Reviewerly/exHarmony huggingface dataset:
Authenticated content distribution framework for digital rights management systems with smart card revocation

Summary The growing features of multimedia distribution across multiple smart devices have attained significant attention of many industries and users. In this context, digital rights management (DRM) is the system, which tries to ensure authorized content distribution. For much diverse and productive research in content distribution, DRM is paying attention to provide a better DRM protocol according to current requirements. But the violation of copyright protection of digital content (video, audio, and secret documents) still has a significant issue of concern. Not only solely copyright protection but also stolen smart cards which keep the license and more specific information of a user is also an attentive concern of DRM users. In this paper, we introduced an authenticated key agreement protocol which maintains secure and authorized content distribution with successful smart card revocation. Moreover, the proposed scheme has simulated under AVISPA tool, which informally confirms the security of the protocol. The formal proof security is given in the random oracle model and then done the performance evaluation. The comparison with the relevant existing schemes shows the advantage of the proposed scheme. It identified that the proposed protocol achieves all the desirable security and performance attributes with valid smart card revocation.
A new non-deterministic drama manager for adaptive interactive storytelling

Adaptive interactive storytelling is a resource used to guide the player through the game narrative in a way he/she can understand, in his/her own unique way, the multifaceted nature of the story being told. In interactive storytelling, the player is presented with a set of options to help directing the narrative, and the Drama Manager (DM) is responsible for controlling the narrative trajectories the user will experience. This work proposes Aldabra, a DM based on a genetic algorithm that is able to present non-deterministic plot lines to the player according to his/her model. The proposed DM will follow the Anthropological Structures of the Imaginary proposed by Durand to guide the narrative, which will be the basis for a player model. Experiments were performed both with synthetic datasets as well as with a group of players, evaluating important properties such as Adaptability, Replayability, Authorial Control and Player Autonomy. The quantitative results show that the GA finds subsets of plot lines that are not far from the optimum while increasing replayability. Qualitative analyses of the players preference considering the three regiments that defined Durand’s theory demonstrate that the theory is appropriate to model the players preferences in interactive storytelling.
Short Paper: Credit Card Fraud Detection using LightGBM with Asymmetric Error Control

Credit card frauds, while only account for about 0.1% of all card transactions, resulting in huge financial and reputational losses. Challenges of detecting credit card frauds are from the imbalanced nature of the recorded data, the need for controlling the trade-off between miss detection and false alarm, and incomplete information due to confidentiality requirements. In this paper, we propose an innovative fraud detection framework implementing the LightGBM method under the Neyman-Pearson paradigm, which enables asymmetric error control. Performance measurement metrics are also introduced to evaluate different classification frameworks. We can successfully keep the miss detection rate under the desired upper bound and control false alarm at the same time.
Image Steganography Based on LSB Matching and Image Enlargement

Steganography plays a main role in data security field. It aims to cover important data within a cover medium such that the data existence remains confidential. The secret data is hidden in such a way that no one knows about it except the sender or the intended recipient. The least significant bit (LSB) substitution method is the most popular image steganography method for hiding a secret data in an image with high payload, while the human visual system (HVS) cannot notice any distortion in the resulted stego image. The proposed scheme employs the LSB matching method as a fundamental stage besides using the principle of an image enlargement. Image enlargement is used to extend the stego image dimensions, adding this step will distribute the secret data over the resulting image. Experimental results showed that the proposed method significantly had a high capacity with preserve image quality. Moreover, the secret data was embedded in non-consecutive pixel positions, which provides a high diffusion and lead to resist the image steganalysis effectively.
Cloud‐assisted secure and conjunctive publish/subscribe service in smart grids

The publish/subscribe (P/S) service on Advanced Metering Infrastructure (AMI) servers of smart grid need to deal with huge amount of data, which may lead to data burst on AMI servers and serious server crash. Moreover, for protecting data security, sensitive data must be encrypted before being published. It obstacles traditional data utilisation based on plaintext P/S service. Thus, enabling an encrypted data-based P/S service is of paramount importance. Considering the huge amount of data and subscribers, it is necessary to allow conjunctive subscriptions containing mixtures of keywords, numeric data etc., and return data according to a reasonable access control mechanism (ACM). In this study, the authors propose a cloud-assisted secure conjunctive publish/subscribe protocol to challenge the encrypted data-based P/S service on AMI servers. To overcome the data burst, the P/S computation tasks are shifted from AMI servers to the cloud. To support conjunctive P/S operations in a reasonable ACM, a prefix-based membership verification algorithm combining with the ciphertext policy attribute-based encryption is explored. The proposed protocol is proved to be secure against chosen keyword/plaintext attacks under formally defined security models. Experiments on the real-world data set further show proposed protocol indeed introduce low overhead on computation and communication.
A High speed Area Efficient Compression technique of Dadda multiplier for Image Blending Application

