Reviewerly/exHarmony huggingface dataset:
F2MC: Enhancing Data Storage Services with Fog-toMultiCloud Hybrid Computing

Public cloud storage can provide customers with unlimited capacity and built-in disaster recovery by exploiting IT resources in data center but suffers high latency and security risk. The emergency of fog computing can mitigate data security risk by leveraging local private server with low latency. We introduce F2MC: a Fog-to-MultiCloud hybrid storage service that combines local fog computing with remote cloud computing to enhance the quality of service (QoS) of data management. It provides an application aware data reduction scheme to improve the data traffic and space efficiency of cloud storage by exploiting deduplication and compression. Then, it further enhances the resiliency and security of cloud storage by striping encrypted data across multiple cloud vendors with a secure fault-tolerant coding. Compared with the state-of-the-art of multicloud storage systems, our experiments demonstrate that F2MC can achieve the highest performance with the same fault-tolerance capability, and a competitive low cloud cost due to its low cloud storage space overheads.
A new method to solve the problem of facing less learning samples in signal modulation recognition

Abstract In machine learning method, the number of training samples is an exceedingly important factor determining the learning system’s robustness. In our previous researches (Liu et al., J. Syst. Eng. Electron. 27.2:333–342, 2016; Liu et al., IET Commun. 11.7:1000–1007, 2017), the extreme learning machines (ELMs) have proven to be an effective and time-saving learning method for pattern classification and the signal modulation recognition. ELMs are utilized to supervised learning issues principally on signal modulation recognition. In this thesis, ELMs are extended for semi-supervised tasks that are based on the manifold regularization, therefore greatly enlarging ELMs’ applicability. This article evolves countermeasures to the less training samples which mitigate the modulation recognition efficacy and demonstrates the robustness of semi-supervised learning for signal classification in AWGN and Rayleigh-fading channels.
You have been CAUTE!

Detection of compromised social media accounts is an important problem as the compromised accounts can be exploited by hackers to spread false and misleading information. In particular, early detection of compromised accounts is essential to mitigating the damages caused by the hackers' posts, which may range from victim shaming to causing widespread public panic and civil unrest. This paper proposes CAUTE, a deep learning framework that simultaneously learns the feature embeddings of the users and their posts in order to identify which, if any, of their posts were written by a different person, i.e. a hacker. Using Twitter as an example of the social media platform, CAUTE learns a tweet-to-user encoder to infer the user features from tweet features and a user-to-tweet encoder to predict the tweet content from a combination of the user features and the tweet meta features. The residual errors of both encoders are then fed into a fully-connected neural network layer to detect whether a post was published by the specified user or by a hacker. Experimental results showed that the features learned by CAUTE are more informative than those generated by conventional representation learning methods. Additionally, CAUTE outperformed several state-of-the-art baseline algorithms in terms of their overall performance and can effectively detect compromised posts early without generating too many false alarms.
Tamil Stopword Removal Based on Term Frequency

As text data in digital form is increasing exponentially nowadays, managing and retrieving these documents becomes difficult. A number of natural language processing (NLP) processes, viz. archival, retrieval, query response, information summarization, etc., highly rely on automatic classification of text documents. This has induced researchers to apply machine learning logic to automatically categorize documents based on languages and within documents belonging to the same language to devise methods to segregate them according to its contents. More than at present, 70% of the total text classification process involves ‘Preprocessing of text’, alone [1]. This indicates its importance of preprocessing and the efficiency based on text classification logic is solely dependent on an efficient preprocessing step. This article deals with corpus creation for Tamil documents and Tamil language stopword removal. Dictionary-based and frequency-based stopword removal methods have been proposed in this work.
Computer Vision Intelligent Approaches to Extract Human Pose and Its Activity from Image Sequences

The purpose of this work is to develop computational intelligence models based on neural networks (NN), fuzzy models (FM), support vector machines (SVM) and long short-term memory networks (LSTM) to predict human pose and activity from image sequences, based on computer vision approaches to gather the required features. To obtain the human pose semantics (output classes), based on a set of 3D points that describe the human body model (the input variables of the predictive model), prediction models were obtained from the acquired data, for example, video images. In the same way, to predict the semantics of the atomic activities that compose an activity, based again in the human body model extracted at each video frame, prediction models were learned using LSTM networks. In both cases the best learned models were implemented in an application to test the systems. The SVM model obtained 95.97% of correct classification of the six different human poses tackled in this work, during tests in different situations from the training phase. The implemented LSTM learned model achieved an overall accuracy of 88%, during tests in different situations from the training phase. These results demonstrate the validity of both approaches to predict human pose and activity from image sequences. Moreover, the system is capable of obtaining the atomic activities and quantifying the time interval in which each activity takes place.
Autonomous Deep Learning: Incremental Learning of Deep Neural Networks for Evolving Data Streams

