Reviewerly/exHarmony huggingface dataset:
Knowledge-Driven Multi-Objective Evolutionary Scheduling Algorithm for Cloud Workflows

Cloud workflow scheduling often encounters two conflicting optimization objectives of makespan and monetary cost, and is a representative multi-objective optimization problem (MOP). Its challenges mainly come from three aspects: 1) a large number of tasks in a workflow cause large-scale decision variables; 2) the two optimization objectives are of quite different scales; 3) and cloud resources are heterogeneous and elastic. So far, many studies focus on adopting multi-objective evolutionary algorithms (MOEAs) to solve the cloud workflow scheduling problem without mining the domain knowledge. To make a good trade-off between the makespan and monetary cost, this paper puts forward a knowledge-driven multi-objective evolutionary workflow scheduling algorithm, abbreviated as KMEWSA, including two novel features. On the one hand, the structural knowledge of workflow is mined to simplify the large-scale decision variables into a series of small-scale components, such accelerating the convergence speed of MOEAs. On the other hand, the knowledge on the Pareto front range is mined to estimate the ideal and nadir points for the objective space normalization during the search process, which helps maintain population diversity for MOEAs. At last, based on twenty real-world workflows and parameters of Amazon EC2, extensive experiments are performed to compare the KMEWSA with three baseline algorithms. The results demonstrate the effectiveness of the KMEWSA in balancing makespan and monetary cost for deploying workflows in cloud computing.
Target Tracking and 3D Trajectory Reconstruction Based on Multicamera Calibration

In traffic scenarios, vehicle trajectories can provide almost all the dynamic information of moving vehicles. Analyzing the vehicle trajectory in the monitoring scene can grasp the dynamic road traffic information. Cross-camera association of vehicle trajectories in multiple cameras can break the isolation of target information between single cameras and obtain the overall road operation conditions in a large-scale video surveillance area, which helps road traffic managers to conduct traffic analysis, prediction, and control. Based on the framework of DBT automatic target detection, this paper proposes a cross-camera vehicle trajectory correlation matching method based on the Euclidean distance metric correlation of trajectory points. For the multitarget vehicle trajectory acquired in a single camera, we first perform 3D trajectory reconstruction based on the combined camera calibration in the overlapping area and then complete the similarity association between the cross-camera trajectories and the cross-camera trajectory update, and complete the trajectory transfer of the vehicle between adjacent cameras. Experiments show that the method in this paper can well solve the problem that the current tracking technology is difficult to match the vehicle trajectory under different cameras in complex traffic scenes and essentially achieves long-term and long-distance continuous tracking and trajectory acquisition of multiple targets across cameras.
Concealing a Secret Message in a Colour Image Using an Electronic Workbench

Steganography is the art of concealing security data in media, such as pictures, audio, video, text, and protocols. The objective of this paper is hiding a secret message in a colour image to prevent an attacker from accessing the message. This is important because more people use the Internet all the time and network connections are spread around the world. The hidden secret message uses two general algorithms that are embedded and extracted. This paper proposes a new algorithm to conceal a secret message in a colour image in LSB. This algorithm includes three phases: 1) dividing the colour image into a number of blocks, 2) concealing the secret message, and 3) transmitting the stego-image from the sender in a multiplexer network and receiving it through a demultiplexer network using an electronic workbench. The outcome of the new algorithm demonstrates good efficiency, high security, and robustness and is executed quickly. The system is evaluated through the measurements of mean square error, peak signal-to-noise ratio, correlation, histogram, and capacity.
Learning with Instance Bundles for Reading Comprehension

When training most modern reading comprehension models, all the questions associated with a context are treated as being independent from each other.However, closely related questions and their corresponding answers are not independent, and leveraging these relationships could provide a strong supervision signal to a model.Drawing on ideas from contrastive estimation, we introduce several new supervision losses that compare question-answer scores across multiple related instances.Specifically, we normalize these scores across various neighborhoods of closely contrasting questions and/or answers, adding a cross entropy loss term in addition to traditional maximum likelihood estimation.Our techniques require bundles of related question-answer pairs, which we either mine from within existing data or create using automated heuristics.We empirically demonstrate the effectiveness of training with instance bundles on two datasets-HotpotQA and ROPES-showing up to 9% absolute gains in accuracy.
Study of Canopy and K-Means Clustering Algorithm Based on Mahout for E-commerce Product Quality Analysis

With the rapid development of “Internet +”, mobile application and communication technology, e-commerce has increasingly become the main shopping way for consumers in China. Aiming at quality keywords, this paper designs a distributed data clustering system based on Hadoop and mahout. In order to overcome the randomness, low accuracy and many iterations of K-Means algorithm, Canopy and K-Means Clustering Algorithm based on Mahout is designed by using the advantages of Canopy algorithm, that is, no need to specify the number of clusters, high efficiency and conciseness Clustering keywords. Based on the algorithm, good results have been obtained.
Feature Based Face Recognition using Machine Learning Techniques’

