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Headline generation is a challenging subtask of abstractive text summarization, which its output should be a summary, shorter than one sentence. It would be precious to develop a dataset for the evaluation of abstractive summarization methods on this task in the Persian language. There are several datasets for headline generation in Persian, most of which are not large enough to be used by more sophisticated methods of text summarization, such as deep learning models. Moreover, all of these datasets are focused on daily news and there is no dataset for summarizing scientific Persian papers. In this article, we present “ElmNet,” a headline generation dataset of about 400,000 abstract/headline pairs of scientific papers, gathered from six major publishers for scientific articles in Persian. We, moreover, evaluate the performance of the most important deep learning-based headline generation methods, on the proposed dataset. The results prove the comparability of the performance of the state-of-the-art methods on this task, to their results on the existing English datasets.

In this paper, we aim to implement augmented reality (AR) on distant text documents or books. For this purpose, we propose a new paper-based AR system that can detect text documents in real scenes, markerize and identify them, estimate their relative 3D poses to the camera, and augment them with virtual contents. Unlike the previous paper-based AR systems (applicable to only close documents), the proposed system not only requires no detection of words or characters, but allows partial occlusions like the previous systems. In our experiments, the proposed system worked at 24 fps and could consistently achieve high identification rates for both occluded and unoccluded pages.

Multimedia field has dramatically grown in the last 20 years. We still remember the first ACM Multimedia Conference in Anaheim, CA in 1993, at whichmost participants even did not know the meaning of the word “multimedia”. In its early age, research in multimedia was focused on video and image compression and developing relevant standards, multimedia communication and synchronization, storage and retrieval, multimedia modeling, and creating first multimedia applications. Today, multimedia is interdisciplinary field, which is in tremendous explosion finding applications in practically all parts of our lives, from art, education, advertisement, and entertainment to engineering, medicine, business, scientific research, and many others. We created two special issues: Survey Papers in Multimedia by World Experts and Hot Research Topics in Multimedia We invited experts in the field both from academia and industry to contribute with their articles, and the response was overwhelming. This special issue “Survey Papers” contains 13 articles presenting the state of the art in the various areas of multimedia, from context modeling, semantic retrieval, personalized multimedia, to multimedia data mining, augmented reality, and others. The main objective of this special issue is to present the current status of multimedia field from leading researchers and practitioners worldwide. We thank all authors for their contributions in creating these two special issues. Guest Editors Ramesh Jain, Albert Del Bimbo, Tat-Seng Chua, and Borko Furht Multimed Tools Appl (2011) 51:1–4 DOI 10.1007/s11042-010-0681-1

In the recent digitization era, image hashing is a key technology, including image recognition, authentication and manipulation detection, among many multimedia security applications. The primary challenge in hashing schemes is to extract its robust feature. For a better understanding and design of a robust image hashing algorithm, it is very crucial to look into few important parameters like discrimination, robustness, reliability, etc. This paper reflects a detailed study of the existing literature on hashing-based image authentication techniques. This work provides a systematic overview and highlights the merits and demerits associated with various state-of-the-art techniques. In particular, the basic features and categories of image authentication techniques based on hashing are explored along with their properties. Moreover, different performance measures such as output metrices, receiver operating characteristics (ROC) parameters, execution time, etc., have been discussed in this work. The paper also compares the performances of various existing algorithms related to different content preserving operations on diverse data sets. This paper summarizes all the techniques and provides the most optimum solutions in regard to image hashing techniques based on different parameters.

There have been considerable advancements in multimedia technologies over the past 5 years. It has been observed that state-of-the-art multimedia systems face three broad categories of challenges: (1) Dependency on continuous network connections, (2) Data-sharing applications & collaboration, and (3) Security issues. Among these, security vulnerability poses a major threat to modern multimedia systems. Therefore, it is imperative to carefully investigate the security issues that can endanger wireless and mobile communications. At present, multimedia security research mainly focuses on wireless traffic monitoring, wireless system attacks, and wireless and mobile security. In this paper, we have used the network attack-type, “Reconnaissance”, which contains two types of malicious activities: (1) OS scanning, and (2) Fuzzing. The goal of this paper is to quantify multimedia security risks due to Fuzzing by using various types of machine learning models. The highest accuracy i.e., 96.8%, is obtained using the XGBoost classifier, which is good compared to the existing models present in the literature. This is the first paper, to the best of our knowledge, that uses machine learning methods to differentiate between benign and malignant Fuzzing attacks.

Securing the network is a top priority for all organizations. Many high-profile breaches in the field of cybersecurity claim to threaten the global economy. In addition to that, they impose a threat for system infiltration and data theft, clearly warning organizations and companies to strengthen the existing cybersecurity frameworks and policies. In this paper, a real-time blockchain based integrated solution is proposed that fortifies the security against cyber-attacks. The scheme introduces the concept of Cyber-Soldiers that work together to update the Artificial Neural Network (ANN) based framework, which exists on the cloud for incentives. The enterprises can subscribe to this scheme monthly. This research work introduces its cryptocurrency CyberCent for the network. Also, smart contracts are deployed on the conditionally public blockchain to carry out monetary transactions. The experimental analysis with state-of-art compression of the framework is also demonstrated in the paper.

In smart grid infrastructure, multimedia communication plays an important role in various applications, for instance, load monitoring, automatic smart meter reading, and energy management. Energy management has gained widespread popularity with the increasing energy demand. The goal of this paper is to explore multiagent-based Reinforcement Learning (RL) for multicarrier (i.e., electricity and gas) Residential Energy Management (REM) systems with data security. It facilitates the possibility for a separate Demand Response Program (DRP) for every energy component accelerates with computational aspects of RL. This paper proposes Q-MSEM, a Q-learning-based Multiagent and Secure Energy Management scheme for the optimal solution of REM problems using RL and Ethereum Blockchain (EB) to reduce energy load and decrease energy costs. Then, Q-MSEM uses Ethereum Smart Contract (ESC) to covenants data security issues using off-chain storage, i.e., InterPlanetary File System (IPFS) to handle data storage costs issues. Experimental results disclose the effectiveness of the proposed Q-MSEM scheme in terms of reduction in load, energy cost (15.82%), and data storage cost.

