Reviewerly/exHarmony huggingface dataset:
CoVID-19 Vaccination Certificate Supply Verification Based on Blockchain

The emergence of the pandemic, Covid-19, impacted the worldwide economy. The daily scientific development helped in creating a vaccine against the virus. In order to limit the spread of the virus, government authorities are making it mandatory for vaccination certificates for people to run businesses or access public facilities like theaters, restaurants, etc. The government mandate of vaccine certificates compelled people to purchase fake certificates and fake entities to provide fake certificates. This paper provides an application-based blockchain solution for registering vaccinating authorities to vaccinate and provides relevant vaccine certificates that anyone can verify in no time. Additionally, analysis of data retrieving from the blockchain is provided because the data reading will be happening by thousands of authorities. The code for the prototype application is available on Github.
SIKM – a smart cryptographic key management framework

Abstract For a secure data transmission in symmetric cryptography, data are encrypted and decrypted using an identical key. The process of creating, distributing, storing, deploying, and finally revoking the symmetric keys is called key management. Many key management schemes are devised that each one is suitable for a specific range of applications. However, these schemes have some common drawbacks like the hardness of key generation and distribution, key storage, attacks, and traffic load. In this article, a key management framework is proposed, which is attack resistant and transforms the current customary key management workflow to enhance security and reduce weaknesses. The main features of the proposed framework are eliminating key storage, smart attack resistant feature, reducing multiple-times key distribution to just one-time interpreter distribution, and having short key intervals – minutely, hourly, and daily. Moreover, the key revocation process happens automatically and with no revocation call.
Learning Detail-Structure Alternative Optimization for Blind Super-Resolution

Existing convolutional neural networks (CNN) based image super-resolution (SR) methods have achieved impressive performance on bicubic kernel, which is not valid to handle unknown degradations in real-world applications. Recent blind SR methods suggest to reconstruct SR images relying on blur kernel estimation. However, their results still remain visible artifacts and detail distortion due to the estimation errors. To alleviate these problems, in this paper, we propose an effective and kernel-free network, namely DSSR, which enables recurrent detail-structure alternative optimization without blur kernel prior incorporation for blind SR. Specifically, in our DSSR, a detail-structure modulation module (DSMM) is built to exploit the interaction and collaboration of image details and structures. The DSMM consists of two components: a detail restoration unit (DRU) and a structure modulation unit (SMU). The former aims at regressing the intermediate HR detail reconstruction from LR structural contexts, and the latter performs structural contexts modulation conditioned on the learned detail maps at both HR and LR spaces. Besides, we use the output of DSMM as the hidden state and design our DSSR architecture from a recurrent convolutional neural network (RCNN) view. In this way, the network can alternatively optimize the image details and structural contexts, achieving co-optimization across time. Moreover, equipped with the recurrent connection, our DSSR allows low- and high-level feature representations complementary by observing previous HR details and contexts at every unrolling time. Extensive experiments on synthetic datasets and real-world images demonstrate that our method achieves the state-of-the-art against existing methods. The source code can be found at https://github.com/Arcananana/DSSR.
Post-quantum Anonymous One-Sided Authenticated Key Exchange Without Random Oracles

Authenticated Key Exchange (AKE) is a cryptographic protocol to share a common session key among multiple parties. Usually, PKI-based AKE schemes are designed to guarantee secrecy of the session key and mutual authentication. However, in practice, there are many cases where mutual authentication is undesirable such as in anonymous networks like Tor and Riffle, or difficult to achieve due to the certificate management at the user level such as the Internet. Goldberg et al. formulated a model of anonymous one-sided AKE which guarantees the anonymity of the client by allowing only the client to authenticate the server, and proposed a concrete scheme. However, existing anonymous one-sided AKE schemes are only known to be secure in the random oracle model. In this paper, we propose generic constructions of anonymous one-sided AKE in the random oracle model and in the standard model, respectively. Our constructions allow us to construct the first post-quantum anonymous one-sided AKE scheme from isogenies in the standard model.
Gaussian estimation for non‐linear stochastic uncertain systems with time‐correlated additive noises and packet dropout compensations

