Reviewerly/exHarmony huggingface dataset:
On applying pseudorandom number generators in quantum cryptography with coherent states

We consider the use of pseudorandom number generators in quantum cryptography. They can be used in two ways: for basis choice according to pseudorandom sequence shared between Alice and Bob and for setting the pseudorandom phase for each coherent state to make worse the performance of some attacks. We propose this technology for the situations where the use of classical cryptographic methods like AES is satisfactory for the legitimate users.
Feature detection based on linear prediction residual for spoofing countermeasures of speaker verification system

The pre-research shows that Linear prediction (LP) residual contains more discriminative information related to replay spoofing attacks, so this paper proposes three features based on LP residual and IMel filter-banks which closely distributed in the high-frequency regions for replay spoofing countermeasures. They are residual IMel frequency cepstral coefficient (RIMFC), LP residual Hilbert envelope IMel frequency cepstral coefficient (LHIMFC) and residual phase cepstral coefficient (RPC). The effectiveness of these features is demonstrated on ASVspoofing2017 Challenge Version 2.0 dataset. Experimental results indicate that the proposed features outperform the baseline system using constant Q cepstral coefficient (CQCC), and the equal error rate (EER) is reduced under the same conditions. Moreover, feature fusions help to achieve higher performance than traditional IMel frequency cepstral coefficient (IMFCC) and CQCC, which indicates that the complementary information of different features is beneficial for detecting replay attacks.
Decomposed Deep Reinforcement Learning for Robotic Control

We study how structural decomposition and interactive learning among multiple agents can be utilized by deep reinforcement learning in order to address high dimensional robotic control problems. We decompose the whole control space of a certain robot into multiple independent agents according to this robot's physical structure. We then introduce the concept of Degree of Interaction (DoI) to describe the level of dependencies (i.e., the necessity of coordination) among the learning agents. Three different methods are then proposed to compute the DoI dynamically during learning. The experimental evaluation demonstrates that the decomposed learning method is substantially more sample efficient than the state-of-the-art algorithms, and more explicit interpretations can be generated on the final learned policy as well as the underlying dependencies among the learning agents.
Generalized Net Model of Blood Donation Processes

A generalized net model of the process of blood donation in the Department of Blood Transfusion Hematology at the University Hospital “Saint Anna” – Sofia is constructed. Further, its possible applications and extensions are discussed.
Single Look SAR Image Segmentation Using Local Entropy, Median Low-Pass Filter and Fuzzy Inference System

Synthetic Aperture Radar (SAR) image segmentation based on intensity information alone poses severe inaccuracy due to the presence of speckle noise. The multiplicative speckle noise produces salt and pepper noise effect in the end product of the image and thereby making it difficult to segment the images efficiently. Moreover, due to the same reason, by applying the classical methods which are based on thresholding and clustering, satisfactory results are often not achieved. Since the SAR images as they are in their original state cause a severe obstacle in segmentation, here it is proposed to first extract the roughness information feature in terms of local entropy and median filtered image; and then feed to the fuzzy inference system. The fuzzy inference system then takes inputs from two different features and decide the segmentation criteria. The end result from the Fuzzy classifier is then thresholded using Otsu’s method to get the final segmented image. The proposed method simplifies the segmentation by generalizing the possible spatial features to be extracted and incorporated in the segmentation process while resulting to efficient and effective results.
Fast Privacy-Preserving Punch Cards

Abstract Loyalty programs in the form of punch cards that can be redeemed for benefits have long been a ubiquitous element of the consumer landscape. However, their increasingly popular digital equivalents, while providing more convenience and better bookkeeping, pose a considerable privacy risk. This paper introduces a privacy-preserving punch card protocol that allows firms to digitize their loyalty programs without forcing customers to submit to corporate surveillance. We also present a number of extensions that allow our scheme to provide other privacy-preserving customer loyalty features. Compared to the best prior work, we achieve a 14× reduction in the computation and a 11× reduction in the communication required to perform a “hole punch,” a 55× reduction in the communication required to redeem a punch card, and a 128× reduction in the computation time required to redeem a card. Much of our performance improvement can be attributed to removing the reliance on pairings or range proofs present in prior work, which has only addressed this problem in the context of more general loyalty systems. By tailoring our scheme to punch cards and related loyalty systems, we demonstrate that we can reduce communication and computation costs by orders of magnitude.
Remote Automated Vulnerability Assessment and Mitigation in an Organization LAN