The paper aims to show how blockchain, particularly Ethereum private blockchain, performs as a record keeper for Malaysian Muslim marriage records as a case study. The study also aims to demonstrate how blockchain technology conserves marriage data and improves data sharing efficiency involves accessing data from the blockchain and multiple users through proof of concept (POC). This POC also intended to seek evidence of immutability, transparency, and authenticity claims by the technology to ensure the record's security is a high priority. The study implements a smart contract on the Ethereum platform in the Government of Malaysia network environment and, the results provide significant evidence of private Ethereum blockchain principles of high availability, efficiency, immutability, transparency, and truthfulness particularly in record keeping. Future research can investigate other blockchain technology for other blockchain initiatives, such as blockchain disruptors. This paper is useful for management researchers and guides management practice for decision-making when determining whether the technology can use as a record-keeper. This paper proposes POC to design and develop record-keeping of Malaysian Muslim marriage records using Ethereum blockchain technology.

The availability of videos has grown rapidly in recent years. Finding and browsing relevant information to be automatically extracted from videos is not an easy task, but today it is an indispensable feature due to the immense number of digital products available. In this paper, we present a system which provides a process to automatically extract information from videos. We describe a system solution that uses a re-trained OpenNLP model to locate all the places and famous people included in a specific video. The system obtains information from the Google Knowledge Graph related to relevant named entities such as places or famous people. In this paper we will also present the Automatic Georeferencing Video (AGV) system developed by RAI (Radiotelevisione italiana, which is the national public broadcasting company of Italy, owned by the Ministry of Economy and Finance) Teche for the European Project “La Citta Educante” (The Educating City: teaching and learning processes in cross-media ecosystem) Our system contributes to The Educating City project by providing the technological environment to create statistical models for automatic named entity recognition (NER), and has been implemented in the field of education, in Italian initially. The system has been applied to the learning challenges facing the world of educational media and has demonstrated how beneficial combining topical news content with scientific content can be in education.

Semantic similarity assessment between concepts is an important task in many language related applications. In the past, many approaches to assess similarity of concepts have been proposed by using one knowledge source. In this paper, some limitations of the existing similarity measures are identified. To tackle these problems, we propose an extensive study for semantic similarity of concepts from which a unified framework for semantic similarity computation is presented. Based on our framework, we give some generic and flexible approaches to semantic similarity measures resulting from instantiations of the framework. In particular, we obtain some new approaches to similarity measures that existing methods cannot deal with by introducing multiple knowledge sources. The evaluation based on eight benchmarks, three widely used benchmarks (i.e., M&C, R&G, and WordSim-353 benchmarks) and five benchmarks developed in ourselves (i.e, Jiang-1, Jiang-2, Jiang-3, Jiang-4, and Jiang-5 benchmarks), sustains the intuitions with respect to human judgements. Overall, some methods proposed in this paper have a good human correlation (Pearson correlation with human judgments and Spearman correlation with human judgments) and constitute some effective ways of determining semantic similarity between concepts.

Human detection and activity recognition is very important research area in the healthcare, video surveillance, pedestrian detection, intelligent vehicle system and home care center. Among the various human activity detection frameworks, the statistical based approach were most intensively studied and used in practice in which pattern recognition was traditionally formulated. More recently, supervised learning based techniques and methods imported from statistical learning theory have deserved increasing attention. Many new supervised learning methods such as transfer learning, multi-instance learning, and the new trends in deep learning techniques have used for the formulation of solutions to the human activity detection. This paper reviews the automatic human detection and their activity recognition in the video sequences and static images. We explain several problems of human detection and activity recognition in different steps such as processing, segmentation of human features extraction and classification. Moreover, discuss the problems in each step and provide the recent state- of-the-art methods, gaps between recent methods, technical difficulties, applications and their challenges. Several features extraction techniques and corresponding problems for human classification have been discussed in details. Special emphasis have been given on convolution neural network that solves the problem of human segmentation, efficient classification and activity recognition. The objective of this review paper is to summarize and review related of the established and recent methods used in various stages of a human detection and activity classification system and identify research topics and applications that are at the forefront of this exciting and challenging field. Further, the evaluation protocols (i.e. datasets and simulation tools) and possible solution of current limitation have been discussed briefly in this survey.

Extreme learning machine (ELM) is a training algorithm for single hidden layer feedforward neural network (SLFN), which converges much faster than traditional methods and yields promising performance. In this paper, we hope to present a comprehensive review on ELM. Firstly, we will focus on the theoretical analysis including universal approximation theory and generalization. Then, the various improvements are listed, which help ELM works better in terms of stability, efficiency, and accuracy. Because of its outstanding performance, ELM has been successfully applied in many real-time learning tasks for classification, clustering, and regression. Besides, we report the applications of ELM in medical imaging: MRI, CT, and mammogram. The controversies of ELM were also discussed in this paper. We aim to report these advances and find some future perspectives.

Recently, social media are the most popular way of consuming news for people due to their fast, low cost, and easy accessibility. Unfortunately, in order to provide financial, political, or personal interests on social media, a large amount of fake news is intentionally produced that contains false information. Although fake news detection is a very important problem to avoid negative effects, efficient studies on this issue are limited. More efficient models are required in order to obtain better solutions with respect to different metrics for fake news detection. In this paper, a novel model was proposed that uses optimization methods for fake news detection. In addition, an improved Salp Swarm Optimization (SSO) based on a nonlinear decreasing coefficient and oscillating inertia weight was proposed to find the best optimum solution for fake news detection for the first time. The standard SSO, Grey Wolf Optimization (GWO) which is one of the most recent swarm intelligence algorithms, and two new adaptive SSO methods were modeled to detect fake news for the first time in this study. These methods were tested over four different real-world fake news data sets to verify the performance of the algorithms proposed in this paper. Furthermore, Friedman test was conducted to distinguish the differences among these methods. The obtained results prove that the proposed new model is significantly superior to standard SSA and GWO on the real-world fake news data sets.