This paper discusses the problem of state estimation for non-linear stochastic uncertain systems with time-correlated additive noise and packet dropout compensations. Both process noise and measurement noise are additive forms of noise that can be described as Gaussian first-order Markov processes. The stochastic uncertainty of the system is described by synchronous correlated white multiplicative noise. When the measurement information from the current epoch is lost, a compensation strategy based on measurement information prediction is used. First, the state information, process noise, and measurement noise are augmented to enable the construction of a new vector to be estimated, and the filter, predictor, and smoother of the system are designed using a Gaussian iterative estimation algorithm based on innovation analysis. The multiplicative noise in the process equation is estimated in real time. Second, a numerical implementation of the proposed algorithm is derived based on the third-degree spherical-radial Cubature Kalman filter(CKF). Finally, the proposed algorithm is compared with an extended Kalman filter (EKF) based on Taylor series expansion. The effectiveness of the proposed algorithm is verified by comparing the simulation results.
A state-of-the-art security and attacks analysis in blockchain applications network

In recent years, blockchain has emerged as one of the promising technologies in applications like internet of things, smart city, digital management, healthcare system, supply chain management, real estate business, smart agriculture, smart retail and smart grid. Blockchain is derived from bitcoin cryptocurrency white paper in 2008 where peer to peer transactions are done. Though blockchain is considered a secure platform to store the information in digital ledger format in trustful environment with proper verification and validation process. Distributed architecture of blockchain network required high computation devices for mining and doing the cryptographic operation like digital signature. In a decentralised network challenges such as data validation, transaction integrity, fault tolerance, user anonymity are need to be addressed. In this paper, the state of the art of security attacks and various network architecture challenges are identified. The security attacks in the blockchain are discussed in-depth. The various applications of blockchain and corresponding security challenges are also explained. Lastly, the implementation of blockchain network creation, deployment of smart contracts using the open-source Ethereum platform was demonstrated.
Quantum state transfer on the complete bipartite graph

Abstract Previously it was shown that (almost) perfect state transfer can be achieved on the complete bipartite graph by a discrete-time coined quantum walk based algorithm when both the sender and receiver vertices are in the same partition of the graph and when the sender and receiver are in opposite partitions of the same size. By changing the coin operator, we analyze the state transfer problem and we show that it is still possible to achieve state transfer with high fidelity even when the sender and receiver are in different partitions with different sizes. Moreover, it is also possible to use an active switch approach using lackadaisical quantum walks where the marked vertex is switched between the sender and receiver during the algorithm.
Cryptogames as Drivers for Blockchain Application Development

In this chapter, we outline reasons why cryptogaming, like cryptocurrencies in general, is an area of special interest for blockchain research. We suggest that cryptogaming is a field that drives forward the development of blockchain applications. Games require continuous interest, added value through interaction, and sufficient content in order to stay viable as a business model. They therefore challenge the ways in which we commonly view blockchains as first and foremost trust technologies and sidesteps from traditional banking, contracts and insurance, and call us to envision a much wider application of the technology than is currently being discussed in both popular media and research.
Radical Isogenies on Montgomery Curves

We work on some open problems in radical isogenies. Radical isogenies are formulas to compute chains of N-isogenies for small N and proposed by Castryck, Decru, and Vercauteren in Asiacrypt 2020. These formulas do not need to generate a point of order N generating the kernel and accelerate some isogeny-based cryptosystems like CSIDH. On the other hand, since these formulas use Tate normal forms, these need to transform Tate normal forms to curves with efficient arithmetic, e.g., Montgomery curves. In this paper, we propose radical-isogeny formulas of degrees 3 and 4 on Montgomery curves. Our formulas compute some values determining Montgomery curves, from which one can efficiently recover Montgomery coefficients. And our formulas are more efficient for some cryptosystems than the original radical isogenies. In addition, we prove a conjecture left open by Castryck et al. that relates to radical isogenies of degree 4.
Image Enhancement Algorithm Based on Image Spatial Domain Segmentation

This paper proposes an image enhancement algorithm based on the theory of image segmentation and image frequency. First, a mathematical model corresponding to the pixel frequency is established by using the difference of pigment values between the pixels in the image and the surrounding pixels (i.e., pixel receptive field). Then, the image is divided into the low frequency region (background area), low-medium frequency region (foreground area), medium-high frequency region (target area) and high frequency region (detail area) by a pixel frequency characteristic graph. Gamma correction, MSRCR, MSR, top hat+bottom hat are used for image enhancement for each area, and then the parts are merged. Three indicators of PSNR, SSIM, and MSE are introduced to evaluate the quality of the enhanced image. The results show that the image enhanced by this algorithm has the highest PSNR and SSIM values and the lowest MSE value, indicating that the enhancement effect of this algorithm is better. Compared with traditional algorithms, the image enhancement algorithm in this paper produces higher image quality and richer details.
Application of Convolutional Neural Networks in Cancer Diagnosis

