Reviewerly/exHarmony huggingface dataset:
An Early Breast Cancer Detection System Using Recurrent Neural Network (RNN) with Animal Migration Optimization (AMO) Based Classification Method

Breast cancer can be detected using early signs of it mammograms and digital mammography. For Computer Aided Detection (CAD), algorithms can be developed using this opportunities. Early detection is assisted by self-test and periodical check-ups and it can enhance the survival chance significantly. Due the need of breast cancer’s early detection and false diagnosis impact on patients, made researchers to investigate Deep Learning (DL) techniques for mammograms. So, it requires a non-invasive cancer detection system, which is highly effective, accurate, fast as well as robust. Proposed work has three steps, (i) Pre-processing, (ii) Segmentation, and (iii) Classification. Firstly, preprocessing stage removing noise from images by using mean and median filtering algorithms are used, while keeping its features intact for better understanding and recognition, then edge detection by using canny edge detector. It uses Gaussian filter for smoothening image. Gaussian smoothening is used for enhancing image analysis process quality, result in blurring of fine-scaled image edges. In the next stage, image representation is changed into something, which makes analyses process as a simple one. Foreground and background subtraction is used for accurate breast image detection in segmentation. After completion of segmentation stage, the remove unwanted image in input image dataset. Finally, a novel RNN forclassifying and detecting breast cancer using Auto Encoder (AE) based RNN for feature extraction by integrating Animal Migration Optimization (AMO) for tuning the parameters of RNN model, then softmax classifier use RNN algorithm. Experimental results are conducted using Mini-Mammographic (MIAS) dataset of breast cancer. The classifiers are measured through measures like precision, recall, f-measure and accuracy.
COVID-19 Mobile Positioning Data Contact Tracing and Patient Privacy Regulations: Exploratory Search of Global Response Strategies and the Use of Digital Tools in Nigeria (Preprint)

<sec> <title>BACKGROUND</title> The coronavirus disease (COVID-19) pandemic is the biggest global economic and health challenge of the century. Its effect and impact are still evolving, with deaths estimated to reach 40 million if unchecked. One effective and complementary strategy to slow the spread and reduce the impact is to trace the primary and secondary contacts of confirmed COVID-19 cases using contact tracing technology. </sec> <sec> <title>OBJECTIVE</title> The objective of this paper is to survey strategies for digital contact tracing for the COVID-19 pandemic and to present how using mobile positioning data conforms with Nigeria’s data privacy regulations. </sec> <sec> <title>METHODS</title> We conducted an exploratory review of current measures for COVID-19 contact tracing implemented around the world. We then analyzed how countries are using mobile positioning data technology to reduce the spread of COVID-19. We made recommendations on how Nigeria can adopt this approach while adhering to the guidelines provided by the National Data Protection Regulation (NDPR). </sec> <sec> <title>RESULTS</title> Despite the potential of digital contact tracing, it always conflicts with patient data privacy regulations. We found that Nigeria’s response complies with the NDPR, and that it is possible to leverage call detail records to complement current strategies within the NDPR. </sec> <sec> <title>CONCLUSIONS</title> Our study shows that mobile position data contact tracing is important for epidemic control as long as it conforms to relevant data privacy regulations. Implementation guidelines will limit data misuse. </sec>
A Distributed Graph Data Storage in Ethereum Ecosystem

GraphChain – a framework for on-chain data management for Blockchains is presented. The framework forms the foundational technology for the Ontochain project offering the synergy between ontologies and the Blockchain mechanisms. The use of Ethereum based Layer-2 mechanisms helped create the idea of Ontospace, which designates an ecosystem for trusted ontologies and trusted processing of smart contracts that can directly use the semantic data.
Parallel Implementation of Real-Time Object Detection using OpenMP

One of the urgent problems of the modern world is the detection and recognition of objects on a video stream in real-time. To work with a video stream, you need to split it into frames that are more convenient for processing. There is a possibility of using multi-core processors for image processing. Typically, the operating system allows only one core of multi-core processors to execute a task. But there are such parallelization tools that allow parallelizing execution when processing data on multi-core processors. And OpenMP is one of them that can be used in real-time object detection processing procedures. But in this case, the main thing is to find that part of the algorithm that is suitable for parallelization. Using OpenMP simply does not give an efficient accelerated result. In this paper, we discuss the implementation of OpenMP technology in real-time object detection algorithms on multi-core processors. Experimental results show that the use of OpenMP technology significantly improves the processing speed for real-time object detection on multi-core processors, when there are no GPUs.
Deep Fraud Detection on Non-attributed Graph