Stock market is a dynamic and volatile market that is considered as time series data. The growth of financial data exposed the computational efficiency of the conventional systems. This paper proposed a hybrid deep learning model based on Long Short- Term Memory (LSTM) and Artificial Bee Colony (ABC) algorithm. ABC is best fit for hyper parameter selection for deep LSTM models and maintains the equilibrium of exploitation and exploration issues. Handling a large volume of multidimensional reviews from social media is a major challenge. This paper evolves the multiple aspects of market sentiments and uses the reliable Big data platform Hadoop ecosystem and its services to compute sentiment polarity index. The ABC-LSTM hybrid model is validated with other core and hybrid models with evolutionary algorithms as Differential Evolution (DE) and Genetic Algorithm (GA). For the experiments, 10 years of historical datasets and social media reviews of IT sector funds Apple Inc. (AAPL), Microsoft corporation (MSFT) and Intel corporation (INTL) from NASDAQ GS, an American stock exchange are considered to validate hybrid forecasting models. Proposed algorithm ABC-LSTM is used to tune the hyperparameters (window size, LSTM units, dropout probability, epochs, batch size and learning rate) and evaluated through Root Mean Square Error (RMSE) and Mean Absolute Percentage Error (MAPE) as loss function. Performance analysis proves that with sentiment polarity, ABC optimized LSTM obtains improved forecasting accuracy over its counterpart models.

In the COVID-19 pandemic, telehealth plays a significant role in the e-healthcare. E-health security risks have also risen significantly with the rise in the use of telehealth. This paper addresses one of e-health’s key concerns, namely security. Secret sharing is a cryptographic method to ensure reliable and secure access to information. To eliminate the constraint that in the existing secret sharing schemes, this paper presents Tree Parity Machine (TPM) guided patients’ privileged based secure sharing. This is a new secret sharing technique that generates the shares using a simple mask based operation. This work considers addressing the challenges presents in the original secret sharing scheme. This proposed technique enhances the security of the existing scheme. This research introduces a concept of privileged share in which among k number of shares one share should come from a specific recipient (patient) to whom a special privilege is given to recreate the original information. In the absence of this privileged share, the original information cannot be reconstructed. This technique also offers TPM based exchange of secret shares to prevent Man-In-The-Middle-Attack (MITM). Here, two neural networks receive common inputs and exchange their outputs. In some steps, it leads to full synchronization by setting the discrete weights according to the specific rule of learning. This synchronized weight is used as a common secret session key for transmitting the secret shares. The proposed method has been found to produce attractive results that show that the scheme achieves a great degree of protection, reliability, and efficiency and also comparable to the existing secret sharing scheme.

Image encryption mechanisms provide confidentiality and concealment of information (image) in transmission over the alleyway, susceptible to prevalent invasions. With escalating threats in cybersecurity, various cryptography techniques were projected by researchers. The abundance of such mechanisms requires systematic investigation so that that appropriate method can be selected for diverse applications. An efficient encryption technique must analyze all the parameters in an ideal situation and practical cases. Numerous survey papers available in the literature for experimental comparison are devoid of many probable attacks such as anti occlusion attack, chosen-plaintext attack, jpeg compression, etc. This paper provides a qualified study of almost all basic, traditional, chaotic, lightweight, quantum, fractal, and qubit based encryption methods under the influence of prevalent intimidation (Salt & Pepper, Gaussian, Poisson, Rotation attack, chosen-plaintext attack, known-plaintext attack, and so on). We thus carried out an experimental and theoretical investigation based on statistical, differential, and quantitative analysis. To measure the efficacy, all the mechanisms are implemented in MATLAB-2014 and provide the standard deviation to each metric. It is observed that the Quantum and Qubit based algorithms are superlative in comparison to others under the majority of extensive threats due to their sensitive behaviour towards initial conditions and highly random behaviour.

Devanagari script is the most widely used script in India and other Asian countries. There is a rich collection of ancient Devanagari manuscripts, which is a wealth of knowledge. To make these manuscripts available to people, efforts are being done to digitize these documents. Optical Character Recognition (OCR) plays an important role in recognizing these documents. Convolutional Neural Network (CNN) is a powerful model that is giving very promising results in the field of character recognition, pattern recognition etc. CNN has never been used for the recognition of the Devanagari ancient manuscripts. Our aim in the proposed work is to use the power of CNN for extracting the wealth of knowledge from Devanagari handwritten ancient manuscripts. In addition, we aim is to experiment with various design options like number of layes, stride size, number of filters, kenel size and different functions in various layers and to select the best of these. In this paper, the authors have proposed to use deep learning model as a feature extractor as well as a classifier for the recognition of 33 classes of basic characters of Devanagari ancient manuscripts. A dataset containing 5484 characters has been used for the experimental work. Various experiments show that the accuracy achieved using CNN as a feature extractor is better than other state-of-the-art techniques. The recognition accuracy of 93.73% has been achieved by using the model proposed in this paper for Devanagari ancient character recognition.

The prediction of electricity consumption is a vital foundation for smart energy management. Since the consumption of power varies with different appliances, better forecasting of power and peak demand is an essential accomplishment for the proper planning and development of the power generation and distribution system. This forecast analysis helps the service providers and the government to understand the lifestyle of the customers. The existing prediction and forecasting models are not meeting the standard requirements and moreover difficult to apply in practice. The forecast says that the boom of electric vehicles will increase the demand of electricity globally by 3% for the upcoming year. There exists a number of machine learning algorithms for classification and decision making. But the accuracy of the exiting methods have shown inferior performance in terms of prediction which leads to inefficient decision making in the quantity of electricity generation. This paper proposes the use of random forest supervised learning model to forecast the consumption of power and identify the level of peak demand. The large smart meter dataset collected at varying seasons of the year is fed to the random forest classifier technique for better analysis and forecasting. This approach outperforms in terms of accuracy, stability and generalization. In addition, this paper investigates the existing models and compares the performance with those models. The performance analysis shows that this model performs better than the other investigated models with performance accuracy of 95.67% and enhanced accuracy of precision and recall.