Early detection and treatment are crucial for recovery from cancer. Due to rapid advancement in medical imaging techniques, today doctors can scan various body parts from different perspectives to diagnose cancer or see the progress during a course of treatment. Such techniques are less painful and require less preparation and recovery. The recent evolution of AI, particularly deep learning algorithms have shown potential in computer-aided diagnosis of cancer. These include analysis of cancer images for detection of tumor region, prognosis, skin tissue inspection, etc. Convolutional Neural Network (CNN) based image processing techniques are showing promising results in the diagnosis of breast cancer, skin cancer, prostate cancer, lung cancer, etc. Literature shows many other CNN-based models such as feed-forward neural network, Deep CNN, Multi-Crop CNN, fully convolutional networks (FCNs), Deep Fully Convolutional Network (DFCNet), Recurrent neural networks, etc. The CNN focus solely on the image-specific features and hence require a lesser number of input parameters. A CNN model reduces the size of the input image vector without losing the features critical for making an accurate prediction. This makes it easier to process and reduces computational complexity. CNNs are also not sensitive to the object positioning in an image since the approximate position of image motifs relative to others is known. CNN deals with the variations in images through augmentation and other pre-processing techniques. This makes them robust to various straining conditions in images. This chapter presents an overview of a generic CNN architecture followed by a discussion on major advances in CNN models and their applications for cancer image analysis.
Accelerating Large-Scale Graph-Based Nearest Neighbor Search on a Computational Storage Platform

K-nearest neighbor search is one of the fundamental tasks in various applications and the hierarchical navigable small world (HNSW) has recently drawn attention in large-scale cloud services, as it easily scales up the database while offering fast search.On the other hand, a computational storage device (CSD) that combines programmable logic and storage modules on a single board becomes popular to address the data bandwidth bottleneck of modern computing systems.In this paper, we propose a computational storage platform that can accelerate a large-scale graph-based nearest neighbor search algorithm based on SmartSSD CSD.To this end, we modify the algorithm more amenable on the hardware and implement two types of accelerators using HLS-and RTL-based methodology with various optimization methods.In addition, we scale up the proposed platform to have 4 SmartSSDs and apply graph parallelism to boost the system performance further.As a result, the proposed computational storage platform achieves 75.59 query per second throughput for the SIFT1B dataset at 258.66W power dissipation, which is 12.83x and 17.91x faster and 10.43x and 24.33x more energy efficient than the conventional CPU-based and GPU-based server platform, respectively.With multi-terabyte storage and custom acceleration capability, we believe that the proposed computational storage platform is a promising solution for cost-sensitive cloud datacenters.
A Convenient Method and Design for Constructing an IoT-Based Smart Automated Sanitization System

According to the researchers, the Internet of Things (IoT) is considered a revolutionary technology that has the potential to have enormous growth, impact, and potential in this modern era. The IoT is a new technology platform that offers a global network of electronic devices and technologies. It had widely acknowledged as one of the most critical areas for upcoming future technologies. As a result, considerable structural modifications and modernization are still required in this field. This paper aims to advance IoT technology by developing an automated sanitization system to protect people’s health by sanitizing their surroundings. In the current global epidemic, an IoT-based auto sanitization system could be a unique solution for avoiding the spread of COVID-19. It can also use as an example of how to live a clean, healthy, and productive life on a budget. Consumers may control IoT devices from anywhere in the world because they work through a cloud data server. This study suggests an automated disinfector machine with a proximity sensor-integrated automated door and NodeMCU to create the prototype. This procedure can conduct automatically, via speech, or even using a mobile application via the internet anywhere in the world.
Intensified Gray Wolf Optimization-based Extreme Learning Machine for Sentiment Analysis in Big Data

Enhancement of the social network has boosted people's interest in researching product ratings and reviews before buying them. This has provided a way for doing research on domain adaptation, sentiment analysis, and natural language processing as separate areas of study. While a classifier learned from one domain may perform poorly on data from another domain, it is important to keep an open mind while exploring other classifiers. Reviews of dataset are currently being processed in big data. Algorithm developed for performing sentiment analysis in standalone machine or low quantity dataset will not give its best performance while performing sentiment analysis in big data. This paper proposes a classifier, namely intensified gray wolf optimization-based extreme learning machine (IGWO-ELM) for performing the sentiment analysis in big data. IGWO-ELM adopts the natural behavior of wolves to find the sentiments in big review dataset. Single layer hidden layer of ELM assists in performing better classification. Proposed classifier has been evaluated using accuracy and F-measure performance metrics. Results make an indication that the proposed classifier attains better classification accuracy than existing classifiers.
Structure Optimization and Investigations of the Hybrid GMDH-Neo-fuzzy Neural Networks in Forecasting Problems

