Reviewerly/exHarmony huggingface dataset:
Supervised Learning for Negation Scope Detection n Arabic Texts

The research in detecting the negation in Arabic is limited due to the unavailability of Arabic corpora targeting this phenomenon. The negation detection affects a set of subfields in the Arabic Natural Language Processing (ANLP), including sentiment analysis and medical information retrieval. Therefore, a corpus is manually annotated with negation for targeting this deficiency in the Modern Standard Arabic (MSA) and Classical Arabic (CA) texts. This corpus is collected from various sources, including King Saud University Corpus of Classical Arabic (KSUCCA) and Wikipedia. It includes texts from various topics, like religion, sports, science, biography, health, technology, education, and history. In addition, we propose a supervised-based learning system for the problem of negation scope detection in Arabic texts. Our system depends on Word2Vec and FastText word embeddings with two different classifiers: the Bidirectional Long Short-Term Memory (BiLSTM) and the Support Vector Machined (SVM) as a baseline system. The results show that one of the FastText-BiLSTM based models achieved a classification accuracy of 93% with F1 score of 89%. The fact that the results of the supervised learning are encouraging further proves the point that the treatment of the negation phenomenon is tractable.
Analyses of different methods of writing using SVM classifier

A person's handwriting reveals his personality and emotion, feeling like fear, happiness, honest or depression. The computer may find it difficult to detect a human motion because it is unaware of human emotion. To predict human emotions, various methods like facial recognition, eye tracking and analysis of handwriting are used. The scientific name of handwriting analysis is graphology; each person writing style is unique when compared to others. Analyzing each person handwriting in the offline method is difficult, costly and takes time. So, this paper is aimed at analyzing the different types of handwriting and extraction of various features. The features extracted will be trained for further classification. In this paper, a machine learning technique support vector machine is being implemented and discussed. The average time taken for writing a sentence was 17 seconds. This proposed system has accuracy varying from 92.33% to 62.5% while predicting the signal.
ESTN: Exacter Spatiotemporal Networks for Violent Action Recognition

To solve the problems of low efficiency of manual violence recognition and low accuracy of violence recognition method combined with deep learning, this paper proposes an exacter spatiotemporal network for violent action recognition (ESTN). Firstly, the video samples are segmented and pruned by data preprocessing technology and data enhancement technology, and the spatiotemporal features of the preprocessed samples are extracted and fused by a two-dimensional convolutional neural network and three-dimensional convolutional neural network. The two-dimensional network mainly extracts the deep spatial features of the video, and the two branches of the three-dimensional network extract the scene spatial features and temporal features respectively. Finally, the recognition results of the segmented samples are fused to get the final result. Our approach has achieved more than 94% accuracy on HockeyFight and more than 70% accuracy on RWF-2000.
A Textile Image Classification based on Texture and Shape Features

In the last few decades, with the progress of image processing textile designer used a most appropriate feature extraction method for both pattern design and analysis of fabric materials. In this proposed method, we have used an efficient features extraction method. Firstly, image is resized and converting to a gray scale level. Then features are extracted on the basis of shape and texture feature extraction methods. Discrete Wavelet Transform (DWT) and Local binary pattern (LBP) are used to find the texture features and for the shape features Invariant moments (IM) is used. Canny edge detection method is used to remove the noise to smooth the image. To apply a robust system, we used PCA to reduce the dimension of feature descriptors. In experimental results, we used 300 batik images for a same and different Pattern. Support Vector Machine (SVM) method is used for a Classification Method. The overall result shows that a combination of multi features gives efficient results as compared to single features extraction method. The accuracy of our system is about 97.6 % after applying the PCA algorithm.
A Web-Based Monitoring System of Network Security Functions in Blockchain-Based Cloud Security Systems

To prevent security attacks targeting enterprises and services, security managers use Network Security Functions (NSFs) from various manufacturers. For the unified management of these used NSFs, the Interface to Network Security Functions (I2NSF) Working Group of Internet Engineering Task Force (IETF) provides a framework with standard interfaces and data models to easily conFigure the NSFs. The user can use this framework to easily manipulate and manage NSFs configuration without any security expertise. One of the proposed interfaces is the Monitoring Interface that can be used to monitor the performance and status of the NSFs. We implemented a blockchain network using Hyperledger Fabric to ensure the integrity of the monitoring data of the NSFs delivered through the monitoring interface. In addition, we implemented a web application that can visualize the monitoring data of NSFs in real-time by implementing REST API using the JavaScript-based Hyperledger SDK.
Investigation of Anomaly Detection Method Using a Variable of System Health Status