A decentralized model is primarily used for communication and file transfer, the peer-to-peer network is based on this model. The field of real-time communication and media streaming has witnessed enormous growth in recent times owing to their use of peer-to-peer network. A significant part of Internet traffic is being created by the peer-to-peer network resulting in an increase in its demand. The rise in prominence of peer-to-peer network can be attributed to its properties like resource utilization and distributed nature. Resource utilization of peer-to-peer network is one foremost motive for selecting this network over the traditional client-server architecture. The availability of selfish peer or free riders affects the total resource utilization and degrades the performance of the network. So, there is a requirement of an incentive-based mechanism to motivate the selfish peer or free riders in the network to improve the performance of the network. In this paper, we will discuss a new score based incentive mechanism to improve the overall resource utilization. This approach is based on a reward-punishment based method, so the peers are encouraged or motivated to share more resources and chastise selfish peers. For calculating the score value of a peer, parameters like upload capacity, video quality, control packets, a time period for which peers stay in the network can be used. Different score values are assigned for different parameters. The simulation results presented in this paper verify the approach and illustrate that the quality of video and performance of network improves using our new score based incentive mechanism. The parameters used for calculating the performances are; end-to-end delay, playback delay, start-up delay, and frame redundancy etc.

Due to the huge flow of data and complications in mutable characteristics of the data, Distributed Denial of Services attacks existed in the Multimedia Internet of Things. Attacks over the IoT have become an increasing menace in recent time, which tries to hack or illegally tamper the streaming data available over the networks. On the other hand, there has been an increase in volume in research contributions to effectively counter these attacks and implement a strong defense mechanism. There have been numerous algorithms and frameworks implemented in recent times that are intelligent and soft computing-based. These evolution-based algorithms play a vital role in self-adapting the system under attack towards increasing and new types of attacks which are increasing day by day. One such area of soft computing algorithms investigated in this paper is the Artificial Neural Network or popularly known as ANNs. It works analogously to the biological neurons in the human body. In this paper, we systematically explain the ANN-based network model to counteract the DDoS attacks in the Multimedia Internet of Things, architecture, and implementation of ANNs, the experimental investigations and findings which help in drawing an inference of ANN-based defense models.

The influence of Internet of Things (IoT) is growing exponentially in the consumer patterns and will continue to increase in the coming years. With millions of interconnected devices over the internet, IoT is currently running into its monumental security and centralization issues that can be resolved using Blockchain. This paper aims to demonstrate the use of a smart contract on a private Ethereum Blockchain (also known as permission less Blockchain) to check whether a user holds enough tokens to utilize a service. Private in this context implies a private instance of the Ethereum implementation in which we make use of the same security and principles of the Ethereum main chain on a personal network of connected IoT devices which improves the overall privacy and security of the system than the traditional schemes. The proposed model consists of a target IoT device controlled by a Raspberry Pi, running a client application that makes decisions based on the state of the smart contract. With this environment, we are able to achieve the following goals: (1) no data leaves the private network (2) all services are employed with transparency and accountability (3) only registered devices can issue requests for using a service. Although the demonstration is made on a home network, the model presented in this paper can be utilized in commercial environments where any service can be availed from a wide array of smart devices through a mobile application.

User-Generated Content (UGC) is turning into the predominant type of internet traffic. Content popularity prediction plays a pivotal role in managing this large-scale traffic. As a result, popularity prediction is increasingly becoming an important area of research in computer networking. Generally, popularity prediction methods are classified into two groups, namely, feature-driven and early-stage. While feature-driven methods predict content popularity before publication, early-stage methods monitor early content popularities to forecast the future. Many papers have shown that early-stage popularity prediction performs better than feature-driven methods. In this paper, we improve the performance of early-stage popularity prediction by first, classifying the data into several clusters using k-means clustering with Pearson correlation distance, and then, training a Deep-Belief Network (DBN) for each cluster. We evaluate our method using a dataset of YouTube videos and show that using a generative model such as DBN for time series prediction significantly improves the performance. Numerical results indicate that our proposed method outperforms other state-of-the-art methods by reducing Mean Absolute Percentage Error (MAPE) and mean Relative Square Error (mRSE) by up to 47.86% and 25.18%.

The real-time solicitations of biometric systems have been extensively used for several things with the growing necessities of higher security level. There are numerous biometric traits used for person identification. In recent years, iris biometric trait become very popular and efficient in many security applications. However, biometric systems are prone to presentation attack. This attack is carried out by using spoofing of any biometric modality and present as a real trait. The objective of this paper is to present a broad and well thought-out overview of the effort that has been conceded out over the preceding years in the field of iris anti-spoofing. Spoofing of an iris image can be done by paper print and contact lens etc. So, it is essential to discover fake iris up to its core level. In this survey, we have discussed various research work done in the past on the topic of different classes of presentation attack and summarized the state-of-the-art methods in a structural way. We have also discussed the future direction in the field of iris liveness detection.

Sentiment analysis is a process of analysis, processing, induction, and reasoning of subjective text with emotional color. It is a research direction of Natural Language Processing (NLP). It is often used to extract the attitudes towards someone or something of people. That can help users find potential problems to improve or predict. As one of the main resources of online media data, film review information is often used as a dataset in the field of sentiment analysis. Researchers put forward many models in sentiment analysis to analyze the film review dataset. Accuracy, precision, recall rate, F1-scores are important standards to measure the quality of a model. To improve these criteria, a new model is proposed in this paper. The new model combines a bidirectional Long Short-Term Memory network (biLSTM) or a bidirectional Gated Recurrent Unit (biGRU) and an Enhanced Multi-Head Self-Attention mechanism. The Enhanced Multi-Head Self-Attention is a two-layer modified Transformer encoder. This modified Transformer encoder is that its masking operation and the last feedforward layer are removed. Besides, the loss function of this new model is the sum of the weighted root mean square error (RMSE) and the cross entropy loss. The operation of this sum can improve the ability of auto-encoder to reproduce. That can improve classification accuracy. The proposed model is an autoencoder classification model. In this model, biLSTM or biGRU are used as encoders and decoders at both ends of the network. Enhanced Multi-Head Self-Attention is used to encode the inter-sentence information as the middle hidden layer. A four-layer autoencoder network model is constructed to perform sentiment analysis on movie review in this paper. The movie review data sets (IMDB movie comment data set and SST-2 sentiment data set) are used in experiments. Experiment results show that the proposed model performs better in terms of accuracy, precision, recall rate, and F1-scores comparing with the baseline models. BiLSTM is better than biGRU by comparing the effect of them in the model. Finally, Bidirectional Encoder Representations from Transformers (BERT) is used in our method instead of word2vec as a pre-training structure. Compared with the baseline model based on BERT, the proposed model is better.