The process of makingSharma, Nishant the network of an organization (an enterprise or an institution) fool-proof and secure holds great importance in today’s interconnected world.Parveen Sultana, H. Any possible backdoor entry into any of the workstations in a large interconnected organizational network has the potential of a severe security breach that could prove fatal to the integrity of the organization.Sayyad, Asif Strong and automated solutions are required that provide as a complete package, a sound vulnerability assessment,Singh, Rahul and satisfactory mitigation should a compromise in any workstation is detected. This paper explores the need and creation of such a complete security package.Patil, Shriniwas
Application of Blockchain Technology in Data Management of University Scientific Research

In the daily scientific research activities, the university will form a large number of files. The goal of data management of university scientific research activities is to ensure the availability, authenticity and validity of scientific research data, but the electronic data is more easily modified. There are problems in data security, storage security and utilization security of scientific research data. The blockchain technology is applied to the data management of scientific research activities, which can realize the management of the whole life cycle of scientific research data and ensure the effective use and security of scientific research data. SWOT analysis is helpful for university research managers to understand the real situation of research management. Therefore, this paper makes a detailed SWOT analysis of university scientific research data management, and proposes a data management system of university scientific research activities based on blockchain technology.
The Method of Searching for Differential Trails of ARX-based Block Cipher Cypress

The method of searching for multi-round differential trails of ARX-based block cipher Cypress is proposed. Cypress is a lightweight block cipher which was designed for using in post quantum period and it operates 256/512-bit blocks and keys. Cypress is based on Feistel network with ARX-transformation as a round function. The proposed method of searching for differential trails is based on several assumptions made within the presented mathematical model (Cypress is a Markov cipher, the input differences of one-round differential trails with high probability have a small Hamming weight etc.). The searching for multi-round differential trails consists in extending probable one-round differential trails to several rounds. Using the method proposed, the efficient differential trail was found for 6-round Cypress-256, proving that 10-round Cypress-256 is resistant against differential cryptanalysis according to the requirements of practical criterion.
Comparison of DNS Based Methods for Detecting Malicious Domains

The Domain Name System (DNS) is an essential component of the internet infrastructure, used to translates domain names into IP addresses. Threat actors often abuse this system by registering and taking over thousands of Internet domains every day. These serve to launch various types of cyber-attacks, such as spam, phishing, botnets, and drive-by downloads. Currently, the main countermeasure addressing such threat is reactive blacklisting. Since cyber-attacks are mainly performed for short periods, reactive methods are usually too late and hence ineffective. As a result, new approaches to early identification of malicious websites are needed. In the recent decade, many novel papers were published offering systems to calculate domain reputation for domains that are not listed in common black-lists. This research implements three such approaches and evaluates their effectiveness in detecting malicious phishing domains. The social network analysis technique performed best, as it achieved a 60.71% detection rate with a false positive rate of only 0.35%.
Text as Semantic Fields: Integration of an Enriched Language Conception in the Text Analysis Tool Evoq$$^{{{\textregistered }}}$$

Analysis of interviews transcripts plays a key role in many human sciences research protocols. Numerous IT tools are already used to support this task. Most of them leave the interpretation task to the analyst, or involve an implicit conception of language which is rarely questioned. Developed in the context of the EFFaTA-MeM (Evocative Framework For Text Analysis - Mediality Models) trans-disciplinary research project, the Evoq software takes a radically innovative approach. It voluntarily integrates concepts from post-structuralism theory which are thus offered as a reading sieve at the analyst’s disposal. This demo paper briefly introduces the main concepts of post-structuralism, then it presents how these concept are modelised in a formal system. Finally, it shows how this approach is integrated in the Evoq software and how the human scientist can benefit from its functionalities.
Research on Low Altitude Object Detection Based on Deep Convolution Neural Network

The rapid and accurate detection of low altitude objects means a great deal to flight safety in low altitude airspace; however, low altitude object detection is very challenging due to the images’ characteristics such as scale variations, arbitrary orientations, extremely large aspect ratio, and so on. In recent years, deep learning methods, which have demonstrated remarkable success for supervised learning tasks, are widely applied to the field of computer vision and good results have been achieved. Therefore, the deep learning method is applied to low altitude object detection in this paper. We proposed a deep convolution neural network model, which utilizes deep supervision implicitly through the dense layer-wise connections and combines multi-level and multi-scale feature. The model has achieved state-of-the-art performance on two large-scale publicly available datasets for object detection in aerial images.
Output Feedback Encryption Mode: Periodic Features of Output Blocks Sequence