It was recently demonstrated that network evolution strategies can be used to handle evolving data streams. However, most of these strategies are applied on shallow network structure making it unable to benefit from the power of depth. The application of deep neural networks (DNNs) for evolving data streams is hindered by its static nature which cannot adapt to rapidly changing environments. In this paper, we demonstrate that recently proposed DNN approaches, Autonomous Deep Learning (ADL) and Deep Evolving Denoising Autoencoder (DEVDAN), are able to address evolving data stream problems by adopting a flexible network structure DNN. In other words, these approaches manage to incrementally construct its network structure from scratch in respect to the variation of data. Furthermore, these methods work with the absence of a user-defined threshold and fully operate in the single-pass learning fashion. The multiple hidden nodes growing and adaptive synapses pruning are proposed to boost the predictive performance. When tested on popular datasets, the modified version of ADL and DEVDAN outperformed its original version and existing strategies while maintaining the space and computational complexity to an acceptable level. Also, data streams related research directions to deal with semi-supervised learning are discussed.
Accuracy improvement in amplitude comparison‐based passive direction finding systems by adaptive squint selection

The direction of arrival (DOA) is one of the most important parameters measured by electronic warfare systems. Monopulse direction finding techniques based on amplitude comparison have many advantages because of their simplicity but they generally have limited accuracy. There are many sources of error in such a system. This work focuses on improving the noise error. An algorithm is proposed for error reduction in amplitude comparison-based direction finding systems. The algorithm, based on signal level and DOA, adaptively utilises either adjacent or alternate antennas for the DOA measurement. No additional hardware is required for the implementation of this algorithm. A prototype system is developed using spiral antennas and the performance of the proposed technique is compared against traditional ratio-based and correlation-based amplitude comparison schemes. Simulations and laboratory experiments show that the DOA accuracy can be improved by an average of 36 and 21% as compared to the ratio-based and correlation-based methods, respectively.
“I Am in Your Computer While We Talk to Each Other” a Content Analysis on the Use of Language-Based Strategies by Humans and a Social Chatbot in Initial Human-Chatbot Interactions

This research analyses the use of language-based strategies in human-chatbot interactions, namely the use of self-disclosure, question asking, expressions of similarity, empathy, humour, and the communication competence of the chatbot. This study aims to discover whether humans and a social chatbot communicate differently. Furthermore, we analyzed to what extent the chatbot's expressions affect the human users' expressions. A content analysis was conducted based on previously collected data from a longitudinal study, in which participants interacted seven times with chatbot Mitsuku over the course of three weeks. A randomly selected sample of 60 interactions was coded and results revealed that the participants self-disclosed more (intimately) and used more reciprocal self-disclosures, while the chatbot asked more questions, more reciprocal questions and more follow-up questions, expressed more similarity and humour. Moreover, the more questions the chatbot asked, the more the participants reciprocated these questions, and the more they self-disclosed. Our findings show that chatbots are programmed to gather information, by asking questions, to keep the conversation going, which elicits self-disclosure in human users. However, the self-disclosure was not reciprocated, which may hinder human-chatbot relationship formation.
Evaluating Variational Autoencoder as a Private Data Release Mechanism for Tabular Data

Multi-market businesses can collect data from different business entities and aggregate data from various sources to create value. However, due to the restriction of privacy regulation, it could be illegal to exchange data between business entities of the same parent company, unless the users have opted-in to allow it. Regulations such as the EU's GDPR allows data exchange if data is anonymized appropriately. In this study, we use variational autoencoder as a mechanism to generate synthetic data. The privacy and utility of the generated data sets are measured. And its performance is compared with the performance of the plain autoencoder. The primary findings of this study are 1) variational autoencoder can be an option for data exchange with good accuracy even when the number of latent dimensions is low 2) plain autoencoder still provides better accuracy when the number of hidden nodes is high 3) variational autoencoder, as a generative model, can be given to a data user to generate his version of data that closely mimic the original data set.
Iterative Division in the Distributive Full Non-associative Lambek Calculus