Human Face has Numerous unique Features to Distinguish between each other. Face can Identified by distinguishing between face and non-face followed by Identification. Traditionally face recognition uses distinct features Comparison to Identify the Faces which is Complex for larger databases and ambiguous in many scenarios. To improve the accuracy and Scalability Proposed method uses machine learning based Haar Cascade technique for face detection and convolutional neural network is used for feature extraction followed by classification using Euclidean distance and cosine transformation to recognize the face. The results demonstrate the work is performed well in recognizing the face efficiently with different variations.
Efficient and Cost-effective Service Broker Policy Based on User Priority in VIKOR for Cloud Computing

Cloud computing has become a practical solution for processing big data. Cloud service providers have heterogeneous resources and offer a wide range of services with various processing capabilities. Typically, cloud users set preferences when working on a cloud platform. Some users tend to prefer the cheapest services for the given tasks, whereas other users prefer solutions that ensure the shortest response time or seek solutions that produce services ensuring an acceptable response time at a reasonable cost. The main responsibility of the cloud service broker is identifying the best data centre to be used for processing user requests. Therefore, to maintain a high level of quality of service, it is necessity to develop a service broker policy that is capable of selecting the best data centre, taking into consideration user preferences (e.g. cost, response time). This paper proposes an efficient and cost-effective plan for a service broker policy in a cloud environment based on the concept of VIKOR. The proposed solution relies on a multi-criteria decision-making technique aimed at generating an optimized solution that incorporates user preferences. The simulation results show that the proposed policy outperforms most recent policies designed for the cloud environment in many aspects, including processing time, response time, and processing cost. KEYWORDS Cloud computing, data centre selection, service broker, VIKOR, user priorities
ParaShoot: A Hebrew Question Answering Dataset

NLP research in Hebrew has largely focused on morphology and syntax, where rich annotated datasets in the spirit of Universal Dependencies are available.Semantic datasets, however, are in short supply, hindering crucial advances in the development of NLP technology in Hebrew.In this work, we present PARASHOOT, the first question answering dataset in modern Hebrew.The dataset follows the format and crowdsourcing methodology of SQuAD, and contains approximately 3000 annotated examples, similar to other questionanswering datasets in low-resource languages.We provide the first baseline results using recently-released BERT-style models for Hebrew, showing that there is significant room for improvement on this task.
A Survey on Imbalanced Data Handling Techniques for Classification

Classification is a supervised learning task based on categorizing things in groups on the basis of class labels. Algorithms are trained with labeled datasets for accomplishing the task of classification. In the process of classification, datasets plays an important role. If in a dataset, instances of one label/class (majority class) are much more than instances of another label/class (minority class), such that it becomes hard to understand and learn characteristics of minority class for a classifier, such dataset is termed an imbalanced dataset. These types of datasets raise the problem of biased prediction or misclassification in the real world, as models based on such datasets may give very high accuracy during training, but as not familiar with minority class instances, would not be able to predict minority class and thus fails poorly. A survey on various techniques proposed by the researchers for handling imbalanced data has been presented and a comparison of the techniques based on f-measure has been identified and discussed.
Message Encryption in Images Using LSB Steganography Sequence to Sequence Architecture

An up-to-date review and analysis of the image encryption using steganography technique will be performed in this paper. The Steganography method is used for encryption of the messages in images using the LSB algorithm and the technique of encrypting data in a data carrier (an image, in this case) in such a manner that an outsider cannot identify that a message has been encrypted into the image. The data carrier may also be an audio file or a text file, however, an image file has been considered for our paper. This technique is different from other data hiding methods like watermarking, in the way that it is far subtler. The watermarks presence is often broad-casted entirely over the image, which prevents any communication/message to be secretive or discreet. Our paper's main concern is about the significant importance of the security of the data and methods to safeguard it.
SAR Image Adversarial Samples Generation Based on Parametric Model

At present, synthetic aperture radar (SAR) image recognition algorithm has achieved high accuracy. With the development of target recognition algorithm, the adversarial sample generation algorithms which can reduce the recognition performance have been gradually developed. To reduce the recognition performance of the sample, the original samples are adjusted slightly using certain algorithms. In this paper, a method of SAR image adversarial sample generation based on parametric model is proposed to ensure the physical feasibility of adversarial samples. First, the parametric model of camouflage structure is added to the original target physical model to realize the SAR image generation based on the parametric model. Then, an improved fast gradient sign method is used to generate SAR images of targets with camouflage structures. Numerical examples validate such method by reducing the accuracy of the original recognition algorithm.
English Semantic Similarity based on Map Reduce Classification for Agricultural Complaints

