Reviewerly/exHarmony huggingface dataset:
Chaos-Based Content Distribution Framework for Digital Rights Management System

Multimedia contents are digitally utilized these days. Thus, the development of an effective method to access the content is becoming the topmost priority of the entertainment industry to protect the digital content from unauthorized access. Digital rights management (DRM) systems are the technique that makes digital content accessible only to the legal rights holders. As the Internet of Things environment is used in the distribution and access of digital content, a secure and efficient content delivery mechanism is also required. Keeping the focus on these points, this article proposes a content distribution framework for DRM system using chaotic map. Formal security verification under the random oracle model, which uncovers the proposed protocol's capability to resist the critical attacks is given. Moreover, simulation study for security verification is performed using the broadly accepted “automated validation of Internet security protocols and applications,” which indicates that the protocol is safe. Moreover, the detailed comparative study with related protocols demonstrates that it provides better security and improves the computational and communication efficiency.
End-to-End Deep Conditional Imitation Learning for Autonomous Driving

Autonomous urban driving has a been rising problem since decades because of interactions with very complex environment. The traditional modular pipeline, rule-based algorithms, has not presented yet an efficient model to rely on, because it can not cover the very large possible scenarios space. Machine Learning techniques like supervised learning or imitation learning and Reinforcement learning have initial promising results with better performance. We propose an end-to-end Deep Conditional Imitation Learning model for autonomous driving inspired by both of Intel and Nvidia. The feature extraction part for Intel is replaced with Nvidia's model. Our proposed model outperforms Intel's architecture performance on CARLA Simulator, and overgeneralizes on various towns with different weather conditions.
Augmented reality in health and medicine

As augmented reality (AR) technologies have become increasingly accessible, there are a range of industries that are interested in adopting and implementing AR. One field in particular that is actively interested in AR is for health and medicine. While there have been several studies about AR and health, the ways in which these studies conceptualize AR and the contexts in which they apply AR vary greatly. This chapter reviews the different areas of literature that have utilized AR, to see where there are commonalities and differences. Furthermore, this chapter illuminates how AR in particular could influence health outcomes based on its unique technical features and affordances. Based on this review, this chapter discusses several promising areas for AR and health and medicine, as well as important gaps and directions for future research.
Prevention of catastrophic interference and imposing active forgetting with generative methods

Artificial neural networks experience serious catastrophic forgetting (or interference) when information is learned sequentially. A significant effort in the machine learning community is devoted to the solution of this problem. Many approaches to overcome the catastrophic interference (CI) find parallels with an organization of the human memory system. In this paper, we provide a review of biologically inspired approaches for CI prevention. The main emphasis is made on the development of methods inspired by generative properties of the brain. We developed and tested several methods for preventing CI using an artificial dataset generated on the base of previous experience of neural network. The proposed methods include the activation maximization approach, the method based on Bayesian learning, and the method based on generative neural networks. The methods based on a combination of episodic memory (several stored samples) and semantic memory (sampling of posterior probability function) show superiority compared to other recent methods devoted to CI prevention. Based on generative approaches, the biologically plausible mechanisms of active forgetting and memory reconsolidation are also demonstrated. The proof of concept experiments were performed on several publicly available datasets.
Phase-coding quantum-key-distribution system based on Sagnac–Mach-Zehnder interferometers

Stability and robustness are important criteria to evaluate the performance of a quantum-key-distribution (QKD) system in real-life applications. However, the inherent birefringence effect of the fiber channel and disturbance caused by the variation of the deployment environment of the QKD system tremendously decrease its performance. To eliminate this adverse impact, we propose a polarization-insensitive phase-coding QKD system based on Sagnac--Mach-Zehnder interferometers. Verified by theoretical analysis and experimental tests, this QKD system is robust against channel polarization disturbance. The robustness and long-term stability of the QKD system is confirmed with a 10-day continuous operation over a 12.6-dB channel, which consists of a 50-km fiber spool and a polarization scrambler (2 rad/s). As a result, an average quantum bit error rate of $0.958%$ and a sustained secure key rate of 3.68 kbps are obtained. Moreover, the secure key rate of the QKD system for a typical channel loss of 10 dB reaches 6.89 kbps, and the achievable maximum transmission distance exceeds 125 km.
A Java Framework for Smart Contracts