Multimedia and Image processing are the two widely used applications in Digital signal processing domain through which the information/data will be delivered in a compact size. The interesting concept lies with approximate computing where the arithmetic designs are performed with reduced power, area and delay. In this paper, the analyses of four different compression techniques are done in a 8X8 dadda multiplier and it is further applied on image blending where pixel by pixel multiplication is been carried out. While comparing the results of both existing and proposed designs, the proposed design operates with greater speed, consumes less power and occupies less area. In addition to that, the proposed multipliers is well suited for image blending with respect to Peak Signal to Noise Ratio (PSNR).
Audit and Compliance in Service Management using Blockchain

Auditing is the inspection of the processes and work done during a period of time whereas compliance is the process of complying to the rules. In Service management space various organizations provide and use various services in the form of transactions and are dependent on each other. An organization may not use only one service to provide its services to the client, it may use multiple services like database, storage, processing, etc. Due to this involvement of multiple parties in the business, it is very tedious to manage these licenses and also more difficult to audit this system. Each organization have stored information at their own data centers, hence it is very hard to guarantee the trust between these organizations. To address this issue we propose a blockchain based solution to ensure transparency and security in the transactions between these organizations.
Web-Based Movie Recommender System

Recommender systems guide users to find and cull items such as restaurants, books, and movies from the huge collection of available options on the Web. Based on the user's taste and preferences, recommender system recommends a set of items from a large set of available items. Recommendation systems include intelligent aides for filtering and selecting Web sites, news stories, TV listings, and alternative info. The users of such systems typically have various conflicting desires, variations in personal preferences, social and academic backgrounds, and personal or skilled interests. As a result, it looks fascinating to possess personalized intelligent systems that suit individual preferences. The need for personalization has proliferated the event of systems that adapt themselves by ever-changing their behavior supported by the inferred characteristics of the user interacting with them. In this paper, we develop a Web-based movie recommender system that recommends movies to users based on their profile using different recommendation algorithms. We also compare various recommendation algorithms such as singular value decomposition, alternating least squares and restricted Boltzmann machines.
Mixture representations of noncentral distributions

With any symmetric distribution μ on the real line we may associate a parametric family of noncentral distributions as the distributions of (X+δ)2,δ=0, where X is a random variable with distribution μ. The classical case arises if μ is the standard normal distribution, leading to the noncentral chi-squared distributions. It is well known that these may be written as Poisson mixtures of the central chi-squared distributions with odd degrees of freedom. We obtain such mixture representations for the logistic distribution and for the hyperbolic secant distribution. We also derive alternative representations for chi-squared distributions and relate these to representations of the Poisson family. While such questions originated in parametric statistics they also appear in the context of the generalized second Ray–Knight theorem, which connects Gaussian processes and local times of Markov processes.
A Study on Syntactic Complexity and Text Readability of ASEAN English News

English is the most widely used language in the world. With the spread and evolution of language, there are differences in the English text expression and reading difficulty in different regions. Due to the difference in the content and wording, English news in some countries is easier to understand than in others. Using an accurate and effective method to calculate the difficulty of text is not only beneficial for news writers to write easy-to-understand articles, but also for readers to choose articles that they can understand. In this paper, we study the differences in the text readability between most ASEAN countries, England and America. We compare the textual readability and syntactic complexity of English news texts among England, America and eight ASEAN countries (Indonesia, Malaysia, Philippines, Singapore, Brunei, Thailand, Vietnam, Cambodia). This paper selected the authoritative news media of each country as the research object. We used different indicators including Flesch-Kincaid Grade Level (FKG), Flesch Reading Ease Index (FRE), Gunning Fog Index (GF), Automated Readability Index (AR), Coleman-Liau Index (CL) and Linsear Write Index (LW) to measure the textual readability, and then applied L2SCA to analyze the syntactic complexity of news text. According to the analysis results, we used the hierarchical clustering method to classify the English texts of different countries into six different levels. Moreover, we elucidated the reasons for such readability differences in these countries.
A Study on Real-Time Edge Computed Occupancy Estimation in an Indoor Environment