Over the last few years, there has been a rapid growth in digital data. Images with quotes are spreading virally through online social media platforms. Misquotes found online often spread like a forest fire through social media, which highlights the lack of responsibility of the web users when circulating poorly cited quotes. Thus, it is important to authenticate the content contained in the images being circulated online. So, there is a need to retrieve the information within such textual images to verify quotes before its usage in order to differentiate a fake or misquote from an authentic one. Optical Character Recognition (OCR) is used in this paper, for converting textual images into readable text format, but none of the OCR tools are perfect in extracting information from the images accurately. In this paper, a method of post-processing on the retrieved text to improve the accuracy of the detected text from images has been proposed. Google Cloud Vision has been used for recognizing text from images. It has also been observed that using post-processing on the extracted text improved the accuracy of text recognition by 3.5% approximately. A web-based text similarity approach (URLs and domain name) has been used to examine the authenticity of the content of the quoted images. Approximately, 96.26% accuracy has been achieved in classifying quoted images as verified or misquoted. Also, a ground truth dataset of authentic site names has been created. In this research, images with quotes by famous celebrities and global leaders have been used. A comparative analysis has been performed to show the effectiveness of our proposed algorithm.

The availability of the system is one of the main requirements of a multimedia-based e-health application that carries critical patient health information in the network environment. On the other hand, the Distributed Denial of Service (DDoS) attack is one of the most common attacks on the availability of computer networks which can be devastating for a healthcare system. Therefore, a countermeasure to this attack has to be performed in the early steps of the attack to protect the systems against its damages. Detection methods cannot support this and are only able to detect the attack after it happened. Thus, it is necessary to predict DDoS attacks according to the evidence which the attack makes in the network in the early steps of the attack. Therefore, Prediction approaches can reduce the cost of the attacks compared to detection approaches. In this paper, we propose a new method for prediction of DDoS attack based on Lyapunov Exponent Analysis and Echo State Network (LEAESN). In this method, the future traffic of the network is predicted using the Exponential Smoothing technique, then the time series of the prediction error is calculated based on the difference of this prediction and the observed traffic of the network. As shown in this paper, this time series is chaotic in the presence of attack traffics. To predict the DDoS attack, this time series is predicted using a Recurrent Neural Echo State Network (SCESN), and the attack is detected using Lyapunov exponent analysis on the predicted time series. For the evaluation of LEAESN, we test the method on the Darpa98 dataset which consists of a standard dataset for evaluation of intrusion detection systems. LEAESN has an appropriate ability to predict the DDoS attack.

This paper explores the behavior of an algorithm called Audio Dynamics Automatic Equalization (ADAE). This algorithm has been inspired by research carried out in the context of image restoration: it is the adaptation of a contrast and color unsupervised equalizer for images, called Automatic Color Equalization (ACE), into the audio domain. Beside testing if the domain shift from image to audio processing can bring some interesting result, this work also investigates if ADAE behaves like already-known technologies for audio manipulation and restoration. To this end, after a description of the original and the derived algorithms, quantitative test are carried out using typical analyses from the Sound and Music Computing literature, such as frequency response, transfer function, and harmonic distortion. Finally, the paper discusses how the algorithm introduces dynamic range adjustments and non-linear distortions, thus behaving like a dynamics processor, a harmonic exciter, and a waveshaper.

Text classification could be defined as the way of allocating text into predefined groups according to its contents. Over the past few years, an increase emerged in the volume of information in the varied fields on the Internet, thus making the classification of texts one of the most important, yet challenging. Text classification is commonly employed in numerous applications and for different objectives. The extensive and broad use of the Internet, particularly in the Arab world, as well as the massive number of the documents and pages which are provided in the Arabic language, raised the need for having suitable tools for classification of these pages and documents by their main categories. The aim of this paper to study the effect of the improved CHI (ImpCHI) Square on the performance of six well-known classifiers: Random Forest, Decision Tree, Naive Bayes, Naive Bayes Multinomial, Bayes Net, and Artificial Neural Networks. These proposed techniques are quite important for improving classification of Arabic documents and can be regarded as a promising basis for the stage of text classification because it contributes to the classification of the texts into predefined categories. This combination method takes the advantages of more than one technique, which can produce better results in the final outcomes. The dataset employed in this paper includes 9055 Arabic documents that were collected from various Arabic resources. Based on their content, these documents were divided into twelve categories. Four performance evaluation criteria were used: the F-measure, recall, precision, and Time build model. The experimental results show that the use of ImpCHI square gives better classification results than the normal CHI square method with all studied classifiers, in terms of all used performance criteria.

Movie genre classification is a challenging task that has increasingly attracted the attention of researchers. The number of movie consumers interested in taking advantage of automatic movie genre classification is overgrowing, thanks to media streaming service providers’ popularization. In this paper, we addressed the multi-label classification of movie genres in a multimodal way. To this end, we created a dataset composed of trailer video clips, subtitles, synopses, and movie posters from 152,622 movie titles of the Movie Database (TMDb). Such a large dataset was carefully curated, organized, and made available as a contribution of this work. We labeled each movie of the dataset according to a set of eighteen genre labels. In the experimental evaluation performed in this paper, we computed different kinds of descriptors, such as Mel Frequency Cepstral Coefficients (MFCCs), Statistical Spectrum Descriptor (SSD), Local Binary Pattern (LBP) from spectrograms, Long-Short Term Memory (LSTM), and Convolutional Neural Networks (CNN). With these descriptors, we trained different monolithic classifiers using BinaryRelevance and ML-kNN techniques. Besides, we also explored the combination of classifiers/features using a late fusion strategy. The fusion of a LSTM trained on synopses and another LSTM trained on the movie subtitles provided our best results in F-Score (0.674) and AUC-PR (0.725) metrics. These results corroborate the existence of complementarity among classifiers trained on different sources of information in this field of application. As far as we know, this is the most comprehensive study developed in terms of diversity of multimedia sources of information to perform movie genre classification.

Pre-processing plays an essential role in disambiguating the meaning of short-texts, not only in applications that classify short-texts but also for clustering and anomaly detection. Pre-processing can have a considerable impact on overall system performance; however, it is less explored in the literature in comparison to feature extraction and classification. This paper analyzes twelve different pre-processing techniques on three pre-classified Twitter datasets on hate speech and observes their impact on the classification tasks they support. It also proposes a systematic approach to text pre-processing to apply different pre-processing techniques in order to retain features without information loss. In this paper, two different word-level feature extraction models are used, and the performance of the proposed package is compared with state-of-the-art methods. To validate gains in performance, both traditional and deep learning classifiers are used. The experimental results suggest that some pre-processing techniques impact negatively on performance, and these are identified, along with the best performing combination of pre-processing techniques.