We study an extension of the Distributive Full Non-associative Lambek Calculus with iterative division operators. The iterative operators can be seen as representing iterative composition of linguistic resources or of actions. A complete axiomatization of the logic is provided and decidability is established via a proof of the finite model property.
Investigation of wind speed effect on different mounted PV systems using satellite data

Abstract Renewable energy utilization has been increasing at the same rate of the increasing the consumption of energy in all countries. The best use of the available energy and the search for different energy sources have been an important field of study all around the world after highly increasing of energy demand. Hence, today, generating energy from solar cells also called photovoltaics (PV) has become essential field of renewable energy. This energy source is also one of the fastest growing technology, converting direct sunlight to electricity. In this article, optimum tilt angles were examined by changing monthly, seasonal, and annually, which were obtained from PV panels installed in Bursa city of Turkey. It was found that the solar radiation increased by 22.21% for monthly, 21.17% for seasonal, and 16.44% for yearly. So, it was necessary to vary the tilt angle of the PV panels at least seasonal (four times a year). In addition, the cooling effect of the wind on the cell temperatures, electrical energy generation, and energy efficiency of the different mounted PV panels with the optimum tilt angle on a monthly basis was theoretically examined. Consequently, the wind cooling was shown to be very effective on cell temperatures and energy efficiency under wind cooling effect was more than 4.8% during a year. Another finding of this study was that the energy efficiency and generated electrical energies of free standing PV panels were higher than sloped‐roof PV panels.
Formalization and co-simulation of attacks on cyber-physical systems

This paper presents a methodology for the formal modeling of security attacks on cyber-physical systems, and the analysis of their effects on the system using logic theories. We consider attacks only on sensors and actuators. A simulated attack can be triggered internally by the simulation algorithm or interactively by the user, and the effect of the attack is a set of assignments to the variables defined in the Controller. The global effects of the attacks are studied by injecting attacks in the system model and co-simulating the overall system, including the system dynamics and the control part. Interesting properties of the behavior of the system under attack can also be formally proved by theorem proving. The INTO-CPS framework has been used for co-simulation, and the methodology is applied to the Line follower robot case study of the INTO-CPS project. The theorem prover of PVS has been used for deriving formal proofs of invariants of the system under attack.
A high precision image registration method for measurement based on the stereo camera system

Image registration is the major problem to many applications including measurement in the area of computer vision. In this paper, a scheme of high-precision measurement based on the stereo camera system is proposed. To take advantages of the speed up robust features and the optical flow features, they are chosen to be the registration basis for estimating and refining the initial geometrical transformation, This paper reviews the existing methods in the optimization procedure of image registration. By analyzing the weakness and strength recent progress in global polynomial optimization, a hybrid method which combines the merits of the SVD method, interleaving parameterization algorithm and the convex linear matrix inequalities relaxation method together is presented to achieve the global solution of the fundamental matrix and the rotation matrix problems. The experimental results confirms the high precision and stability of the proposed method.
Haar wavelet based algorithm for solution of second order electromagnetic problems in time and space domains

In this paper, a novel wavelet algorithm has been proposed for solving second order electromagnetic problems both in time and space domains. The algorithm has been used to solve four different problems, i.e. the transmission line problem, the problems associated with electromagnetic waves travelling in different media, the problem related to uniform plane wave in lossy dielectric and problem associated with the telegrapher's equation. The comparison between the results obtained using the proposed approach and the conventional numerical techniques demonstrate that the proposed technique exhibits better time complexity. In essence, the proposed algorithm possesses complexity of order O(n) whereas the conventional technique possesses complexity order of O(n2). Furthermore, the memory requirement of the proposed algorithm is significantly lower when compared to the conventional technique.
A Dynamic ACO-Based Elastic Load Balancer for Cloud Computing (D-ACOELB)

Cloud computing is for the delivery of computational service like servers, software, databases, storage, networks, intelligence, analytics, and more via the Internet ("the cloud") for tendering faster innovation, economies of scale, and flexible resources. The workload in cloud computing is defined as the amount of computational work at a given time that the computer has been given to do. This computational workload comprises of some number of users connected to and interacting with the computer's applications additionally to the computer's application programs running in the system. This workload may alter dynamically based on the available resources and the end-users. Hence, the key challenging duty in the cloud computing is balancing the workload among the Virtual Machines (VM's) of the systems. So, there is a need for introducing an elastic dynamic load balancer for distributing the workloads efficiently in the cloud. A load balancer can distribute the loads among the VM's of multiple servers or compute resources. The utilization of such elastic and dynamic load balancer can enhance the fault tolerance capability of applications and the availability of cloud resources. As your needs change, adding or removing of computing resources without disrupting the overall flow of requests is made possible utilizing this elastic and dynamic load balancer. Generally, an elastic load balancing task chains three types of balancers, they are Application Load Balancers, Classic Load Balancers, and Network Load Balancers. The Application Load Balancer can be chosen based on the need of applications. In this chapter, it proposed a dynamic and elasticity approach (D-ACOELB) for workload balancing in cloud data centers based on Ant Colony Optimization (ACO).
Structure Learning of Bayesian Networks Using Elephant Swarm Water Search Algorithm