Due to environmental changes, including global warming, climatic changes, ecological impact, and dangerous diseases like the Coronavirus epidemic.Since coronavirus is a hazardous disease that causes many deaths, government of Egypt undertook many strict regulations, including lockdowns and social distancing measures.These circumstances have affected agricultural experts' presence to help farmers or advise on solving agricultural problems.For helping this issue, this work focused on improving support for farmers on the major field crops in Egypt Retrieving solutions corresponding to farmer query.For our work, we have mainly focused on detecting the semantic similarity between large agriculture dataset and user queries using Latent Semantic Analysis (LSA) based on Term Frequency Weighting and Inverse Document Frequency (TF-IDF) method.In this research paper, we apply SVM MapReduce classifier as a framework for paralleling and distributing the work on the dataset to classify the dataset.Then we apply different approaches for computing the similarity of sentences.We presented a system based on semantic similarity methods and support vector machine algorithm to detect the similar complaints of the user query.Finally, we run different experiments to evaluate the performance and efficiency of the proposed system as the system performs approximately 77.8%~94.8% in F-score measure.The experimental results show that the accuracy of SVM classifier is approximately 88.68%~89.63%and noted the leverage of SVM classification to the semantic similarity measure between sentences.
CLASSIFICATION OF VEHICLE TYPES USING BACKPROPAGATION NEURAL NETWORKS WITH METRIC AND ECENTRICITY PARAMETERS

One of the efforts to break down traffic jams is to establish special lanes that can be passed by two, four or more wheeled vehicles. By being able to recognize the type of vehicle can reduce congestion. Citran based vehicle classification helps in providing information about the vehicle type. This study aims to classify the type of vehicle using a backpropagation neural network algorithm. The vehicle image can be recognized based on its shape, then the backpropagation neural network algorithm will be supported by metric and eccentricity parameters to perform feature extraction. Then from the results of feature extraction with metric parameters and eccentricity, the object will be classified using a backpropagation neural network algorithm. The test results show an accuracy of 87.5%. This shows the algorithm can perform classification well.
Adoption of deep learning Markov model combined with copula function in portfolio risk measurement

Abstract In order to accurately describe the risk dependence structure and correlation between financial variables, carry out scientific financial risk assessment, and provide the basis for accurate financial decision-making, first the basic theory of Copula function is established and the mixed Copula model is constructed. Then the hybrid Copula model is nested in a hidden Markov model (HMM), the risk dependences among banking, insurance, securities and trust industries are analysed, and the Copula–Garch model is constructed for empirical analysis of investment portfolio. Finally, the deep learning Markov model is adopted to predict the financial index. The results show that the mixed Copula model based on HMM is more effective than the single Copula and the mixed Copula models. The empirical structure shows that among the four major financial industries in China, the banking and insurance industries have strong interdependence and high probability of risk contagion. The investment failure rate under 95%, 97.5% and 99% confidence intervals calculated by Copula–Garch model are 4.53%, 2.17% and 1.08%, respectively. Moreover, the errors of deep learning Markov model in stock price prediction of Shanghai Pudong Development Bank (sh600000), Guizhou Moutai (sh600519) and China Ping An Insurance (sh601318) are 2.56%, 2.98% and 3.56% respectively, which indicates that the four major financial industries in China have strong interdependence and risk contagion, so that the macro or systemic risks may arise, and the deep-learning Markov model can be adopted to predict the stock prices.
TSM: Measuring the Enticement of Honeyfiles with Natural Language Processing

Honeyfile deployment is a useful breach detection method in cyber deception that can also inform defenders about the intent and interests of intruders and malicious insiders.A key property of a honeyfile, enticement, is the extent to which the file can attract an intruder to interact with it.We introduce a novel metric, Topic Semantic Matching (TSM), which uses topic modelling to represent files in the repository and semantic matching in an embedding vector space to compare honeyfile text and topic words robustly.We also present a honeyfile corpus created with different Natural Language Processing (NLP) methods.Experiments show that TSM is effective in inter-corpus comparisons and is a promising tool to measure the enticement of honeyfiles.TSM is the first measure to use NLP techniques to quantify the enticement of honeyfile content that compares the essential topical content of local contexts to honeyfiles and is robust to paraphrasing.
Detecting Anomalous Kicks in Taekwondo With Spatial and Temporal Features