Sensing the presence of occupants and estimating the occupancy level in an indoor environment are the fundamental requirements for various applications performing remote monitoring, home automation and optimal resource planning. Data generated from a set of passive heterogeneous sensors deployed for this purpose are multimodal and streaming in nature. This work aims to formulate the human occupancy estimation in an indoor environment as a multi-class problem and proposes a edge-based data management framework for human occupancy estimation. The proposed framework is low-cost and light-weight in addition to being capable of performing real-time inference. Also testbed experimentation results is provided to justify the performance of the proposed scheme.
Reduction of Noises From Degraded Document Images Using Image Enhancement Techniques

Evolution of digital devices and computers makes an increasing attraction in document image analysis. Many of the paper documents have been transferred and stored using digital devices in large manner. In this work, we have done image enhancement techniques to reduce the noises from degraded document images. Here, we have taken sample images from Document Image Binarization Contest (DIBCO) dataset images. We have done contrast stretching, histogram equalization, noise filtering, Laplacian transformation, global and local thresholding methods to remove show-through noise, uneven illumination noise and shot noise from degraded document images using OpenCV open source software. Further, performance metrics were estimated to understand the efficiency of the above methods in removing the aforementioned noises.
Trajectory Type Prediction and Multi-Target Tracking

Multiple target tracking is well known computer vision problem due to its wide range of application in industrial and biomedical domains. Significant amount of research has been done to tackle the problem, but still it is challenging to design robust tracking system that can provide solutions for many real-world tracking applications. In this paper, we present two-stage approach for tracking multiple target trajectories in video sequences. In the first stage, machine learning is utilized to predict the type of the trajectory on which a target is travelling. We selected Bidirectional Long Short-Term Memory Recurrent Neural Network to classify trajectories generated by the targets. The network is beneficial for learning bidirectional long-term dependencies between time steps of sequential data. In the second stage, polynomial curve (arc) of 2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">nd</sup> degree is being fit to the location data of previous six video frames and polynomial curve (arc) of 3 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">rd</sup> degree is being fit to the velocity curve computed from the six previous frames. This information helps to extrapolate the future velocity of the target and its future location. We demonstrate the tracking abilities of our new approach and estimate the error of prediction on simulated trajectory data and on real-world videos.
A Look into Privacy-Preserving Blockchains

Blockchain is a revolutionary technology promising a new generation of decentralized, transparent and tamper-proof database systems. However, one of the main challenges of the emerging blockchain systems remains to address data privacy. In Bitcoin and similar e-cash systems, privacy is limited to anonymity and addressed by using cryptographic pseudonyms for identification and authentication of parties. Privacy has more breadth in systems maintaining actual data such as medical records or energy meters. In this paper, we look into recently proposed privacy-preserving blockchains, analyze their strengths and weaknesses, and highlight research questions requiring future investigation. We conclude that privacy-preserving blockchains are still in their infancy and more research is definitely needed in this area.
An Effective Modification of Play Fair Cipher with Performance Analysis using 6X6 Matrix

Researchers have shown great interest in expanding and modifying the Playfair Cipher algorithm to overcome its pre-existing demerits. An explosion of research in cryptography for Playfair cipher occurred the last decade. While the inability of encrypting numeric characters exists in traditional Playfair Cipher, the algorithm is incompatible with many present technologies. Even for the encryption of a proper information in the form of human expressions, encryption of spaces between the words is needed as post processing of the decrypted message remains confusing without the feature. This paper proposes a modification of Playfair Cipher which is able to encrypt alphanumeric characters as well as spaces. The paper reaches the goal by modifying the dimension of the key matrix from 5 by 5 to 6 by 6 and introducing a method to replace spaces with configured digraphs from the generated key matrix. It is believed that introducing the mentioned features can prevent frequency attacks in Playfair Cipher. In this era of Internet of Things, the small devices need algorithms with lower memory consumption. Updated Playfair cipher can be implemented to aid to that.
E-trade Management System Architecture