Fraud detection problems are usually formulated as a machine learning problem on a graph. Recently, Graph Neural Networks (GNNs) have shown solid performance on fraud detection. The successes of most previous methods heavily rely on rich node features and high-fidelity labels. However, labeled data is scarce in large-scale industrial problems, especially for fraud detection where new patterns emerge from time to time. Meanwhile, node features are also limited due to privacy and other constraints. In this paper, two improvements are proposed: 1) We design a graph transformation method capturing the structural information to facilitate GNNs on non-attributed fraud graphs. 2) We propose a novel graph pre-training strategy to leverage more unlabeled data via contrastive learning. Experiments on a large-scale industrial dataset demonstrate the effectiveness of the proposed framework for fraud detection.
Machine learning

Healthcare is at the dawn of a new era of intelligent systems and improved human relationships. The potential of artificial intelligence (AI) and machine learning (ML) technologies to support decision-making, optimize workflows, and free up quality human time is revolutionizing how people deliver and receive care. The success and performance of AI-based expert-level diagnostic systems have inspired unprecedented optimism. However, there are growing concerns about ethics, safety, and equity in the delivery of care. The lack of clarity about how it works and the resulting mistrust has negatively affected the relationship between AI and caregivers and recipients, preventing adoption. This chapter provides a general overview of the various AI learning areas and a detailed introduction to the area of federated learning, a key to the application of machine learning in the future vision of healthcare.
Kleros: A Socio-Legal Case Study Of Decentralized Justice &amp;amp; Blockchain Arbitration

Kleros—a decentralized blockchain-based arbitration solution that relies on smart contracts and crowdsourced jurors—was conceived to bridge the trust gap that separates e-commerce participants, as in the paradigmatic example of “Alice and Bob.” Previous studies have mainly focused on describing blockchain-based arbitration solutions such as Kleros, and at least one has warned about the potential risks to essential procedural safeguards presented by anonymous and economically-incentivized jurors. Using Kleros as a socio-legal case study of a token-based business providing cutting-edge decentralized blockchain arbitration services, we paint the “big picture” of how Kleros works, and analyze its efficacy to improve on traditional online dispute resolution. We also report the results of our exploratory, observational user survey and a series of interface trials. We make a number of contributions to the literature. First, our case study introduces the concept of the “decentralized sheriff,” which we use to refer to the crowd-based compliance mechanism implemented by Kleros to certify crypto tokens. We argue that Kleros’ “decentralized sheriff” contributes to the public good by filling a regulatory hole with respect to the crypto market. Second, building on the work of scholars who studied Kleros, we analyze the major obstacles to widespread adoption of Kleros. Third, we explore whether social interactions of trust and claiming present unique characteristics under Kleros compared to models described in Law and Society literature. Throughout our analysis, we formulate questions for future research and investigation.
Quantum Attacks on 1K-AES and PRINCE

Abstract By introducing the BHT algorithm into the slide attack on 1K-AES and the related-key attack on PRINCE, we present the corresponding quantum attacks in this paper. In the proposed quantum attacks, we generalize the BHT algorithm to the situation where the number of marked items is unknown ahead of time. Moreover, we give an implementation scheme of classifier oracle based on Quantum Phase Estimation algorithm in presented quantum attacks. The complexity analysis shows that the query complexity, time complexity and memory complexity of the presented quantum attacks are all $\mathcal{O}(2^{n/3})$ when the success probability is about $63\%$, where $n$ is the block size. Compared with the corresponding classical attacks, the proposed quantum attacks can achieve subquadratic speed-up under the same success probability no matter on query complexity, time complexity or memory complexity. Furthermore, the query complexity of the proposed quantum slide attack on 1K-AES is less than Grover search on 1K-AES by a factor of $2^{n/6}.$ When compared with the Grover search on PRINCE, the query complexity of the presented quantum attack on PRINCE is reduced from $\mathcal{O}(2^{n})$ to $\mathcal{O}(2^{n/2}).$ When compared with the combination of Grover and Simon’s algorithms on PRINCE, the query complexity of our quantum attack on PRINCE is reduced from $\mathcal{O}(n\cdot 2^{n/2})$ to $\mathcal{O}(2^{n/2}).$ Besides, the proposed quantum slide attack on 1K-AES indicates that the quantum slide attack could also be applied on Substitution-Permutation Network construction, apart from the iterated Even-Mansour cipher and Feistel constructions.
A Cardiac Shape Model for Segmentation Uncertainty Quantification

Segmentation of cardiac images is a variable component of many patient specific computational pipelines, yet its impact on simulated results are still not fully understood. A hurdle to to exploring the impact of the segmentation variability is the technical challenge of building a statistical shape model of the ventricles. In this study, we improved open our previous shape analysis by creating a unified shape model including both the epicardium and endocardium. We tested four techniques within ShapeWorks to generate a ventricular shape model: standard, multidomain, hybrid multidomain, and geodesic distance. The multidomain and hybrid multidomain generated a shape model using all eleven segmentations, and the geodesic distance method generated a shape model using a subset of four segmentations. Each of the shape models captured spatially dependent characteristics of the segmentation variability, including wall thickness, annular diameter, and basal truncation. While each of the three methods have benefits, the hybrid multidomain approach provided the most accurate shape model with fewest points and may be most useful in a majority of applications.
Global Convolutional Neural Processes