A new scoring system based on information technology (IT) is an innovative way to judge matches fairly. However, there are reliability and expertise problems due to sophisticated differences in human-machine judging criteria. Taekwondo, a traditional Korean sport, uses an IT-based protector and scoring system. The system records the player’s movement data through its sensors, but it has a limitation in that it cannot detect anomalous kicks. Because anomalous kicks are executed with minimal strength outside the typical form of kick patterns, it interrupts fair judgment of the scoring system and reduces the liveliness of Taekwondo matches. To minimize the dispute by anomalous kicks, we aim to detect the anomalous kicks in Taekwondo matches. We construct a kick dataset and propose an attention-based deep learning model. The proposed model has the advantage of simultaneously handling the spatial and temporal features of the kick dataset because it is a model that combines a convolutional neural network (CNN) and bidirectional long-short term memory (BiLSTM) with an attention mechanism. The experimental results showed that the accuracy of the attention-based CNN-BiLSTM is 95.67%, and the false-positive rate is 0.086. We open our dataset and it is freely available on https://github.com/daanVeer/Taekwondo_dataset.
DotFAN: A Domain-Transferred Face Augmentation Net

The performance of a convolutional neural network (CNN) based face recognition model largely relies on the richness of labeled training data. However, it is expensive to collect a training set with large variations of a face identity under different poses and illumination changes, so the diversity of within-class face images becomes a critical issue in practice. In this paper, we propose a 3D model-assisted domain-transferred face augmentation network (DotFAN) that can generate a series of variants of an input face based on the knowledge distilled from existing rich face datasets of other domains. Extending from StarGAN's architecture, DotFAN integrates with two additional subnetworks, i.e., face expert model (FEM) and face shape regressor (FSR), for latent facial code control. While FSR aims to extract face attributes, FEM is designed to capture a face identity. With their aid, DotFAN can separately learn facial feature codes and effectively generate face images of various facial attributes while keeping the identity of augmented faces unaltered. Experiments show that DotFAN is beneficial for augmenting small face datasets to improve their within-class diversity so that a better face recognition model can be learned from the augmented dataset.
End-to-end Neural Information Status Classification

Most previous studies on information status (IS) classification and bridging anaphora recognition assume that the gold mention or syntactic tree information is given (Hou et al., 2013a;Hou, 2016;Rösiger et al., 2018; Hou, 2020b; Yu and Poesio, 2020).In this paper, we propose an end-to-end neural approach for information status classification.Our approach consists of a mention extraction component and an information status assignment component.During the inference time, our system takes a raw text as the input and generates mentions together with their information status.On the ISNotes corpus (Markert et al., 2012), we show that our information status assignment component achieves new state-ofthe-art results on fine-grained IS classification based on gold mentions.Furthermore, our system performs significantly better than other baselines for both mention extraction and finegrained IS classification in the end-to-end setting.Finally, we apply our system on BASHI (Rösiger, 2018) and SciCorp (Rösiger, 2016) to recognize referential bridging anaphora.We find that our end-to-end system trained on IS-Notes achieves competitive results on bridging anaphora recognition compared to the previous state-of-the-art system that relies on syntactic information and is trained on the indomain datasets (Yu and Poesio, 2020).
INTERFACE TWO-CHANNEL PORTABLE SYSTEM FOR 24 HOUR REGISTRATION OF BREATHING AND NASAL CYCLE

The registration of respiration through the left and right nostrils simultaneously makes it possible to register the dynamics and periodicity of the nasal cycle. For this purpose, an interface two-channel portable system has been developed for 24-hour recording of breathing through the two nostrils separately. The portable system can be used for multifunctional medical examinations under any conditions in patients with various anatomical and physiological disorders of breathing, sleep, and consciousness.
Present State-of-The-Art of Association Rule Mining Algorithms

A Data mining is the method of extracting useful information from various repositories such as Relational Database, Transaction database, spatial database, Temporal and Time-series database, Data Warehouses, World Wide Web. Various functionalities of Data mining include Characterization and Discrimination, Classification and prediction, Association Rule Mining, Cluster analysis, Evolutionary analysis. Association Rule mining is one of the most important techniques of Data Mining, that aims at extracting interesting relationships within the data. In this paper we study various Association Rule mining algorithms, also compare them by using synthetic data sets, and we provide the results obtained from the experimental analysis.
Construction and Analysis of Integral User-Oriented Trustworthiness Metrics

Trustworthiness metrics help users to understand information system’s or a device’s security, safety, privacy, resilience, and reliability level. These metrics have different types and natures. The challenge consists of the integration of these metrics into one clear, scalable, sensitive, and reasonable metric representing overall trustworthiness level, useful for understanding if the users can trust the system or for the comparison of the devices and information systems. In this research, the authors propose a novel algorithm for calculation of an integral trustworthiness risk score that is scalable to any number of metrics, considers their criticality, and does not perform averaging in a case when all metrics are of equal importance. The obtained trustworthiness risk score could be further transformed to trustworthiness level. The authors analyze the resulting integral metric sensitivity and demonstrate its advantages on the series of experiments.