In spacecraft systems, it is important to quickly detect and deal with anomalies. However, the increasing complexity of spacecraft systems makes it difficult to avoid failures using previous anomaly detection methods. In recent years, anomaly detection methods based on machine learning have been studied. But it is difficult for a spacecraft to detect anomalies autonomously in orbit. In this study, we propose an anomaly detection method that defines and monitors a single variable representing the health status of a spacecraft system, instead of monitoring a huge amount of telemetry data. We have confirmed that the single variable can represent the health status of a spacecraft system and can be applied to the anomaly detection method.
Research on the construction of College English mobile translation practice platform based on Corpus

With the rapid development of mobile Internet technology, the traditional translation practice platform of College English (CET) has some limitations, such as poor real-time interaction, insufficient monitoring and intervention, and weak personalized design, which can not meet the needs of translation practice teaching. More and more translation software and corpus technology represented by Trados are used in the field of translation at home and abroad, but most domestic scholars have not applied corpus to the development and construction of College English websites, and there are few research results in this field. Through analysis, this paper builds a mobile translation practice platform based on corpus, builds a corpus platform, and supports mobile access, so as to provide convenience for ordinary English learners to search corpus and assist English autonomous learning. This paper points out the construction method of the practice platform that meets the needs of CET translation teaching, with a view to providing reference for CET translation practice teaching.
Study on Terrain Matching Method Based on Cater Detection

The Chang'e-4 lunar probe carrying the Moon Rabbit 2 lunar rover was lifted off by the Long March 3B launch vehicle and landed on the back side of the Moon after 26 days of space flight, becoming the world's first probe to land on the back side of the Moon. In this paper, a terrain matching method based on crater detection is designed using the images taken by Chang'e-4 on the back side of the Moon. This method firstly establishes a local land marker library based on the existing data and the intended landing area of the spacecraft, then identifies and labels the craters in the images taken by the landing camera, and finally matches the craters in the captured images with the data in the land marker library to obtain the position information of the spacecraft during the landing process.
Online Consumer Identification Method for Live Streaming Using Artificial Intelligence

This paper studies the key technology of ID card recognition for online consumer identification method, which involves image processing, pattern recognition, neural network, and other fields, and based on the key technology of the system software programming, testing and demonstrating the results of identification. The processing and identification method used in this paper is relatively simple, easy to implement, and has a good identification effect, provides an effective id card automatic identification system scheme. The method proposed in this paper can be used for the fast identification of consumers on live broadcast platforms, for subsequent recommendation classification tasks, and intelligent recommendation algorithms.
BlockCACert – A Blockchain-Based Novel Concept for Automatic Deployment of X.509 Digital Certificates

Digital certificates (also known as X509 public-key certificates) are used to prove the identity of a client or of a server to the communicating partner and to establish an encrypted channel for secure communication. The most prominent use of digital certificates is found in the HTTPS protocol, for securing websites and clients by means of digital signatures and public-key encryption. Because digital certificates are signed by Certificate Authorities that supposedly everyone trusts, it is implicitly said that these certificates provide some degree of trustworthiness. But history shows us that Certificate Authorities can also make mistakes, issue false certificates or be compromised in some form or another. Also, currently there are multiple types of digital certificates that can be issued: DV, OV and EV, which cost differently based on the grade of trust. We believe that real trust should not be quantifiable in degrees of trust: we either trust a certificate or we don’t. We propose BlockCACert, a blockchain-based solution for CAs for issuing all types of X509 certificates using the ring signatures concept, which fixes the problem of general trust and multiple-level trust by using a global procedure for issuing all types of digital public-key certificates.
Analysis of Machine Learning, Deep Learning, and Artificial Neural Network Approaches for Breast Cancer Classification

Breast cancer is one of the most common causes of death worldwide among women, with good survival rates if detected early. In our work, we compared supervised, semi- supervised and unsupervised learning on the biomedical dataset, Wisconsin Breast Cancer Dataset, to establish the model with the best performance and hence apply for computer aided diagnosis. The metrics used for the same includes performance of the network as well as the ease of implementation, As a result, we hope to close the gap between technology innovation and its implementation in healthcare.
Parameterized Complexity of Dynamic Belief Updates: A Complete Map

