a series of experiments such that the number of selected features would be approximately equal to the number of features returned by the Binary Chicken Swarm Optimization algorithm.
INFORMATIONAL ASPECTS OF DIGITAL LITERACY

Appears in: EDULEARN20 Proceedings Publication year: 2020Pages: 3910-3916ISBN: 978-84-09-17979-4ISSN: 2340-1117doi: 10.21125/edulearn.2020.1064Conference name: 12th International Conference on Education and New Learning TechnologiesDates: 6-7 July, 2020Location: Online Conference