The ability to deal with uncertainty in machine learning models has become equally, if not more, crucial to their predictive ability itself. For instance, during the pandemic, governmental policies and personal decisions are constantly made around uncertainties. Targeting this, Neural Process Families (NPFs) have recently shone a light on prediction with uncertainties by bridging Gaussian processes and neural networks. Latent neural process, a member of NPF, is believed to be capable of modelling the uncertainty on certain points (local uncertainty) as well as the general function priors (global uncertainties). Nonetheless, some critical questions remain unresolved, such as a formal definition of global uncertainties, the causality behind global uncertainties, and the manipulation of global uncertainties for generative models. Regarding this, we build a member GloBal Convolutional Neural Process(GBCoNP) that achieves the SOTA log-likelihood in latent NPFs. It designs a global uncertainty representation p(z), which is an aggregation on a discretized input space. The causal effect between the degree of global uncertainty and the intra-task diversity is discussed. The learnt prior is analyzed on a variety of scenarios, including 1D, 2D, and a newly proposed spatial-temporal COVID dataset. Our manipulation of the global uncertainty not only achieves generating the desired samples to tackle few-shot learning, but also enables the probability evaluation on the functional priors.
Path Planning Algorithm on Large Scale Terrain Data Based on PRM with Non-Uniform Sampling Strategy

Path planning is a research hotspot in the field of geographic information. Many scholars research on path planning on road networks, but path planning in areas without road network has not been thoroughly studied. This paper aims to find lower-cost paths quickly on large scale digital terrain data. We proposed a method based on the PRM algorithm which incorporates a non-uniform sampling strategy to increase the possibility to find better paths with fewer sampling points. A post-optimization method is also suggested to optimize the final path. Experiments prove the effectiveness of our algorithm compared to classical PRM with the uniform sampling strategy.
Review on Real-Time Drone Detection Based on Visual Band Electro-Optical (EO) Sensor

Drones have gained a lot of traction in the fields of amateur and professional video creation. They are con- strained by more than just civilian lines of action. Drones, on the other hand, have been employed in terrorist attacks, privacy breaches, and inadvertent accidents in high-risk areas like air- ports. This is a major source of risk for important sites. It is not easy to identify such attacks in a timely manner. Many researchers from various fields are looking into this issue. New technologies have been devised to manage and monitor the horizon in reaction to these incidents, ensuring protection in high-risk locations. From a cost-to-quality aspect, the use of camera systems for drone detection is extremely appealing. Drone detection via visual information, on the other hand, is impeded by drones' striking resemblance to other things, such as birds or aircraft. Furthermore, because drones could travel at high speeds, detection is preferred in the real-time domain. This paper goes into making a review on current algorithms for drone detection in real-time domain based on EO sensors. The most typical methodology for drone detection is classified to some traditional computer vision methods and recently deep learning methods. This paper aims to guide the researchers to design upcoming drone detecting systems using the most practical technologies that satisfy certain requirements such as adaptability, extensibility, dependability, and affordability.
Method for Running Dynamic Systems over Encrypted Data for Infinite Time Horizon without Bootstrapping and Re-encryption

In this paper, we propose a method for dynamic systems to operate over homomorphically encrypted data for an infinite time horizon, where we do not make use of reset, re-encryption, or bootstrapping for the encrypted messages. The given system is first decomposed into the stable part and the anti-stable part. Then, the stable part is approximated to have finite impulse response, and by a novel conversion scheme, the eigenvalues of the state matrix of the anti-stable part are approximated to algebraic integers. This allows that the given system can be implemented to operate for an infinite time horizon using only addition and multiplication over encrypted data, without re-encrypting any portion of data. The performance error caused by the approximation and quantization can be made arbitrarily small, with appropriate choice of parameters.
Verbal sentiment analysis and detection using recurrent neural network

Sentiment analysis of human beings is a recent research field that grows exponentially. Though certain works have been published on this area, much remains to be improved in terms of human attitude and accuracy. Sentiment analysis of text, sentiment analysis of speech, and visual sentiment analysis have been reported earlier. Though the task is difficult with only verbal statements, in this work, verbal data are considered to analyze human sentiments using a deep recurrent neural network (RNN). First, the state-of-the-art of the work is discussed. Two major sentiments along with two submajor sentiments are worked out. Some of the versions are recorded from human subjects in different moods. These are deeply analyzed instead of only considering positive and negative options. Due to the efficacy of spectral features, different spectral techniques are used to derive features. Furthermore, the segmental features as parts of speech are considered along with Mel-frequency cepstral coefficients. Neural network-based models are tested for the task of classification. Finally, a deep RNN is used, evaluated, and found to be better than other methods that can lead to speech data mining. The training of RNN with these input features is performed using the stochastic gradient descent algorithm. The results show that the efficacy is higher than those reported in previous works.
IMMERSIVE TELECOMMUNICATIONS: A SURVEY AND FUTURE DEVELOPMENT