We investigate periodic characteristics of sequence of output blocks in the output feedback encryption mode. The model of random homogeneous substitution is used for an abstract description of this formation. This property is directly related to the periodic properties of output feedback encryption mode, since it characterizes the probabilistic distribution of output blocks with certain period appearance, provided that the assumption is made that the properties of the block symmetric cipher are consistent with certain properties of the random substitution. Also in the work specific practical tasks are solved, namely recommendations are being developed for the application of the outbound feedback on the encryption threshold, certain requirements and limitations are justified.
A construction of highly nonlinear Boolean functions with optimal algebraic immunity and low hardware implementation cost

In this paper we put forward a method to construct balanced Boolean functions by modifying the Tang–Carlet–Tang functions with the help of functions in Generalized Maiorana–McFarland class. Essential cryptographic properties, for instance, nonlinearity, algebraic immunity and immunity to fast algebraic attacks are considered. A new lower bound on the nonlinearity of the functions that are contained in our construction is obtained. It is slightly better than the known result. Owing to a combinatorial fact, it can be demonstrated that the proposed functions have optimal algebraic immunity. Meanwhile, they also possess high nonlinearity and a moderate behavior against fast algebraic attacks. Apart from theoretical contributions, hardware implementation efficiency on the proposed functions is also analyzed. Since the participation of GMM class component, experimental results imply that functions constructed in this paper have a small implementation size and employ fewer logic gates compared with the construction containing an iterative structure.
Fast Nonlinear Filter-Based Local Phase Quantization for Texture Classification

Texture divides an image into subparts called regions. Texture analysis characterizes region using texture content. Local phase quantization is a conventional method, which gives highest classification accuracy. Bilateral filter is also a good approach to obtain smoothness of a digital image while preserving the edges. Also, fast bilateral filter is another approach for high dynamic range images. In this study, new approach is proposed by integrating local phase quantization and fast bilateral filter, which in turn results in good classification accuracy. Using different filter domain parameter and filter range parameter texture, features such as mean, standard deviation, entropy, skewness, and kurtosis are extracted. Finally, for classification, these features are given to k-nearest neighbor (K-NN) classifier. The new hybrid technique is for testing and training images from Brodatz database. The results of proposed technique are compared with conventional local phase quantization results. Best classification accuracy is obtained by hybrid method for different values of domain and range parameters with different window sizes.
Types of Games

This chapter introduces the most important classes of games underlying practical security models. These include Stackelberg games, Nash games, signaling games, and games with distribution-valued payoffs. The latter build upon empirical methods and data science to construct games from data, but also reveals theoretic connections to multi-criteria optimization using lexicographic goal priorities (that classical games cannot deal with, but distribution-valued games can handle). Each game description is accompanied by examples from the security domain to motivate and illustrate the use of the individual model. Each class of game is discussed in relation to the other types, highlighting pros and cons, las well as applications, detailed in later chapters.
Secure and Robust Blind Watermarking of Digital Images Using Logistic Map

Advances inPurti,Grace Karuna the digital media ease the users to transmit/share digitalYadav,Dilip Kumar data in the form of audio files, digital imagesChandra Mouli,P. V. S. S. R. and digital videos. However, the risk involved in such transmission or sharing is also increasing. The necessity to protect the copyrights of digital data and thereby identification of the owner is essential. Digital watermarking is the best solution for ownership identification and copyright protection. This paper proposes a novel blind watermarking approach based on logistic map. The watermark is embedded into a cover image using singular value decomposition (SVD). The experimental results and comparative analysis prove the efficacy of our method. For objective evaluation, peak signal to noise ratio (PSNR) and normalized cross-correlation (NCC) have been used to estimate the imperceptibility and robustness of our watermarking process.
A Thin Client Error-Correcting Data Storage Framework Based on Blockchain

Traditional centralized and distributed data storage systems are hard to achieve data integrity. A blockchain is more resistant to modification of the data than widely used technologies, such as digital signature and digital watermarking. Each node in a blockchain is required to store data so that all the nodes should have powerful computing and storage capacity. Centralized service points make a thin client possible at the cost of decreasing decentralization that a blockchain possesses. In addition, a wrong block breaks the data consistency in the system, and existing data dissemination protocol cannot solve the wrong block problem efficiently. This paper proposes a thin client error-correcting data storage framework based on blockchain. The data of the clients are stored as blocks in the system to achieve the data integrity. The data requests are handled by randomly selected blockchain nodes. We propose two error-correcting mechanisms to validate and correct the wrong data blocks. We also analyze the performance of the proposed framework with respect to data tamper proof, consensus, and thin client. The proposed framework has the characteristics of lightweight client, data tamper proof, and high resource utilization in the blockchain network.
Buddhist Hasta Mudra Recognition Using Morphological Features