The hybrid evolving GMDH- neo-fuzzy system was suggested and investigated. The application of GMDH based on self-organization principle enables to build optimal structure of neo-fuzzy system and train weights of neural network in one procedure. The suggested approach allows to prevent the drawbacks of deep learning such as vanishing or exploding of gradient. As a node of neo-fuzzy system neo-fuzzy neuron with small number of tunable parameters is suggested. This enables to cut training time and accelerate convergence of training. The experimental studies of hybrid neo-fuzzy network were carried out in the task of forecasting of industrial output index, share prices and NASDAQ index. The forecasting efficiency of the suggested hybrid neo-fuzzy system in macro-economy and financial sphere was estimated, its structure was optimized and sensitivity to variation of tuning parameters was investigated.
Why Do Online Countering Violent Extremism Strategies Not Work? The Case of Digital Jihad

This article discusses the reasons why content moderation and control in cyberspace, being a primary means of online countering violent extremism (CVE), does not work as intended. It makes four main arguments. Firstly, despite years of efforts from CVE stakeholders, the propaganda of militant Islamist VEOs is still easily accessible on the Internet. This study has mapped hundreds of addresses engaged in digital jihadist activities at the turn of 2020–2021. It effectively proves that the current approach to online CVE brought few tangible effects. Secondly, these programs have been unevenly applied to militant Islamist organizations. There have been groups which represent violent extremist ideology but are not actively combated by stakeholders. As a result, these groups have been able to establish a solid foothold on the surface web. Thirdly, the efficiency of CVE strategies based on content takedowns are decreased by the relative ease of terrorists reestablishing banned communication channels, the availability of vast alternatives for online propaganda dissemination, the introduction of impractical legislation by governments, overlapping jurisdictions on the Internet and the “Streisand effect.” Finally, adequate strategies aimed at curbing digital jihad should be primarily based on offline and online activities which fall outside of the remit of CVE.
QDistRnd: A GAP package for computing the distance of quantum error-correcting codes

The GAP package QDistRnd implements a probabilistic algorithm for finding the minimum distance of a quantum low-density parity-check code linear over a finite field GF(q).At each step several codewords are randomly drawn from a distribution biased toward smaller weights.The corresponding weights are used to update the upper bound on the distance, which eventually converges to the minimum distance of the code.While there is no performance guarantee, an empirical convergence criterion is given to estimate the probability that a minimum weight codeword has been found.In addition, a format for storing matrices associated with q-ary quantum codes is introduced and implemented via the provided import/export functions.The format, MTXE, is based on the well established MaTrix market eXchange (MTX) Coordinate format developed at NIST, and is designed for full backward compatibility with this format.Thus, MTXE files are readable by any software package which supports MTX.
Understanding deep learning defenses against adversarial examples through visualizations for dynamic risk assessment

In recent years, deep neural network models have been developed in different fields, where they have brought many advances. However, they have also started to be used in tasks where risk is critical. Misdiagnosis of these models can lead to serious accidents or even death. This concern has led to an interest among researchers to study possible attacks on these models, discovering a long list of vulnerabilities, from which every model should be defended. The adversarial example attack is a widely known attack among researchers, who have developed several defenses to avoid such a threat. However, these defenses are as opaque as a deep neural network model, how they work is still unknown. This is why visualizing how they change the behavior of the target model is interesting in order to understand more precisely how the performance of the defended model is being modified. For this work, three defense strategies, against adversarial example attacks, have been selected in order to visualize the behavior modification of each of them in the defended model. Adversarial training, dimensionality reduction, and prediction similarity were the selected defenses, which have been developed using a model composed of convolution neural network layers and dense neural network layers. In each defense, the behavior of the original model has been compared with the behavior of the defended model, representing the target model by a graph in a visualization. This visualization allows identifying the vulnerabilities of the model and shows how the defenses try to avoid them.
diveXplore 6.0: ITEC’s Interactive Video Exploration System at VBS 2022

Continuously participating since the sixth Video Browser Showdown (VBS2017), diveXplore is a veteran interactive search system that throughout its lifetime has offered and evaluated numerous features. After undergoing major refactoring for the most recent VBS2021, however, the system since version 5.0 is less feature rich, yet, more modern, leaner and faster than the original system. This proved to be a sensible decision as the new system showed increasing performance in VBS2021 when compared to the most recent former competitions. With version 6.0 we reconsider shot segmentation, map search and introduce new features for improving concept as well as temporal context search.