In this work we present the model for construction of e-trade management system architecture which consists of two basic blocks: the landing Web-pages management unit of the online store is responsible for interacting with the user, receiving orders and collecting primary information and Web-enterprise management unit, responsible for processing orders, their delivery, and all enterprise's business processes realization. We have focused our attention to developing an architecture model for e-trady management system. A service-oriented architecture (SOA) using enterprise service bus (ESB) concept is offered for architectural implementation. In developed e-trade management system architecture we used the method of integrating complex information systems within the information environment based on SOA and ESB technologies, which represents an independent, integrative subsystem for relatively integrated information systems. Typical information systems that belong to the e-trade system are used as services. These web-based services should be developed and used to enhance ESB efficiency. Such web-services implement SOAP and use WSDL (Web Services Description Language), as well as UDDI specification (Universal Description, Discovery and Integration). At that, each typical information system is simultaneously data supplier and data consumer. Each information system handles its own data class, while being able to both transmit and receive, adjust and modify data according to operational information from other information systems within main business processes.
Case Study: Extenuation of XSS Attacks through Various Detecting and Defending Techniques

Cross-Site Scripting attack (XSS) is the foremost vulnerability identified in the world of the web today. XSS attacks permit an intruder to execute the malevolent code and tainted source on the user browser, which results in various effects like cookies stealing, password attack, hacking credit/debit card numbers or any PIV’s of consumers etc. A detailed analysis of the XSS exploitation along with its detection and protection scheme has been discussed in this paper. The case study concerns with the existing methodologies and approaches used to detect and mitigate XSS attacks and its vulnerabilities. It talks about both the client and server side detection methods with static and dynamic approaches which have been presented so far to detect the attack. Also, it includes the XSS Defense methods and tools used to protect the client as well as the server. The motive of this paper is to give an exclusive look over the extenuation of XSS attacks through various detecting and defending techniques.
Multi-Modal Pyramid Feature Combination for Human Action Recognition

Accurate human action recognition remains a challenging task in the field of computer vision. While many approaches focus on narrow image features, this work proposes a novel multi-modal method that combines task specific features (action recognition, scene understanding, object detection and acoustic event detection) for human action recognition. This work encompasses two contributions: 1) The introduction of a feature fusion block that uses a gating mechanism to perform attention over features from other domains and 2) A pyramidal feature combination approach that hierarchically combines pairs of features from different tasks using the previous fusion block. The richer features generated by the pyramid are used for human action recognition. This approach is validated using a subset of the Moments In Time dataset, resulting in an accuracy of 35.43%.
Deep Convolutional Arabic Sentiment Analysis with Imbalanced Data

Deep Convolutional Neural Networks (CNNs) have shown prominent performance in different NLP tasks. A basic factor in such performance is the huge amount of data used for learning. On the other hand data sets with large size, high quality and full representation needed for text analysis tasks are rarely found. In most cases existent data sets encounter class imbalance problem where data entries belong to one category is much larger than data entries represent another category. In this paper CNN performance is investigated under imbalance condition in Arabic sentiment analysis using character level representation. A data set with highly imbalanced representation of sentiment polarity classes is utilized for the investigation. Algorithm related and data related approaches are implemented to process the imbalance problem. Multiple CNN s with various configurations are tested applying cost-sensitive learning, under-sampling and oversampling techniques.
Zero-Knowledge Proofs Do Not Solve the Privacy-Trust Problem of Attribute-Based Credentials: What if Alice Is Evil?

Zero-knowledge schemes have recently become a popular attempt to offer users privacy in an attribute-based credential system. In this article, we do not contest the mathematics of these schemes; we assume it is logically sound. Instead, we draw attention to the trade-off that is made when employing cryptography instead of trusted parties to protect user privacy. We assert that, for these approaches to create the trust required by credential verifiers, they must introduce mechanisms that limit their utility and create significant privacy risk to the user that cuts against data minimization goals. Greater trust must be placed in the shelf life of cryptography to prevent the user from being unwantonly correlated than alternative approaches. Just as we would discourage storing encrypted private data on public blockchains, we discourage this approach here. Lastly, this article introduces the concept of a trusted witness which provides privacy for honest users and solves the privacy-trust problem without the disadvantages of the zero-knowledge approach.
Articulatory Features Based TDNN Model for Spoken Language Recognition

In order to improve the performance of the Spoken Language Recognition (SLR) system, we propose an acoustic modeling framework in which the Time Delay Neural Network (TDNN) models long term dependencies between Articulatory Features (AFs). Several experiments were conducted on APSIPA 2017 Oriental Language Recognition(AP17-OLR) database. We compared the AFs based TDNN approach to the Deep Bottleneck (DBN) features based ivector and xvector systems, and the proposed approach provide a 23.10% and 12.87% relative improvement in Equal Error Rate (EER). These results indicate that the proposed approach is beneficial to the SLR task.