Accessibility is a key requirement for any multimedia tool and application. With the current trend towards immersive experiences, such as Virtual Reality (VR) and 360o video, it becomes key that these environments are adapted to be fully accessible. However, until recently the focus has been mostly on adapting the existing techniques to fit immersive displays, rather than considering new approaches for accessibility designed specifically for these increasingly relevant media experiences.This paper surveys a wide range of 360o video players and examines the features they include for dealing with accessibility, such as Subtitles, Audio Description, Sign Language, User Interfaces and other interaction features, like voice control and support for multi-screen scenarios. These features have been chosen based on guidelines from standardization contributions, like in the World Wide Web Consortium (W3C) and the International Communication Union (ITU), and from research contributions for making 360° video consumption experiences accessible. The in-depth analysis has been part of a research effort towards the development of a fully inclusive and accessible 360° video player. The paper concludes by discussing how the newly developed player has gone above and beyond the existing solutions and guidelines, by providing accessibility features that meet the expectations for a widely used immersive medium, like 360° video.

In computer vision, scene text component recognition is an important problem in end-to-end scene text reading systems. It involves two major sub-problems - segmentation of such components into scene characters and classification of segmented characters into known character classes. Significant attention and increasingly focused research efforts are being put forth and reasonable progress in this field has already been made, though a diversity of challenges like background complexity, variety of text appearances, noise, blur, distortion and various other degradation and deformation issues are still left to address. In this paper, we present (i) a detail survey of scene component segmentation and/or recognition methods reported so far in literature, (ii) related datasets available for quantitative evaluation and benchmarking segmentation and/or recognition performance, (iii) comparative results and analysis over the reported methods, and (iv) discussion on open areas to be looked into in order to achieve the desired goal of end-to-end scene text recognition. Moreover, this paper provides an acceptable reference for researcher in the area of scene text components segmentation and recognition.

Aiming at the research of real-time data transmission and multi-scale image decomposition of embedded optical sensor array, the principle, method and fusion strategy of multi-sensor image fusion are studied comprehensively, thoroughly and systematically by combining the imaging characteristics of source image with multi-scale geometric analysis tools using machine learning algorithm. A new quality scalable video image coding framework is also proposed in this paper, which is implemented by a multi-scale online dictionary learning algorithm based on structured sparse video signals. For the purpose of different types of images and image fusion, a new high quality scalable video image coding framework based on machine learning algorithm is proposed on the basis of comprehensive analysis of prior information such as imaging mechanism of image sensor and imaging characteristics of source image. A multi-scale online dictionary learning algorithm based on machine learning for sparse video signal structure is proposed. Through the hierarchical structure of wavelet decomposition, the searching domain of online learning is optimized to a hierarchical sparse block, and its sparse representation coefficients are obtained by using machine learning sparse coding idea. The real-time data transmission of embedded optical sensor array based on machine learning and multi-scale image decomposition algorithm proposed in this paper have good fusion performance, which is of great significance for further research and engineering application of image fusion technology.

With the growth of data mining and machine learning approaches in recent years, many efforts have been made to generalize these sciences so that researchers from any field can easily utilize these sciences. One of the most important of these efforts is the development of data mining tools that try to hide the complexities from researchers so that they can achieve a professional output with any level of knowledge. This paper is focused on reviewing and comparing data mining and machine learning tools including WEKA, KNIME, Keel, Orange, Azure, IBM SPSS Modeler, R and Scikit-Learn to show what approach each of these methods has taken in the face of the complexities and problems of different scenarios of generalization of data mining and machine learning. In addition, for a more detailed review, this paper examines the challenge of network intrusion detection in two tools, Knime with graphical interface and Scikit-Learn with coding environment.

Social media platform like Twitter is one of the primary sources for sharing real-time information at the time of events such as disasters, political events, etc. Detecting the resource tweets during a disaster is an essential task because tweets contain different types of information such as infrastructure damage, resources, opinions and sympathies of disaster events, etc. Tweets are posted related to Need and Availability of Resources (NAR) by humanitarian organizations and victims. Hence, reliable methodologies are required for detecting the NAR tweets during a disaster. The existing works don't focus well on NAR tweets detection and also had poor performance. Hence, this paper focus on detection of NAR tweets during a disaster. Existing works often use features and appropriate machine learning algorithms on several Natural Language Processing (NLP) tasks. Recently, there is a wide use of Convolutional Neural Networks (CNN) in text classification problems. However, it requires a large amount of manual labeled data. There is no such large labeled data is available for NAR tweets during a disaster. To overcome this problem, stacking of Convolutional Neural Networks with traditional feature based classifiers is proposed for detecting the NAR tweets. In our approach, we propose several informative features such as aid, need, food, packets, earthquake, etc. are used in the classifier and CNN. The learned features (output of CNN and classifier with informative features) are utilized in another classifier (meta-classifier) for detection of NAR tweets. The classifiers such as SVM, KNN, Decision tree, and Naive Bayes are used in the proposed model. From the experiments, we found that the usage of KNN (base classifier) and SVM (meta classifier) with the combination of CNN in the proposed model outperform the other algorithms. This paper uses 2015 and 2016 Nepal and Italy earthquake datasets for experimentation. The experimental results proved that the proposed model achieves the best accuracy compared to baseline methods.

Counting-based secret sharing is becoming a vital efficient multimedia technique for raising the security of sensitive data especially when collective access to data are essential. Secret sharing distributes shares to participants forcing their joint availability in order to give permission. The share keys of the system deserve to be remembered and highly protected against intruders, presenting a challenging issue, noticing that shares are normally produced without giving participants any preference. Therefore, this research adds image steganography technique to the counting-based secret-sharing system to gain more applicable security. The paper proposes redistribution of LSB image steganography to embed share keys in colour cover image. The proposed method ensured that the shares hiding locations differ within cover images to increase the level of security. The paper analysis show that stego-image quality and security is attractive. It showed interesting results in terms of performance and payload capacity motivating this research to be a direction for coming improvements.