This article defines a framework for programming, in Java, smart contracts over blockchain. The framework consists of a restricted runtime and of an instrumentation procedure for classes that need to be persisted to blockchain, for payable contract methods and for gas metering. This instrumentation abstracts away any difference between storage and memory data location, which is at the origin of tricky semantical issues and bugs in Solidity. Moreover, this framework allows one to leverage, in a transparent way, existing expertise and tools from the Java world, in order to build smart contracts in a simple and comfortable way. The resulting contracts are strongly-typed and work over a shared storage, that allows simple intercontract communication. This makes it easy to install libraries or microservices in blockchain.
Learning-Based Model to Fight against Fake Like Clicks on Instagram Posts

Online social networks (OSN) are one of the favorite places where people share posts like their photos, videos, and text to gain popularity. On the other hand, the marketing industry tries to gain the popularity of their advertisement using such OSNs. Popularity of a particular post depends on the number of likes received by that post. To increase one's social worth, people try to use this market by artificially increasing the likes on their posts. There is a lack of research in the current literature on Instagram which is one of the growing OSNs. Our work focuses on detecting valid and fake like of posts with the application of learning model taking into consideration several popular factors. We developed an automated learning model to detect fake liking behavior on the Instagram post. The learned model can accurately differentiate between the legitimate and fake liker with an accuracy of 97% with ensemble-based learning model and also autoencoder is used to detect bots activity.
Using Peer Code Review to Support Pedagogy in an Introductory Computer Programming Course

This full research category paper reports the result of an experiment that designed and implemented peer code review (PCR) to help introductory computer science (CS1) students understand programming concepts and improve their programming skills. Instructors at North Dakota State University have observed that students enrolled in CS1 programming course experience difficulty understanding programming concepts. We performed an empirical study that evaluated if PCR can help address the problem. We analyzed data collected from the PCR session, code development activity and end-of-study questionnaire. Our result provides insights into the most and least reported error types. Students were able to systematically improve the review output. More importantly, PCR had a significant positive impact on students' performance when developing their own code. PCR can be an effective teaching tool that when used in CS1 programming course can improve students' understanding of programming concepts and improve their programming skills.
Primal path algorithm for compositional data analysis

We consider the LASSO estimator for compositional data in which covariates are nonnegative, and their sum is always one. Due to the linear constraint of the regression coefficients caused by the sum to one condition, standard algorithms for LASSO cannot be applied directly to compositional data. Hence, a specific regularized regression model with linear constraints is commonly used. However, linear constraints incur additional computational time, which becomes severe in high-dimensional cases. Additionally, the exact computation for the regression is not investigated under existing methods. In this paper, we first propose an exact solution path algorithm for a l1 regularized regression with high-dimensional compositional data and extend to a classification model. We also compare its computational speed with that of previously developed algorithms and then apply the proposed algorithm to analyzing income inequality data in economics and human gut microbiome data in biology. By analyzing simulated and real data sets, we illustrate that our specialized algorithm is significantly more efficient than the generalized LASSO algorithm for compositional data.
An optimal hardware implementation of the KECCAK hash function on virtex-5 FPGA

Security has become a very demanding parameter in today’s world of speed communication, one of the methods to ensure information integrity is the use of hash function which generates a stream of bytes (hash) which must be unique. The Keccak hash function is one of more than fifty candidates accepted by NIST for the SHA-3 hash function competition. In this article, an optimal implementation of this function will be presented. This implementation intended to reducing circuit complexity, and increasing performance hardware implementations of this cryptographic hash function. The proposed design is coded in VHDL and implemented on Virtex 5 FPGA.
An Evolutionary Approach to Variational Autoencoders

Variational autoencoders are an important tool in the domain of generative data models and yet they remain difficult to design due to a lack of intuition as to how to best design the corresponding encoder and decoder neural networks in addition to determining the size of the latent dimension. Furthermore, there is no definitive guidance on how one should structure these networks. Designing an effective variational autoencoder typically requires fine-tuning and experimenting with different neural network architectures and latent dimensions, which, for large datasets, can be costly both in time and money. In this work we present an approach for designing variational autoencoders based on evolutionary neural architecture search. Our technique is efficient, avoiding redundant computation, and scalable. We explore how the number of epochs used during the neural architecture search affects the properties of the resulting variational autoencoders as well as study the characteristics of the learned latent manifolds. We find that evolutionary search is able to find highly performant network even when the networks are evaluated after only two epochs of training. Using this insight we are able to dramatically reduce the overall computational requirements of our neural architecture search system applied to variational autoencoders.
Storage and Rack Sensitive Replica Placement Algorithm for Distributed Platform with Data as Files