Currently there is a growing need for technology to enhance the ability of people to interact remotely. The technologies available at the moment can always meet the needs of users, in particular in matters of providing telepresence, as well as the transmission of haptic data. The article proposes the introduction of a new term – immersive technologies in telecommunications, as well as an overview of technologies and future research related to this subject. Research subject is immersive telecommunication technologies. Research method is an analysis of the literature on the subject. Core results are analysis and survey of the immerse communications equipment and network requirements. Practical relevance of the work is that the results can be used for immerse communications applications design and development.
Organizing Academic Mobility of Engineering Students to Universities of France

The paper is relevant due to the importance of academic mobility in integrating foreign students into the educational environment of French universities. The paper is to show that there are special educational programs that prepare foreign students to study in France. Each university offers its own training mechanism. Some universities supervise resident classes in the country of origin before students arrive in France, where students of different majors are mixed. Some cooperate with French language resource centers, where distance learning activities are often adapted to the problems of integrating students into French universities. Almost all French universities offer classes to adapt foreign students to the university environment within the university during their first year of studies. The paper also shows the French for Academic Mobility program implemented at KNRTU. This educational program include cultural, methodological, and linguistic aspects necessary for preparing students to study in France. The paper shows that in order to draw up and implement a program for preparing students for academic mobility, a survey was conducted on the reasons why students attend classes in preparation for studies at French universities. The analysis of the reasons listed allowed the teacher to properly draw up a plan and a program of classes in order to use more effective methods, techniques and means of training. The paper also presents the content of the developed program prepared according to the requirements of students.
Improved One-Dimensional Piecewise Chaotic Maps for Information Security

Chaos represents interesting features that are suitable for the cryptography domain. One-dimensional chaotic maps are widely used for security issues due to their simplicity and chaotic behavior versus other multidimensional chaotic maps that can be complex for hardware implementation and hard to analyze. However, classical one-dimensional chaotic maps present a reduced range of chaotic behavior. In this paper, we propose two new piecewise compound one-dimensional chaotic maps; an Altered Sine-Logistic map based on Tent map (ASLT) and a combined Cubic-Tent map (CT). The proposed compound maps combine classical and simple one-dimensional chaotic maps to produce an extensive range of chaotic behavior. The ASLT system comprises a combined Sine-Tent map in the first piece of the function and a combined Logistic-Tent map in the second piece of the function. Then, the CT map is based on the nonlinear fusion operation between the Cubic map and the piecewise Tent map. Simulation results and chaotic behavior analysis are provided using the bifurcation diagram, Lyapunov exponent, initial sensitivity, and Shannon entropy measure. The evaluation results demonstrate the effectiveness of the proposed 1D maps with better chaotic performances, chaotic range, and complexity compared to their corresponding classical chaotic maps. The simple structure and effectiveness of the proposed systems make them suitable for chaos-based cryptography providing better security strength and more randomness.
Research on Sarcasm Detection of News Headlines Based on Bert-LSTM

Sarcasm detection is usually difficult to be identified. Most of the existing sarcasm detection is based on the content of social networking sites, and there is very little sarcasm detection of news headlines. This paper proposes a news headline sarcasm detection based on Bert LSTM model, using Bert to process the word vector, taking the output result as the input of LSTM. The news headline data set written by public professionals which is deemed as professional and low noise, is used to verify this model, resulting that the model has a good recognition rate.
ADDAI: Anomaly Detection using Distributed AI

When dealing with the Internet of Things (IoT), especially industrial IoT (IIoT), two manifest challenges leap to mind. First is the massive amount of data streaming to and from IoT devices, and second is the fast pace at which these systems must operate. Distributed computing in the form of edge/cloud structure is a popular technique to overcome these two challenges. In this paper, we propose ADDAI (Anomaly Detection using Distributed AI) that can easily span out geographically to cover a large number of IoT sources. Due to its distributed nature, it guarantees critical IIoT requirements such as high speed, robustness against a single point of failure, low communication overhead, privacy, and scalability. Through empirical proof, we show the communication cost is minimized, and the performance improves significantly while maintaining the privacy of raw data at the local layer. ADDAI provides predictions for new random samples with an average success rate of 98.4% while reducing the communication overhead by half compared with the traditional technique of offloading all the raw sensor data to the cloud.