Reviewerly/exHarmony huggingface dataset:
Improving the Fitness Function of an Evolutionary Suspense Generator Through Sentiment Analysis

The perception of suspense in stories is affected not only by general literary aspects like narrative structure and linguistic features, but also by anticipation and evocation of feelings like aversion, disgust or empathy. As such, it is possible to alter the feeling of suspense by modifying components of a story that convey these feelings to the audience. Based on a previous straightforward model of suspense adaptation, this paper describes the design, implementation and evaluation of a computational system that adapts narrative scenes for conveying a specific user-defined amount of suspense. The system is designed to address the impact of different types of emotional components on the reader. The relative weighted suspense of these components is computed with a regression model based on a sentiment analysis tool, and used as a fitness function in an evolutionary algorithm. This new function is able to identify the different weights on the prediction of suspense in aspects like outcome, decorative elements, or threat’s appearance. The results indicate that this approach represents a significant improvement over the previous existing approach.
Securing Telecare Medical Information System with Blockchain Technology

Electronic Healthcare services are nowadays very popular for providing low-cost medical services. Security is a major challenge in this area, and Blockchain technology can help us to provide a high-level of security of sensitive health records. In this paper, the basic concept of Blockchain Technology is discussed first and after that we have discussed the evolution of Blockchain: Blockchain 1.0 (Bitcoin, Algorand), Blockchain 2.0 (Ethereum) and Blockchain 3.0 (Hyperledger Fabric). In this paper, a system has been designed for maintaining healthcare records involving different actors: patients, doctors, insurance company, pharmacy and many more so that patients, doctors, researcher, medical institutes, hospital, etc. get benefited from the previously saved medical history of patients without breaching into their privacy and maintaining availability, confidentiality and integrity.
Exploring Data Correlation between Feature Pairs for Generating Constraint-based Adversarial Examples

Adversarial example (AE), an input that is modified slightly to cause a machine learning system to produce erroneous outputs, has seen significant studies recently. Unfortunately, the fine data perturbation of AE ignores to keep potential data correlations between feature pairs. Thus, such AE will be easily filtered by configuring data correlations as basic filtering rules. In this paper, avoiding not to be filtered as well as causing false classification, an advanced robust AE generation attack is proposed. We first define four basic data correlations called strict linear constraint, approximate linear constraint, addition boundary constraint and zero multiplication constraint. Then, based on embedding multiple data correlations into one constraint matrix from the Pearson analysis, our approach can enable a Hadamard product of the constraint matrix and the sign of gradient matrix to craft perturbations, keeping consistent data correlations. Experimental results on intrusion detection system (IDS) indicate: 1) Nearly all AEs from original IFGSM are invalid by filtering according to basic data correlations; 2) In our method, AEs against a targeted DNN-based classifier can achieve an attack success rate of 99%, with transfer attack ability of 94% average success rate to attack other different mainstream classifiers.
Research on Library Digital Resource Evaluation System Based on Computer Multimedia Aid

With the continuous increase in the total amount of library digital resource services, issues such as digital resources and performance evaluation have gradually attracted the attention of libraries. Traditionally, digital resource providers provide statistical data. However, due to benefits, the accuracy and authenticity of the data are open to question. Taking a particular university library as an example, the paper analyzes the information log of users accessing digital resources to obtain user information needs. It uses big data mining technology to extract user information needs and preferences, forming a trend map of user needs and digital resources matching. The study found that the system can provide an auxiliary decision-making basis for subsequent purchases, provide data support for the evaluation of digital resource management in colleges and universities, and provide a scientific management model for the digital resource construction service system close to user experience.
Analysis and Comparison of Three Classical Color Image Interpolation Algorithms

Abstract With the development of society, the progress of science and technology, the level of computer has been improved. Digital imaging technology has also achieved unprecedented development. The most commonly used digital cameras are Charge-coupled device (CCD) and Complementary Metal Oxide Semiconductor (CMOS) image sensors. Bayer filters are often used to help sensors capture color information. However, only one component can be achieved per pixel. Full-color images must be obtained by color interpolation. Therefore, the color interpolation algorithm has attracted the attention of the academic community. In this paper, three classical color interpolation algorithms- Bilinear interpolation algorithm, COK Algorithm, Gradient Interpolation Algorithm- are compared and studied. The Optimal Algorithm is selected by comparing false color, noise and color moire fringe. After comparison, it is found that the three basic algorithms will have color moire fringes appear. But the gradient interpolation algorithm has the least moire fringe and noise. COK Algorithm, second of all. But the COK Algorithm has color overflows.
An Advanced Weighted Round Robin Scheduling Algorithm