Abstract Dynamic Belief Update is a model checking problem in Dynamic Epistemic Logic concerning the effect of applying a number of epistemic actions on an initial epistemic model. It can also be considered as a plan verification problem in epistemic planning. The problem is known to be PSPACE-hard. To better understand the source of complexity of the problem, previous research has investigated the complexity of 128 parameterized versions of the problem with parameters such as number of agents and size of epistemic actions. The complexity of many parameter combinations has been determined, but previous research left 14 parameter combinations open. In this paper, we solve all of these open problems. Most of the parameter combinations turns out to be fixed-parameter intractable, except for 2 that are fixed-parameter tractable.
Scams Committed Solely Through Computer Technology, Internet and E-Mail

The main aim of this chapter is to describe how information technology is, in combination with social engineering, misused to lure a big sum of money from internet users just via e-mail, how the money transfer is performed, who is a victim, who is an attacker, what are the possibilities to investigate such a case, how to protect yourself, and what developments can be expected in this area of ​​cybercrime.
PA-FaSTrack: Planner-Aware Real-Time Guaranteed Safe Planning

Guaranteed safe online trajectory planning is becoming an increasingly important topic of robotic research, due to the need to react quickly in unknown environments. However, as a result of modelling mismatch, some error during trajectory tracking is inevitable. In this paper, we present Planner-Aware FaSTrack, or PA-FaSTrack, which provides guaranteed Tracking Error Bounds (TEBs) by solving a Hamilton-Jacobi (HJ) variational inequality in the tracking error space. PA-FaSTrack improves upon the state-of-the-art method, FaSTrack [1], by accounting for motion primitives implied by the planning algorithm in the problem formulation. Our method provides a sequence of TEBs, with each TEB corresponding to a segment of the planned path. We also propose necessary modifications to real time tree based planning algorithms in order to make them compatible with the provided TEB sequence. By integrating planning and tracking more closely together, we greatly decrease the degree of conservatism compared to the original FaSTrack, allowing the autonomous system to navigate safely through much narrower spaces. We demonstrate our method using two representative dynamical systems.
Dependency-Based Classification With Multimodal Data Using Regular Vine Copulas

In internet-of-things and big data applications, multiple sensors are required to acquire information about the same event. The dependencies among these modalities have to be leveraged to gain new perspectives and to improve the accuracy of the underlying performance measures. Retrieving this dependency information from the multimodal data helps in dimensionality reduction, and performance improvements. However, capturing these dependencies can be a major challenge due to a complex heterogeneity profile in the data. In this paper, we develop a dependency-based model for classification with multimodal data representation and information fusion using regular vine (R-Vine) copula theory. The efficacy of the developed models is tested on STISEN and TMD datasets for human activity recognition and transport mode detection applications. Further, we show that the F1 scores obtained for our model-98.50 percent and 98.649 percent for STISEN and TMD respectively - are better compared to existing dependency-blind architectures. These dependency-blind architectures use feature extraction using a deep neural network followed by (a) no information fusion, and (b) R-Vine-based fusion.
Measuring the academic influence of geological research institutions based on international awards

Aiming at the problem of insufficient consideration of the dimensions of scientific research personnel in the evaluation of academic influence of existing scientific research institutions, the data of international award winners is used as an index to evaluate the academic influence of scientific research institutions, and an empirical analysis of global geology from the dimensions of data collection, indicator analysis and model construction. Based on the 23 important international awards set by 18 international academic organizations of geology, the information of 229 winners was collected for empirical analysis. The research results show that the top five countries in terms of the number of international award winners are the United States, the United Kingdom, Canada, Australia, and Italy. 229 winners were distributed among 140 institutions. The institution with the largest number of awards was the U.S. Geological Survey, with a total of 12 awardees.
Big Data and Data-Related Abuses of Market Power

This chapter analyzes Big Data and Big Data abuses of market power. The chapter provides an overview of developments under both EU and US case law relevant to such abuses as well as some of the economic underpinnings of such an analysis. While there are not many cases specific to this interface, a number of related areas of law both in the merger and conduct context provide a template for how competition authorities, courts, and academics should frame such issues.
MSCU: Accelerating CNN Inference with Multiple Sizes of Compute Unit on FPGAs

The FPGA-based accelerators have shown great potential in improving the performance of CNN inference. However, the existing FPGA-based approaches suffer from a low Compute Unit (CU) efficiency due to a large amount of invalid computation, thus leading to a great performance degradation. In this paper, we find that no single CU can perform the best across all the convolutional layers (Conv-layers). To this end, we propose multiple sizes of compute unit (MSCU), an approach to accelerating CNN inference on FPGAs. The key idea of MSCU is to select the best combination of CUs via dynamic programming scheduling for each Conv-layer, and then assembles each Conv-layer combination into a computing solution of the given CNN to deploy on FPGAs. The experimental results show that MSCU can achieve 3.36E-03 GOPs/Slice on Xilinx Zynq ZU3EG for performance density which is 4.29x higher than other approaches.
Task Pre-migration Strategy in Mobile Edge Computing Environment

