Reviewerly/exHarmony huggingface dataset:
Mitigating Masquerade using Nonce in Symmetric Key Distribution - Survey

Key distribution deals with mechanisms for secure distribution of keys. Since symmetric key cryptography requires both parties(encryption and decryption) to use the same key, the security of key distribution techniques is pivotal to the secrecy of overall exchange. This process generally uses master keys and session keys for key distribution and can be further strengthened by making use of nonces. A nonce is a non-repeating value (number used once) which may or may not be random. Such a value can be incorporated in cryptographic algorithms so as to make guessing and predicting difficult for an adversary during the exchange of messages between two entities over the network. This can help in mitigating masquerading and replay attacks. A masquerade attack is a kind of active attack wherein one entity pretends to be a different entity. Some other kinds of active attacks such as replay attack and modification of messages can also be grouped under the umbrella of masquerade attack. Such attacks often take advantage of the predictable nature of certain steps during the exchange of messages between two entities over the network. In this paper, we explore the usage of nonces in various cryptographic and network security applications in symmetric key distribution environment so as to prevent active attacks like masquerade attacks.
Automatic Kidney Volume Estimation System Using Transfer Learning Techniques

Deep learning technology is widely used in medicine. The automation of medical image classification and segmentation is essential and inevitable. This study proposes a transfer learning–based kidney segmentation model with an encoder–decoder architecture. Transfer learning was introduced through the utilization of the parameters from other organ segmentation models as the initial input parameters. The results indicated that the transfer learning–based method outperforms the single-organ segmentation model. Experiments with different encoders, such as ResNet-50 and VGG-16, were implemented under the same Unet structure. The proposed method using transfer learning under the ResNet-50 encoder achieved the best Dice score of 0.9689. The proposed model’s use of two public data sets from online competitions means that it requires fewer computing resources. The difference in Dice scores between our model and 3D Unet (Isensee) was less than 1%. The average difference between the estimated kidney volume and the ground truth was only 1.4%, reflecting a seven times higher accuracy than that of conventional kidney volume estimation in clinical medicine.
Explainable Attentional Neural Recommendations for Personalized Social Learning

Learning and training processes are starting to be affected by the diffusion of Artificial Intelligence (AI) techniques and methods. AI can be variously exploited for supporting education, though especially deep learning (DL) models are normally suffering from some degree of opacity and lack of interpretability. Explainable AI (XAI) is aimed at creating a set of new AI techniques able to improve their output or decisions with more transparency and interpretability. In the educational field it could be particularly significant and challenging to understand the reasons behind models outcomes, especially when it comes to suggestions to create, manage or evaluate courses or didactic resources. Deep attentional mechanisms proved to be particularly effective for identifying relevant communities and relationships in any given input network that can be exploited with the aim of improving useful information to interpret the suggested decision process. In this paper we provide the first stages of our ongoing research project, aimed at significantly empowering the recommender system of the educational platform “WhoTeach” by means of explainability, to help teachers or experts to create and manage high-quality courses for personalized learning.The presented model is actually our first tentative to start to include explainability in the system. As shown, the model has strong potentialities to provide relevant recommendations. Moreover, it allows the possibility to implement effective techniques to completely reach explainability.
Fast Whole Slide Image Analysis Of Cervical Cancer Using Weak Annotation

Cervical cancer is the most common gynecological disease, which seriously endangers women's health. Histopathological diagnosis is the most reliable determinant for cervical cancer prognosis and treatment. However, it is a time-consuming and error-prone task for pathologists to manually analyze the whole slide image(WSI). Automated detection of cancer tissue and histopathological diagnosis are of great signification to reduce the workload for pathologists. So we proposed a method combining deep learning(DL) and traditional machine learning(ML) to realize the rapid analysis of the cervical cancer WSI. Our method first applied a patch-level network to detect the pathological patches and generate a heatmap that can highlight the tumor area. Considering the difficulty of obtaining pixel-level annotations, we designed a weak annotation strategy for training the network and verified the feasibility of this strategy on a public dataset. Besides, we used overlapping heatmap generation strategy(Overlapping HMGS) and feature sharing strategy of fully convolutional neural networks(CNN) to balance the accuracy and speed of tumor region detection. Then features extracted from the heatmaps are fed into a traditional machine learning model for the WSI-level prediction. Our approach not only achieved excellent performance with 97% accuracy on our cervical cancer WSI dataset, but it is also efficient and interpretable.
Basics of Matlab Programming

In this chapter, basics of MatLab programming are explained and demonstrated with the help of useful examples. Arithmetic operations over variables and matrix have been explained with a point of view to avoid common mistakes during implementation. Examples of writing codes and commands for equations used in signal and images processing have been given for clear understanding. Tips for plotting and figures are also provided. Creation of script and function files in MatLab has been explained with simple examples. Various conditional statements and looping concepts have been described properly in a simple and easy way. Loading, visualization, and understanding of one- and two-dimensional signals are demonstrated by taking examples of biomedical signals and images. Basics of filtering process for signal and images have also been explained with real and working examples. After exercising all the examples given in this chapter, readers will be able to implement and solve scientific problems and models in MatLab.
A Novel Low Power Method of Combining Saliency and Segmentation for Mobile Displays