Nowadays, Cloud infrastructure products are increasingly popular and the issue of Job Scheduling has become a hot research area. Job Scheduling in the Cloud Computing paradigm is a problem more difficult than the one in a conventional distributed scheme. Moreover, Job Scheduling is known to be a NP-Hard problem and as a result becomes intractable as the scale of the corresponding instance becomes larger. In this article the probabilistic Advanced Weighted Round Robin (AWRR) algorithm is introduced. Results show that the proposed algorithm can reduce both Mean Response Time and Utilization.
Survey of the Image Recognition Based on Deep Learning Network for Autonomous Driving Car

Recently, autonomous driving car is a hot area of research and the image recognition technology is one of the key technologies for autonomous driving cars to drive safely on the road. With the development of times and technology, deep learning has been widely applied in image recognition technology, and it plays an important role in this field. In this paper, the development of image recognition technology in autonomous driving cars and deep learning are introduced. Different models of deep learning networks, such as Deep Neural Network (DNN), Recurrent Neural Network (RNN) and Convolutional Neural Network (CNN) are also analyzed. In addition, the theoretical basis of deep learning in image recognition are summarized and the applications of deep learning in image recognition are carried out. Finally, the paper will look into the future development of autonomous vehicles and the future of image recognition technology. It is concluded that, image recognition system, based on deep learning network has been widely used in autonomous driving cars at present, and it has a prospective future for further development.
DOSing Distributed Ledger Technology: IOTA

With advancements in connected technology, the number of ambitious applications involving Internet of Things (IoT) are drastically growing.This increases concerns related to security, scalability, and interoperability of IoT.As the network of connected devices grows, decentralized technologies become inevitable.Within this trend towards decentralization, distributed ledger technology (for instance IOTA) will be a significant driving force.IOTA is an innovative distributed ledger technology targeted towards low power devices, where energy efficiency is a high priority.Public research regarding security threats against IOTA especially denial-of-service (DoS) is essentially non-existent.In this paper we focus on exploring a DoS attack against IOTA.The proposed attack methodology takes advantage of the lack of fees along with the ability to transfer minuscule amounts.By sending many conflicting transactions as it results in a high number of re-attachments.The high number of re-attachments threatens IOTA's suitability for the IoT sphere.The implications of such attack, as well as the future of this issue in terms of the planned removal of the centralized coordinator are discussed.
Incentive Analysis of Bitcoin-NG, Revisited

Bitcoin-NG is among the first scalable blockchain protocols by decoupling blockchain operation into two planes: leader election and transaction serialization. Its decoupling idea has inspired a new generation of blockchain protocols. However, the existing incentive analysis of Bitcoin-NG has several limitations. First, the impact of network capacity is ignored. Second, an integrated incentive analysis that jointly considers both key blocks and microblocks is still missing. In this paper, we aim to address these two limitations. First, we propose a new incentive analysis that takes the network capacity into account, showing that Bitcoin-NG can still maintain incentive compatibility against the microblock mining attack even under limited network capacity. Second, we leverage a Markov decision process to jointly analyze the incentive of both key blocks and microblocks, showing that the selfish mining revenue of Bitcoin-NG is a little higher than that in Bitcoin only when the selfish miner controls more than 35% of the mining power. We hope that our in-depth incentive analysis for Bitcoin-NG can shed some light on the mechanism design and incentive analysis of nextgeneration blockchain protocols.
Micro-expression recognition based on motion detection method

Abstract Micro-expressions are emotional representations that occur spontaneously and cannot be controlled by humans. The micro-expression movements are temporary with fast duration and have subtle movements with little intensity. This is difficult to detect with the human eye. Previous studies have shown that micro-expression movements occur in several areas of the face. This study aims to determine the subtle movements in several areas of the face using the motion detection method. We compared the performance of two motion detection methods: the optical flow method and the Block Matching Algorithm (BMA) method. The optical flow method uses the Kanade-Lucas Tomasi (KLT) method and the BMA method uses the Phase Only Correlation (POC) algorithm. Observations were carried out based on region, where the face area was divided into several observation areas: eyebrows, eyes and mouth. Both methods perform motion detection between frames. The KLT method tracks the movement of the observation points on the frame movement. Meanwhile, the POC method matches the blocks between frames. If the two blocks are the same, no motion vector is generated. However, if the two blocks are different, it is assumed that there is a translational motion and a motion vector is generated. Experiments were conducted using a dataset from CASME II with emotional classes of disgust, happiness, surprise, and sadness. The classification accuracy of the POC method is 94% higher than the KLT method of 84.8% which uses the SVM classification.
A Bottom-Up Approach for Moroccan Legal Ontology Learning from Arabic Texts