Distributed File System (DFS) is a key component in cloud and data center networking. Frequent hardware failure and network bottlenecks in the underlying infrastructure degrade the performance significantly. Replica technique provides enhanced fault tolerance by storing multiple replicas of a single data block. In the Hadoop platform, the Hadoop Distributed File System (HDFS) handles data storage and provides replica placement services. The Default HDFS replica engine adopts a simple rack aware policy and is designed to improve fault tolerance by storing data blocks in multiple racks. However, the HDFS replica engine does not consider key performance indicators of data center resources such as rack utilization and node storage utilization. Furthermore, in HDFS data is stored as uniformly divided small-sized blocks, which increases traffic flow during the entire file access, therefore degrading the response time. In this research, we propose a Storage and Rack Sensitive (SRS) replica placement algorithm that aims at improving the rack and storage utilization of data center resources. The proposed algorithm also attempts to optimize traffic flow during file access by storing data as original files instead of small uniform blocks. Experimental results of the proposed SRS algorithm are compared against the default HDFS replica distribution and significant improvement on rack-utilization and storage-utilization were observed. Furthermore, latest literature confirms that the “Data as a File” approach indeed decreases the amount of data flow caused by file access traffic.
Building an Integrated Comment Moderation System – Towards a Semi-automatic Moderation Tool

The past decade has been characterized by a strong increase in the use of social media and a continuous growth of public online discussion. With the failure of purely manual moderation, platform operators started searching for semi-automated solutions, where the application of Natural Language Processing (NLP) and Machine Learning (ML) techniques is promising. However, this requires huge financial investments for algorithmic implementations, data collection, and model training, which only big players can afford. To support smaller or medium-sized media enterprises (SME), we developed an integrated comment moderation system as an IT platform. This platform acts as a service provider and offers Analytics as a Service (AaaS) to SMEs. Operating such a platform, however, requires a robust technology stack, integrated workflows and well-defined interfaces between all parties. In this paper, we develop and discuss a suitable IT architecture and present a prototypical implementation.
Classification of regional and genre varieties of Chinese: A correspondence analysis approach based on comparable balanced corpora

Abstract This paper proposes a robust text classification and correspondence analysis approach to identification of similar languages. In particular, we propose to use the readily available information of clauses and word length distribution to model similar languages. The modeling and classification are based on the hypothesis that languages are self-adaptive complex systems and hence can be classified by dynamic features describing the system, especially in terms of distributional relations of constituents of a system. For similar languages whose grammatical differences are often subtle, classification based on dynamic system features should be more effective. To test this hypothesis, we considered both regional and genre varieties of Mandarin Chinese for classification. The data are extracted from two comparable balanced corpora to minimize possible confounding factors. The two corpora are the Sinica Corpus from Taiwan and the Lancaster Corpus of Mandarin Chinese from Mainland China, and the two genres are reportage and review. Our text classification and correspondence analysis results show that the linguistically felicitous two-level constituency model combining power functions between word and clauses effectively classifies the two varieties of Chinese for both genres. In addition, we found that genres do have compounding effect on classification of regional varieties. In particular, reportage in two varieties is more likely to be classified than review, corroborating the complex system view of language variations. That is, language variations and changes typically do not take place evenly across the board for the complete language system. This further enhances our hypothesis that dynamic complex system features, such as the power functions captured by the Menzerath–Altmann law, provide effective models in classifications of similar languages.
Non-Stochastic Hypothesis Testing with Application to Privacy Against Hypothesis-Testing Adversaries

We consider privacy against hypothesis-testing adversaries within a non-stochastic framework. We develop a theory of non-stochastic hypothesis testing by borrowing the notion of uncertain variables from non-stochastic information theory. We define tests as binary-valued mappings on uncertain variables and prove a fundamental bound on the performance of tests in non-stochastic hypothesis testing. We use this bound to develop a measure of privacy. We then construct reporting policies with prescribed privacy and utility guarantees. The utility of a reporting policy is measured by the distance between reported and original values. We illustrate the effects of using such privacy-preserving reporting polices on a publicly- available practical dataset of preferences and demographics of young individuals with Slovakian nationality.
Face Recognition: Challenges and Issues in Smart City/Environments