In the mobile edge computing environment, the requirements for low latency are relatively urgent, especially for high data volume computational migration, such as driverless system, real-time monitoring. When the user leaves the edge server coverage, data interruption occurs, and the user needs to re-upload the data to the server for calculation, which leads to extended time of the edge server service and increased power consumption of the mobile device. To solve this problem, this paper proposes a data pre-migration mechanism between servers in the mobile edge computing environment, which pre-puts user-related data to the edge server in the user’s moving direction. When the task is interrupted, the user does not need to re-upload data. The seamless connection of tasks can effectively reduce server service time and reduce device power consumption.
On Decoding Binary Quasi-Reversible BCH Codes

For the recently developed quasi-reversible BCH codes with long lengths and high error-correcting capability, this paper is aimed at proposing a new and faster decoding procedure. It consists of four steps: 1) compute the consecutive syndromes; 2) calculate the syndrome functions by the forward and backward recursions; 3) solve a linear subsystem together with one matrix multiplication in order to find an error-locator polynomial; 4) determine the errors from the obtained polynomial by using the root-finding algorithm. This procedure, especially in Steps 2 and 3, differs greatly from the conventional procedures, which determine an error-locator polynomial directly from solving a linear system with the aid of the consecutive syndromes. The key idea behind this decoding technique is that the computational complexity of such a small subsystem instead of an originally large linear system can be significantly reduced, although there are additional forward and backward syndrome calculations with low complexity increasing. Finally, the illustrative examples and numerical simulations can be helpful to demonstrate the accuracy and efficacy of the presented decoding technique at different error-correcting capabilities.
Federated Learning with Incrementally Aggregated Gradients

We consider the standard federated learning (FL) framework where a set of clients coordinate with a central server to train a statistical model. In a single-machine centralized setting, it is well-known that for smooth and strongly convex finite-sum optimization problems, one can design algorithms that guarantee exact linear convergence to the global minimum without computing full (batch) gradients at every iteration. Despite its popularity, an analog of the above result does not exist in FL. Motivated by this gap, we consider a setting where the local loss function of each client can be expressed as a finite sum of smooth component functions. For this setting, we propose a novel computationally-efficient FL algorithm called FedTrack that rests on two key ideas: (i) using the most recently communicated versions of the clients’ gradients in the local update rule, and (ii) incrementally aggregating gradients of the component functions of each client. While the first idea serves to overcome the effect of heterogeneity across the clients’ local loss functions, the second helps to significantly reduce the overall number of gradient computations. For both strongly convex and non-convex local loss functions, we prove that the convergence guarantees of FedTrack match their centralized counterparts (up to constants). In particular, for the strongly convex setting, we show that FedTrack guarantees exact linear convergence to the global minimum deterministically.
Question-Answer Generation for Data Augmentation

Question generation is a popular issue following the rise of deep neural networks; however, previous works either assumed that answers for question generation were known or considered answer extraction and question generation as two separated tasks. In this work, we regard the overall task of question-answer generation (QAG) a combination of the two so that the QAG model can generate questions without labeled answer, and then propose a new QAG approach based on the attention technique. Our approach avoids the high cost of labeling answers and the time for training another model to find the answers for the questions generated. The results show that a fine-tuned question-answer generation model can obtain better performance and it provides a good data augmentation method for automated question-answering. We also find that the generative adversarial network improves the performance of the question-answer generation model on data augmentation.
A research model to explain acceptance of speech therapy devices by senior aphasia patients

In addition to health-related impairments of age such as poor health, cognitive disorders, and frailty, aging people also face the risk of encountering situations that can lead to social exclusion and have significant negative consequences for their independence and quality of life. The home healthcare initiative intends to reduce healthcare costs, enhance healthcare quality, and increase patient independence. Information technologies like healthcare smart-home-solutions become a significant role in this effort. For these to be successful and ensure the users’ acceptance, the focus must be on them. Although particularly older adults could benefit from eHealth, they often feel unable to use the technologies. Drawing on the technology acceptance model and health belief model, we seek to understand if older people would use smart-home-environments for home-based therapy. This paper presents a research model on eHealth adoption in a speech therapy context.