Ontologies constitute an exciting model for representing a domain of interest, since they enable information-sharing and reuse. Existing inference machines can also use them to reason about various contexts. However, ontology construction is a time-consuming and challenging task. The ontology learning field answers this problem by providing automatic or semi-automatic support to extract knowledge from various sources, such as databases and structured and unstructured documents. This paper reviews the ontology learning process from unstructured text and proposes a bottom-up approach to building legal domain-specific ontology from Arabic texts. In this work, the learning process is based on Natural Language Processing (NLP) techniques and includes three main tasks: corpus study, term acquisition, and conceptualization. Corpus study enriches the original corpus with valuable linguistic information. Term acquisition selects tagged lemmas sequences as potential term candidates, and conceptualization drives concepts and their relationships from the extracted terms. We used the NooJ platform to implement the required linguistic resources for each task. Further, we developed a Java module to enrich the ontology vocabulary from the Arabic WordNet (AWN) project.The obtained results were essential but incomplete. The legal expert revised them manually, and then they were used to refine and expand a domain ontology for a Moroccan Legal Information Retrieval System (LIRS).
What We Have Here Is Failure to Validate: Summer of LangSec

This article reviews several exploits of input-handling vulnerabilities and categorizes these vulnerabilities based on their root causes. We describe the language-theoretic security paradigm and discuss how it can be used to tackle these categories of vulnerabilities.
DDES: A Distribution-Based Dynamic Ensemble Selection Framework

Dynamic Ensemble Selection (DES) is a special type of ensemble modeling that selects different subsets of base classifiers for different test sample cases.In this process, multiple base classifiers are compared in terms of their competence as an ensemble to the best possible prediction for a given test sample case.Traditional DES methods rely on the Euclidean distance-based k-Nearest Neighbor (kNN) algorithm to identify the most relevant reference data points in such a way that the base classifiers correctly classifying them can be considered for an optimal ensemble.However, this approach is sensitive to the local structure of the data and the presence of noisy or irrelevant attributes.This study proposes a novel distribution-based DES (DDES) framework that takes the data structure into consideration when selecting reference data points.The experimental results for 30 classification problems indicate that the proposed approach yielded the best accuracy among the competing DES methods.Additionally, we discuss the correlation between the data complexity and the improvement in classification performance.
I've Got All My Readers With Me

Reading is not simply a solitary activity. It occurs in a social context, which influences both a reader's choices about what to read, and their understanding about what they read. Digitally, direct personal communication and online discussions are two examples of social reading activity. However, our understanding of social reading is still young: we know some elements of the activity, but we lack a comprehensive picture of its behavioural elements and how they interact. This study aims to bridge that existing gap.
Designing a Text-based CAPTCHA Breaker and Solver by using Deep Learning Techniques

Text-based CAPTCHAs are most commonly used by various websites to distinguish between humans and computers. It is used as a security measure. This work consists of a dynamic approach that is proposed to predict Text-based CAPTCHAs that challenges the supposition that they cannot be solved by computers. Three types of CAPTCHAs namely Rotated, Noisy Arc, Complicated Background have been taken. The CAPTCHAs are pre-processed based on their type. Different pre-processing techniques like Erosion, Dilation, Binarization are used to remove the noise from the CAPTCHA. The pre-processed CAPTCHAs are then fed to the Convolutional neural network (CNN) which generates a feature vector. This feature vector is then passed to the long short term memory (LSTM) which generates a sequence of characters. This sequence is displayed as outcome to the user. The dataset for Rotated, Noisy Arc, Complicated Background CAPTCHAs consisted of 9,955, 1,070 and 1,000 images respectively. The model was also tested for CAPTCHAs involving a combination of different resistance mechanisms. The model was able to predict Rotated CAPTCHAs with an accuracy of 85.97%, Noisy Arc CAPTCHAs with an accuracy of 84.52% and Complicated BackgroundCAPTCHAswithanaccuracyof82.91 %.
Non-parallel Sequence-to-Sequence Voice Conversion for Arbitrary Speakers