Saliency, which means the area human vision is concentrated, can be used in many applications, such as enemy detection in solider goggles and person detection in an auto-driving car. In recent years, saliency is obtained instead of human eyes using a model in an automated way in HMD (Head Mounted Display), smartphones, and VR (Virtual Reality) devices based on mobile displays; however, such a mobile device needs too much power to maintain saliency on a mobile display. Therefore, low power saliency methods have been important. CURA tried to power down, according to the saliency level, while keeping human visual satisfaction. But it still has some artifacts due to the difference in brightness at the boundary of the region divided by saliency. In this paper, we propose a new segmentation-based saliency-aware low power approach to minimize the artifacts. Unlike CURA, our work considers visual perceptuality and power management at the saliency level and at the segmented region level for each saliency. Through experiments, our work achieves low power in each region divided by saliency and in the segmented regions in each saliency region, while maintaining human visual satisfaction for saliency. In addition, it maintains good image distortion quality while removing artifacts efficiently.
An Image Encryption Method Using Chaos and DNA Encoding

A simple method, for image encryption using 1D discrete chaos and DNA encoding, is proposed in this article. Here, chaotic maps are used to encode the secret image as DNA sequence and also to diffuse the encoded image with DNA operations and finally a cipher image is obtained. Additionally, hash function SHA-256 is employed to plain image and this makes the current method more robust. The result of the present method is studied and analyzed to established the aptness of the proposed method.
Talking to Voice Assistants: Exploring Negative and Positive Users’ Perceptions

The widespread use of voice assistants (VAs), which collect and process a huge volume of user data through user interactions, is beginning to attract the attention of marketing researchers. The early studies investigating consumers' negative (i.e., risk) and positive (i.e., benefits) perceptions related to users-VAs interactions appear partial and fragmented. Consequently, this paper aims at exploring the key perceptual factors of risk and benefits of interactions with VAs on smartphones. Then, we propose to profile Generation Y users according to their perceptions and analyse whether they are significantly different in terms of frequency of VA use, VA attitude, VA familiarity, innovativeness and age. By adopting a quantitative exploratory approach, 349 questionnaires are collected and validated. By running an exploratory factor analysis, a three-factor solution is outlined. Besides, the non-hierarchical k-means cluster analysis highlights three Generation Y user clusters. This research contributes to the nascent strand of studies on user perceptions related to interactions with VAs, by offering an overall view of perceived risk and benefits. Finally, we propose to marketers strategic guidelines in terms of targeting and marketing communication design.
On Ensembles, I-Optimality, and Active Learning

Abstract We consider the active learning problem for a supervised learning model: That is, after training a black box model on a given dataset, we determine which (large batch of) unlabeled candidates to label in order to improve the model further. We concentrate on the large batch case, because this is most aligned with most machine learning applications, and because it is more theoretically rich. Our approach blends three ideas: (1) We quantify model uncertainty with jackknife-like 50-per cent sub-samples (“half-samples”). (2) To select which n of C candidates to label, we consider (a rank- $$(M-1)$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mo>(</mml:mo> <mml:mi>M</mml:mi> <mml:mo>-</mml:mo> <mml:mn>1</mml:mn> <mml:mo>)</mml:mo> </mml:mrow> </mml:math> estimate of) the associated $$C\times C$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mi>C</mml:mi> <mml:mo>×</mml:mo> <mml:mi>C</mml:mi> </mml:mrow> </mml:math> prediction covariance matrix, which has good properties. (3) Our algorithm works only indirectly with this covariance matrix, using a linear-in- C object. We illustrate by fitting a deep neural network to about 20 percent of the CIFAR-10 image dataset. The statistical efficiency we achieve is $$3\times$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mn>3</mml:mn> <mml:mo>×</mml:mo> </mml:mrow> </mml:math> random selection.
Modified spectral method for optimal estimation in linear continuous-time stochastic systems

Abstract The spectral method to solve estimation problems for linear continuous-time stochastic systems with polynomial measurements is presented. It is based on both the spectral form of mathematical description (the representation of deterministic functions and random processes by orthogonal series) and the particle filter. The main goal of this work is to implement the continuous-time particle filter without a time discretization. The proposed spectral method provides the possibility to solve estimation problems such as filtering, smoothing and prediction.
Visual Intention Classification by Deep Learning for Gaze-based Human-Robot Interaction

In this work, we propose a deep learning model to classify a human’s visual intention in gaze-based Human-Robot Interaction(HRI). We consider a scenario in which a human wears a pair of eye tracking glasses and can select an object by gaze and a robotic manipulator picks up the object. A neural network is trained as a binary classifier to classify if a human is looking at an object. The network architecture is based on Fully Convolutional Net(FCN), Convolutional Block Attention Modules(CBAM) and Residual Blocks. We evaluate our model with two experiments. In one experiment we test the performance in the scenario where only a single object exists and the other one multiple objects exist. The results show that our proposed network is accurate and it can generalize well. The F1 score on the single object is 0.971 and 0.962 on multiple objects.
Reduced Complexity Neural Network Equalizers for Two-Dimensional Magnetic Recording