Bayesian networks are useful analytical models for designing the structure of knowledge in machine learning. Bayesian networks can represent probabilistic dependency relationships among the variables. One strategy of Bayesian Networks structure learning is the score and search technique. The authors present the Elephant Swarm Water Search Algorithm (ESWSA) as a novel approach to Bayesian network structure learning. In the algorithm; Deleting, Reversing, Inserting, and Moving are used to make the ESWSA for reaching the optimal structure solution. Mainly, water search strategy of elephants during drought periods is used in the ESWSA algorithm. The proposed method is compared with simulated annealing and greedy search using BDe score function. The authors have also investigated the confusion matrix performances of these techniques utilizing various benchmark data sets. As presented by the results of the evaluations, the proposed algorithm has better performance than the other algorithms and produces better scores and accuracy values.
Empirical Analysis using Feature Selection and Bootstrap Data for Small Sample Size Problems

With the emergence of more application scenarios, the classification accuracy of small sample size problems especially in some areas such as medical data, needs to be further improved. This paper presents an empirical analysis using feature selection and bootstrap data to overcome this problem. Four traditional classification models were used in the experiment: k-Nearest Neighbors, Naive Bayes, Decision Tree and XGBoost. According to the result, the bootstrap method especially with XGBoost classifier provides better classification accuracy than using feature selection method and the data that randomly selected from original data, respectively.
A Hybird Method Based on Particle Swarm Optimization and Moth-Flame Optimization

This paper presents a novel hybrid optimization algorithm based on particle swarm optimization (PSO) and moth-flame optimization (MFO), called Hy-PSO-MFO, for solving the function optimization problems. In this algorithm, the ideas of PSO and MFO are integrated in a rational way to overcome the drawbacks of PSO and MFO, and thus improve the global search ability. The performance of the proposed algorithm is investigated on solving the well-known benchmark functions. The experimental results demonstrate that the Hy-PSO-MFO performs better than the standard PSO and original MFO in terms of solution quality and convergence speed.
Path tracking control based on Deep reinforcement learning in Autonomous driving

Lane keep assist (LKA) and Adaptive Cruise Control (ACC) are two fundamental yet critical functions for autonomous driving, and conventional methods using PID controllers may not perform well in certain extreme driving conditions. In this paper, we propose a reinforcement learning based approach to train the agent to learn LKA and ACC and hence adapt to diverse scenarios. Particularly, we employ deep deterministic policy gradient (DDPG) algorithm to train the agent and consider both state space and action space as continuous, and designed two neural network critic-network and actor-network to simulate the strategy function and Q-function. Then, we train the two neural networks by deep learning method. Finally, Simulations are conducted with both reinforcement learning and traditional PID controller, and the results of reinforcement learning is more adaptive to extreme road conditions in comparison with a traditional PID controller.
A Study on Ranking Fusion Approaches for the Retrieval of Medical Publications

In this work, we compare and analyze a variety of approaches in the task of medical publication retrieval and, in particular, for the Technology Assisted Review (TAR) task. This problem consists in the process of collecting articles that summarize all evidence that has been published regarding a certain medical topic. This task requires long search sessions by experts in the field of medicine. For this reason, semi-automatic approaches are essential for supporting these types of searches when the amount of data exceeds the limits of users. In this paper, we use state-of-the-art models and weighting schemes with different types of preprocessing as well as query expansion (QE) and relevance feedback (RF) approaches in order to study the best combination for this particular task. We also tested word embeddings representation of documents and queries in addition to three different ranking fusion approaches to see if the merged runs perform better than the single models. In order to make our results reproducible, we have used the collection provided by the Conference and Labs Evaluation Forum (CLEF) eHealth tasks. Query expansion and relevance feedback greatly improve the performance while the fusion of different rankings does not perform well in this task. The statistical analysis showed that, in general, the performance of the system does not depend much on the type of text preprocessing but on which weighting scheme is applied.