Voice conversion (VC) aims to modify the speaker's tone while preserving the linguistic information. Recent works show that voice conversion has made great progress on non-parallel data by introducing phonetic posteriorgrams (PPGs). However, once the prosody of source and target speaker differ significantly, it causes noticeable quality degradation of the converted speech. To alleviate the impact of the prosody of the source speaker, we propose a sequence-to-sequence voice conversion (Seq2SeqVC) method, which utilizes connectionist temporal classification PPGs (CTC-PPGs) as inputs and models the non-linear length mapping between CTC-PPGs and frame-level acoustic features. CTC-PPGs are extracted by the CTC based automatic speech recognition (CTC-ASR) model and used to replace time-aligned PPGs. The blank token is introduced in CTC-ASR outputs to identify fewer information frames and get around consecutive repeating characters. After removing blank tokens, the left CTC-PPGs only contain linguistic information, and the phone duration information of the source speech is removed. Thus, phone durations of the converted speech are more faithful to the target speaker, which means higher similarity to the target and less interference from different source speakers. Experimental results show our Seq2Seq-VC model achieves higher scores in similarity and naturalness tests than the baseline method. What's more, we expand our seq2seqVC approach to voice conversion towards arbitrary speakers with limited data. The experimental results demonstrate that our Seq2Seq-VC model can transfer to a new speaker using 100 utterances (about 5 minutes).
A Comparative Study of Deep Neural Network Models on Multi-Label Text Classification in Finance

Multi-Label Text Classification (MLTC) is a well-known NLP task that allows the classification of texts into multiple categories indicating their most relevant domains. However, training model tasks on texts from web user deal with redundancy or ambiguity of linguistic information. In this work, we propose a comparative study about different neural network models for a multi-label text categorisation task in finance domain. Our main contribution consists of presenting a new annotated dataset that contains <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">~</sup> 26k posts from users associated to finance categories. To build that dataset, we defined 10 specific-domain categories that cover financial texts. To serve as a baseline, we present a comparative study analysing both the performance and training time of different learning models for the task of multilabel text categorisation on the new dataset. The results show that transformer-based language models outperformed RNN-based neural networks in all scenarios in terms of precision. However, transformers took much more time than RNN models to train an epoch model.
Revisiting the memory capacity in reservoir computing of directed acyclic network

Reservoir computing (RC) is an attractive area of research by virtue of its potential for hardware implementation and low training cost. An intriguing research direction in this field is to interpret the underlying dynamics of an RC model by analyzing its short-term memory property, which can be quantified by the global index: memory capacity (MC). In this paper, the global MC of the RC whose reservoir network is specified as a directed acyclic network (DAN) is examined, and first we give that its global MC is theoretically bounded by the length of the longest path of the reservoir DAN. Since the global MC is technically influenced by the model hyperparameters, the dependency of the MC on the hyperparameters of this RC is then explored in detail. In the further study, we employ the improved conventional network embedding method (i.e., struc2vec) to mine the underlying memory community in the reservoir DAN, which can be regarded as the cluster of reservoir nodes with the same memory profile. Experimental results demonstrate that such a memory community structure can provide a concrete interpretation of the global MC of this RC. Finally, the clustered RC is proposed by exploiting the detected memory community structure of DAN, where its prediction performance is verified to be enhanced with lower training cost compared with other RC models on several chaotic time series benchmarks.
Object Tracking in Video Sequence based on Kalman filter

Object tracking has been a hot topic in the area of computer vision. In this paper, a new video moving object tracking method based on Kalman filter is proposed. In initialization, a moving object selected by the user is segmented and the dominant color is extracted from the segmented target. In tracking step, a motion model is constructed to set the system model of adaptive Kalman filter firstly. Then, the dominant color of the moving object in HSI color space will be used as feature to detect the moving object in the consecutive video frames. The detected result is propagated as the measurement of adaptive Kalman filter and the estimate parameters of adaptive Kalman filter are adjusted by occlusion ratio adaptively. Experiments demonstrate that the proposed method is effective video for object tracking applications.
Machine Learning-Based Malicious X.509 Certificates’ Detection

X.509 certificates play an important role in encrypting the transmission of data on both sides under HTTPS. With the popularization of X.509 certificates, more and more criminals leverage certificates to prevent their communications from being exposed by malicious traffic analysis tools. Phishing sites and malware are good examples. Those X.509 certificates found in phishing sites or malware are called malicious X.509 certificates. This paper applies different machine learning models, including classical machine learning models, ensemble learning models, and deep learning models, to distinguish between malicious certificates and benign certificates with Verification for Extraction (VFE). The VFE is a system we design and implement for obtaining plentiful characteristics of certificates. The result shows that ensemble learning models are the most stable and efficient models with an average accuracy of 95.9%, which outperforms many previous works. In addition, we obtain an SVM-based detection model with an accuracy of 98.2%, which is the highest accuracy. The outcome indicates the VFE is capable of capturing essential and crucial characteristics of malicious X.509 certificates.