Smart city challenges, like increased traffic, risk to public safety, effective law enforcement and the smart environment challenges on improving personalized services such as health care and home environment need person identification. Face recognition has proved to be useful and amicable bio-metric for smart city and smart environment challenges. In this paper, we review the use cases pertaining to smart city and smart environment leading to use case specific requirements on Face recognition. We describe the open challenges in anti-spoofing and standardization to make face recognition a fool proof system. For applications with demand for low power, we show that with proper considerations on training data, cost function, and model architecture we could build a low-complex CNN model with reasonable accuracy (91.4% on LFW data set).
FPGA Floating Point Fractional-Order Chaotic Map Image Encryption

Chaotic Logistic maps are iterative functions that are highly sensitive to initial conditions. They can be used for pseudorandom number key generation (PRNG) in encryption systems. Generalizing the conventional logistic map to the fractional-order domain adds extra degrees of freedom in designing larger key spaces enhancing the encryption security levels. The hardware implementation of the fractional-order map is a challenging task to achieve high accuracy and precision. For this reason, IEEE 754 Floating Point representation is employed in this paper to introduce the generalized fractional-order logistic map implementation on Virtex 5 FPGA achieving clock frequency of 260.4 MHz. The integration of the designed PRNG block in an image encryption system is also presented.
Autonomous Robot Navigation in Dynamic Environment Using Deep Reinforcement Learning

Compared to traditional control methods, deep reinforcement learning (DRL) has the ability to learn how to solve complex tasks in a dynamic environment simply by collecting experience. In this paper, we study the application of DRL method in robotic autonomous control with detection capability in simulated dynamic environment. More specifically, we have adopted Deep Q Network (DQN), double DQN and dueling DQN algorithms in DRL. As with fixed reward settings, these original DRL algorithms do not perform well while navigating a robot in dynamic environment. To address the problems, we designed a novel reward shaping method and conducted a series of experiment with all three improved DRL algorithms. The results show that the new reward shaping method can significantly improve the DRL performance when they are applied in robot navigation settings.
Text Summarization using Neural Networks

Text Summarization is the technique of extricating notable data from the first content archive. In this procedure, the separated data is produced as a consolidated report and introduced as a clearly expressed rundown. Text Summarization can be broadly classified into Extractive Summarization and Abstractive Summarization. This paper describes an algorithm based on a combination of both these approaches. Initially, essential sentences are identified and stitched together to form a consolidated report. The significance of a sentence is chosen in light of measurable and semantic highlights of sentences. This shorter representation is then passed through an Encoder-Decoder model to generate a concise summary representing the whole article. The proposed model is capable of effectively creating a concise summary, which is semantically and linguistically correct, by understanding the whole content and reletting it in its own words. The proposed methodology focuses only on the relevant sentences and passes it to the Bi-Directional RNN for identifying and representing the core idea of the article.
Three stage cervical cancer classifier based on hybrid ensemble learning with modified binary PSO using pretrained neural networks

Cervical cancer is one of the major challenges in developing nations like India.In recent years, a lot of research has been done todetect cervical cancer at an early stage through the pap-smear test, human papillomavirus test (HPV), etc. In this study, we have proposed athree-stage cervical cancer classifier to classify cervical cells among normal and abnormal cells using a hybrid ensemble classifier based onfeatures extracted using pre-trained neural networks. Furthermore, this work extends to classify the cells among different levels of dysplastic mainly mild, moderate and severe. The accuracy achieved for 2-class classification among normal and abnormal cells is up to 100% while for 4-class classification among normal, mild, moderate and severe dysplastic cells is up to 98.91% and 99.12% for new and old Herlev university hospital datasets respectively.
Data Compression Based on Coding Methods With a Controlled Level of Quality Loss

This article is devoted to the actual problem-the formation of modern technologies for processing digital information messages and the development of a video data compression method with a controlled level of quality loss. In contrast to the existing algorithms, the developed method is based on standard procedures for preliminary quantization of the transformant components of the discrete cosine transformation under conditions of effective reduction of their volume and the parameter of weight coefficients. This approach of discrete cosine transformation allows to achieve conversion and redistribution of the output signal energy in the frequency plane and to obtain areas, that have a smaller amount of information representation of the digital image from the position of its visual perception by the human visual system. This redistribution of the energy of the digital video signal allows to eliminate psychovisual redundancy of the image and provides the opportunity to make quality decisions by increasing the data compression ratio. The proposed method allows to increase the compression ratio of images by 7-10% compared to similar methods using DCT.