Mudras are considered as spiritual gestures in the religious sense and hold a very important place in the cultural and spiritual space in India. Images are the symbolic representations of divinity in religious artwork and their origins are conveyed through the religions and spiritual beliefs. Such gestures also have some specific meaning in the Buddhist religion. It refers to some of the events in the life of Buddha or denotes special characteristics of the Buddha deities. In recent years, automatic identification of these gestures has gained a greater interest from the machine learning community. This would help to identify the various deities that exist in the Buddhist religion, leading to digital preservation of cultural heritage art. This paper provides a framework that recognizes the Buddhist hand gesture or Hasta Mudra. The morphological features are extracted from the gesture employing geometric parameters. The experimental results show that utilising geometric features and using k-Nearest Neighbor (kNN) as a classifier, an approximately 70% recognition rate is achieved.
Human video synthesis using generative adversarial networks

In this work, a video synthesis model based on Generative Adversarial Networks (Human GAN) is proposed, whose objective is to generate a photorealistic output by learning the mapping function from an input source to output video. However, the image to image generation is a quite popular problem, but the video synthesis problem is still unexplored. Directly employing existing image generation method without taking temporal dynamics into account leads to frequent temporally incoherent output with low visual quality. The proposed approach solves this problem by wisely designing generators and discriminators combined with Spatio-temporal adversarial objects. While comparing it to some robust baselines on public benchmarks, the proposed model proves to be superior in generating temporally coherent videos with extremely low artifacts. And results achieved by the proposed model are more realistic on both quantitative and qualitative measures compared to other existing baselines techniques.
Text-Dependent Closed-Set Two-Speaker Recognition of a Key Phrase Uttered Synchronously by Two Persons

In this paper, we propose a novel text-dependent speaker recognition system using a key phrase uttered synchronously by two persons, i.e., uttered in unison. Hereafter, we refer to this speech as a duo utterance. This proposed system accepts the duo utterance uttered by the enrollment speaker pair but rejects the duo utterance from other speaker pairs and the utterance uttered by a single speaker. The difference from the conventional speaker recognition systems is that the proposed system requires the duo utterance for speaker recognition. Hence, it is expected that the proposed system is a high-level security system because this system requires duo utterance. To realize this proposed system, we have employed a d-vector and a hidden Markov model (HMM). The d-vector is the feature vector extracted by a speaker identification deep neural network (DNN) that is trained to identify the speakers and the speaker pairs using the frame-level acoustic features. Additionally, it is widely known that the HMM is suitable for the speaker model of text-dependent speaker recognition system. To evaluate the proposed method, we conducted speaker identification experiments. Experimental results show that the proposed system achieved higher performance than the MFCC-based system. In addition, we investigated the errors of the experimental results for improving future systems.
Theoretical Arguments and Debates on Public Enterprises Privatization: Ethiopian Focus

Purpose: To critically review the theoretical arguments and debates on privatization, challenges, and prospects in the Ethiopian context, and the various countries experience on privatization with especial focus on sub-Saharan countries and come up with valuable directions to undertake the privatization process successfully. Design/Methodology/Approach: The entire review basis the theoretical end empirical literature published in various national and international reputable journals. Findings: Privatization is not a recent phenomenon globally; different economies practiced it via arguing and debating in terms of its economic and political benefits. Currently, the government of Ethiopia is practicing economic reforms to get ready for the country to join middle-income countries by 2025. In filling this gap, inter alia, the government has taken new initiative to privatize the existing wholly state-owned enterprises, both for local and foreign owners, with the primary objectives of limiting government's participation in the manufacturing and service sectors, and thereby realizing economic efficiency and competition, boosting foreign exchange, rising tax revenues, simplifying problems associated with costs of living through controlling inflation, reducing unemployment and alleviating the problem of good governance. Practical Implications: Finally, it tried to forward sequential steps that the government of Ethiopia should follow at least to mitigate the common mistakes and to undertake the privatization process successfully by alleviating the fear of the citizens that those SOEs would be concerted in the hands of some oligarchies. Originality/Value: the inferences made as a result of this review will offer new insights through which countries like Ethiopia, who are suffering from political unrest and enormous amount of unemployment, can justly distribute the wealth of public enterprises especially to the local citizens and thereby mitigate the unbalanced concentration of resources in the hands of some oligarchies. Keywords : privatization; a state-owned enterprise; privatization debates; Ethiopia DOI: 10.7176/JESD/11-11-06 Publication date: June 30th 2020