IBM Watson is one of the representative tools for speech recognition system which can automatically generate not only speech-to-text information but also speaker ID and timing information, which is called as Informatized Caption. However, if there is some noise in the voice signal to the IBM Watson API, the recognition performance is significantly decreased. It can be easily found in movies with background music and special sound effects. This paper aims to improve the inaccuracy problem of current Informatized Captions in noisy environments. In this paper, a method of modifying incorrectly recognized words and a method of enhancing timing accuracy while updating database in real time are suggested based on the original caption and Informatized Caption information. Experimental results shows that the proposed method can give 81.09% timing accuracy for the case of 10 representative animation, horror and action movies.

Multi-secret image sharing techniques (MSS) extend the secret image sharing schemes. The paper reports a novel MSS scheme designed to be collusion resistant. It starts with a brief introduction of the concept thereafter discussing the related works in the domain. Security is an implicit requirement for MSS techniques. There should be no possibility of collusion among the shareholders. No less than k shareholders for (k, n) MSS scheme should be having even a slight view of the secret from their shares, n being the total number of shareholders. The paper reviews and highlights the security issues like possibility of collusion, randomness issues or robustness against tampering attack in the state-of-the-art works. It proposes a new (n, n) MSS scheme in order to rectify the issues. The algorithm encrypts N different secret images to generate N noisy share images. It resolves the existing problems in a secure, and time efficient manner. A range of experiments specifically time complexity, noise, tampering, collusion and histogram analysis are performed for rigorous security analysis. The results, thus obtained indicated that the algorithm is efficient and preserves the security requirements in a much better way than the existing MSS schemes.

Classification of bacteria is essential in the medical diagnosis of infectious agents, their phylogenetic study, and their biotechnological exploitation for healthcare, food, industry, and agricultural sectors. Nevertheless, skilled experts and professional human-effort are necessary to identify and classify the bacteria manually. With the advancement of technology, now the task of recognizing details from digital stereomicroscopes is being performed by computers based on machine learning and computer vision technologies. Besides, machine learning methods include Deep Neural Networks (NN) has attained remarkable outcomes in the field of image classification recently. Furthermore, other machine learning methods except for NN methods already have acceptable performance. In this paper, we review the publications, which investigate the discrimination between bacteria genera and suborders based on macroscopic images via image processing and machine learning methods. The published research works in this regard are summarized, and the pros and cons of them are discussed. Moreover, the related databases and resources for this purpose are surveyed, and the lack of such data points in the global catalogue of microorganisms is criticized. In addition, in this paper, we have investigated an approach to automate the process of bacteria recognition and classification with the use of the Gabor transform and XGBoost classification method. We have used a dataset that includes microscopic images of three different Myxobacterial suborders. The trained model was able to recognize and classify all members of three different categories of bacteria, while the experimental results of prediction achieved an accuracy of around 91%, which has enhancement about 2% in term of accuracy.

In today’s knowledge driven society and technology driven economy, when data is considered to be one of the most important corporate resource of an organization, equally important is the security of the same to prevent unauthorized users to access and manipulate it. Watermarking being an appropriate solution for claiming authorization has prompted many researchers to work in this direction and accordingly a substantial amount of research work has been done using different schemes on various media types. The main impetus in writing this paper is to provide a broader view as how much work has been carried so far and what are the different dimensions that have been taken into consideration to watermark color images using discrete wavelet transformation. The paper will help prospective researchers to gain more insight about the work that has been done and pave a way for them to design and implement better techniques.

Image watermarking techniques are used to provide copyright protection and verify ownership of media/entities. This technique refers to the concept of embedding of secret data/information of an owner in a given media/entity for determining any ownership conflicts that can arise. Many watermarking approaches have been offered by various authors in the last few years. However, there are not enough studies and comparisons of watermarking techniques in soft computing environments. Nowadays, soft computing techniques are used to improve the performance of watermarking algorithms. This paper surveys soft computing-based image watermarking for several applications. We first elaborate on novel applications, watermark characteristics and different kinds of watermarking systems. Then, soft computing based watermarking approaches providing robustness, imperceptibility and good embedding capacity are compared systematically. Furthermore, major issues and potential solutions for soft computing-based watermarking are also discussed to encourage further research in this area. Thus, this survey paper will be helpful for researchers to implement an optimized watermarking scheme for several applications.

The mobile devices have entered the race of deploying high tech to the users but inherit a defect of limited battery storage. With the limitationof battery capacity, energy efficiency has been a major concern for Mobile Cloud Computing (MCC). The multimedia application comprises rich media that needs higher processing and computation, and the resource and computation are constrained. Energy-efficient transmission is a big concern because it equips the mobile device with high-end hardware components, but still, are far behind to the battery capacity and computation competence. This paper intends to introduce and investigate the different approaches/algorithms and tools that best fit to save resource utilization and energy consumption rationally. The algorithms like the Genetic Algorithm, Greedy scheduling algorithm, power-aware list-based scheduling, Offloading based task scheduling, media cloud distributed scheduling algorithm, etc. are discussed in this research. The data, Energy-efficient technique, and Cloud server response that defines each of the main components is introduced in this paper that concerns the implementation of the energy-efficient transmission in MCC. We propose these components that illustrate the workflow in the technology. A process when the data is perceived by the technique to adequately deliver a response to the mobile device. An energy-efficient transmission technique for MCC is a development towards the use of mobile cloud computing in this field working to counter the utilization of energy and resources that execute locally. This concept results in reducing the amount of data transmitted with the use of sharing the data externally between the tasks that will save energy. Through this paper, readers will understand the benefit of using the energy-efficient technique in the MCC. Also, the reader will understand the classification groups, validation criteria, future gaps of the 30 literature reviews about the technique, and how they intend to energy optimization in mobile devices.

Cryptography (encryption/decryption) is one of the prevailing mechanisms for protection of information (data or image) in the growing era of computerized exchange. It is the art of securing data/image by changing it into an unreadable format, called cipher text/image. Encryption mechanism is said to be efficient if it offers high security and robustness against attacks, and endow with very low correlation value between cipher and the original information. Enormous techniques and corresponding survey papers are available in literature considering only a few methods or parameters into account, but there is a stern need of investigation, which thoroughly considers vast variety of techniques and compares them in the light of numerous performance metrics under the influence of wide variety of probable threats. In view of the fact, this paper has taken account of almost all traditional, modern, chaotic, and quantum-chaotic based methods under the influence of prevalent intimidation and does a comprehensive investigation based on various performance metrics. To measure the efficacy, all the mechanisms are implemented in MATLAB-2014. Chaos and Quantum based algorithms are the superlative in comparison to others presented in literature under most of the extensive threats (attacks, noises etc.) and can resist brute force attacks due to large key space. In addition, suggestions for future scope have been given.

Face recognition is an efficient technique and one of the most preferred biometric modalities for the identification and verification of individuals as compared to voice, fingerprint, iris, retina eye scan, gait, ear and hand geometry. This has over the years necessitated researchers in both the academia and industry to come up with several face recognition techniques making it one of the most studied research area in computer vision. A major reason why it remains a fast-growing research lies in its application in unconstrained environments, where most existing techniques do not perform optimally. Such conditions include pose, illumination, ageing, occlusion, expression, plastic surgery and low resolution. In this paper, a critical review on the different issues of face recognition systems are presented, and different approaches to solving these issues are analyzed by presenting existing techniques that have been proposed in the literature. Furthermore, the major and challenging face datasets that consist of the different facial constraints which depict real-life scenarios are also discussed stating the shortcomings associated with them. Also, recognition performance on the different datasets by researchers are also reported. The paper is concluded, and directions for future works are highlighted.

Identifying a person based on their behavioral and biological qualities in an automated manner is called biometrics. The authentication system substituting traditional password and token for authentication and relies gradually on biometric authentication methods for verification of the identity of an individual. This proves the fact that society has started depending on biometric-based authentication systems. Security of biometric authentication needs to be reviewed and discussed as there are multiple points related to integrity and public reception of biometric-based authentication systems. Security and recognition accuracy are the two most important aspects which must be considered while designing biometric authentication systems. During enrollment phase scanning of biometric data is done to determine a set of distinct biometric feature set known as biometric template. Protection of biometric templates from various hacking efforts is a topic of vital importance as unlike passwords or tokens, compromised biometric templates cannot be reissued. Therefore, giving powerful protection techniques for biometric templates and still at that very moment preparing great identification accuracy is a good research problem nowadays, as well as in the future. Furthermore, efficiency under non-ideal conditions is also supposed to be inadequate and thus needs special attention in the design of a biometric authentication system. Disclosure of various biometric traits in miscellaneous applications creates a severe compromise on the privacy of the user. Biometric authentication can be utilized for remote user authentication. In this case, the biometric data of users typically called templates are stored in a server. The uniqueness and stability of biometrics ended it useful over traditional authentication systems. But, a similar thing made the enduring harm of a user’s identity in biometric systems. The architecture of the biometric system leads to several hazards that lead to numerous security concerns and privacy threats. To address this issue, biometric templates are secured using several schemes that are categorized as biometric cryptosystems, cancelable biometrics, hybrid methods, Homomorphic Encryption, visual cryptography based methods. Biometric cryptosystems and cancelable biometrics techniques provide reliable biometric security at a great level. However, there persist numerous concerns and encounters that are being faced during the deployment of these protection technologies. This paper reviews and analyses various biometric template protection methods. This review paper also reflects the limitations of various biometric template protection methods being used in present times and highlights the scope of future work.

Gaming video streaming services are growing rapidly due to new services such as passive video streaming of gaming content, e.g. Twitch.tv, as well as cloud gaming, e.g. Nvidia GeForce NOW and Google Stadia. In contrast to traditional video content, gaming content has special characteristics such as extremely high and special motion patterns, synthetic content and repetitive content, which poses new opportunities for the design of machine learning-based models to outperform the state-of-the-art video and image quality approaches for this special computer generated content. In this paper, we train a Convolutional Neural Network (CNN) based on an objective quality model, VMAF, as ground truth and fine-tuned it based on subjective image quality ratings. In addition, we propose a new temporal pooling method to predict gaming video quality based on frame-level predictions. Finally, the paper also describes how an appropriate CNN architecture can be chosen and how well the model performs on different contents. Our result shows that among four popular network architectures that we investigated, DenseNet performs best for image quality assessment based on the training dataset. By training the last 57 convolutional layers of DenseNet based on VMAF values, we obtained a high performance model to predict VMAF of distorted frames of video games with a Spearman’s Rank correlation (SRCC) of 0.945 and Root Mean Score Error (RMSE) of 7.07 on the image level, while achieving a higher performance on the video level leading to a SRCC of 0.967 and RMSE of 5.47 for the KUGVD dataset. Furthermore, we fine-tuned the model based on subjective quality ratings of images from gaming content which resulted in a SRCC of 0.93 and RMSE of 0.46 using one-hold-out cross validation. Finally, on the video level, using the proposed pooling method, the model achieves a very good performance indicated by a SRCC of 0.968 and RMSE of 0.30 for the used gaming video dataset.

Decision making by analyzing the underlying sentiment has become one of the challenging task with the explosion of rich source of user generated, diverse contents on the web. Sentiment analysis can ease the process of obtaining an overall sentiment by processing millions of reviews or documents altogether. A review or any textual data consists of numerous keywords, which hold different weightage based on various factors. An efficient ranking technique for those keywords is proposed in this paper in order to aid in the sentiment analysis process. A co-occurrence graph based statistical approach is adopted in this paper to find the global rank of the keywords. A novel node weighting technique is proposed, which will be used for the improvisation of the state of art method: Node and Edge Rank (NE-Rank) along with degree, to rank the keywords. The algorithm considers five different influential parameters to propose the node weighting technique. Also, a keyword may hold bi-polarity and depict different polarity according to the domain of application. Therefore, this paper proposes a novel, well organized and efficient sentiment analysis model using a graph based keyword ranking and domain specific rank based polarity assignment algorithm. The role or impact of an important keyword will be always more in comparison to a weaker one for determining the polarity of the review. Thus, the rank based polarity assignment technique is proposed with the use of the global ranks of keywords, to solve the domain dependency problem of the keywords. The proposed model is evaluated and validated using four different existing models for four different customer review datasets.