This article investigates the reduced complexity neural network (NN)-based architectures for equalization over the two-dimensional magnetic recording (TDMR) digital communication channel for data storage. We use realistic waveforms measured from a hard disk drive (HDD) with TDMR technology. We show that the multilayer perceptron (MLP) nonlinear equalizer achieves a 10.91% reduction in bit error rate (BER) over the linear equalizer with cross-entropy (CE)-based optimization. However, the MLP equalizer’s complexity is <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$6.6\times $ </tex-math></inline-formula> the linear equalizer’s complexity. Thus, we propose the reduced complexity MLP (RC-MLP) equalizers. Each RC-MLP variant consists of finite-impulse response (FIR) filters, a nonlinear activation, and a hidden delay line. A proposed RC-MLP variant entails only <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$1.59\times $ </tex-math></inline-formula> the linear equalizer’s complexity while achieving a 8.23% reduction in BER over the linear equalizer.
Designing reverse converter for data transmission systems from two-level RNS to BNS

Abstract In this paper, we present a reverse conversion from a two-stage Residue Number System (RNS) to a Binary Number System (BNS) with a special set of level 1 modules {2 α 1 , 2 α 2 − 1, …, 2 α n − 1} and level 2 modules {2 β 1 , 2 β2 − 1, …, 2 β k − 1}. The proposed method is based on the Chinese Remainder Theorem (CRT) with fractions and using a calculation method that uses constant multiplications to speed up calculations. This article discusses the simulation of FPGA reverse conversion to a two-stage RNS using the proposed method and the standard CRT using adders, their comparison of latency and hardware costs.
The Student's Anxiety and Resilience Research in the Context of the Transition to Distance Learning

The transition to the new training format usually causes different reactions among participants. Transition in a pandemic environment carries many risks for both teachers and students. The aim of this article is to investigate the anxiety and sustainability of students in the context of an unintentional transition to a new education format. Based on a representative sample, the authors have attempted to investigate the effects of studying under coronavirus conditions for foreign and Russian students. With the aid of modern research tools and programs for mathematical and statistical data processing, the indicators of personal and situational anxiety, resilience and academic performance of students at the beginning and the end of the first wave of COVID-19 were analyzed. The interrelationships between the components of the studied phenomena were identified, as well as the positive and negative aspects of the new online learning format from the student point of view. In particular, the results revealed the prolonged influence of resilience components such as social and emotional flexibility. There is also a link between situational anxiety and students' academic performance. The author's questionnaire identifying the specifics of distance learning during the pandemic led to the drafting of several recommendations to improve the use of forced distance learning.
SIM boxing – why mobile regulators need to act now

Mobile operators lose more than $28bn a year to telecoms fraud and revenue loss, according to the Communications Fraud Control Association (CFCA). In an industry where margins are increasingly being squeezed, fraud represents the single biggest preventable revenue threat to mobile operators, and in some regions SIM boxing is a significant contributor to the problem. Fraud represents the single biggest preventable revenue threat to mobile operators, and in some regions SIM boxing is a significant contributor to the problem. SIM box or bypass fraud takes place when individuals or organisations buy thousands of SIM cards to unlawfully terminate calls. A multi-layered approach, using intelligent technology solutions combined with tighter control over SIM card sales, and penalties for poor fraud management performance, are new weapons in network regulators' armoury in the fight against criminals, says Andy Gent of Revector.
Blind Recognition Algorithm for Scrambled Channel Encoder Based on the Features of Signal Matrix and Layered Neural Network

Blind recognition is of great significance in non-cooperative communication research. In practical communication scenarios, multistage signal processing schemes are usually used, including scrambling, coding, modulation, etc. However, most prior researches on blind recognition only focus on the scenario where the single signal processing method is applied, and it is impractical. In this paper, a blind recognition algorithm based on the characteristics of signal matrix and layered neural network for scrambled channel encoder is proposed. First, this method analyzes the difference between the matrix features in different types of scrambled channel encoders and regards it as an important condition for recognition. Then, the convolutional neural network is used to learn the scrambled codes and classify the different encoder types. At last, the layered neural network architecture of channel encoder type recognition is proposed, which can recognize different encoder types by categories. Moreover, it also can resist error code. In this paper, the recognition of 6 channel encoder types is realized with high robustness and adaptability. Simulation results show that the recognition rate of the convolution encoder has reached 95% when the bit error rate is 0.5%, which is better than the traditional channel encoder classification method based on matrix features.
A Fabric Way to Enhance Histogram Equalization &amp;amp; Support Vector Machine Method

Content Based Image Retrieval (CBIR) is a creating pattern in Digital Image Processing (DIP) for seeking and recovering the inquiry picture from extensive variety of databases. CBIR framework comprise of different stages to concentrate and match the features and find the pictures from the large scale picture databases based on visual substance such as Color, Shape and Texture according to the userâ€™s interest. To supply the reasonable answer to the client query, Content Based Image Retrieval provides some run of work. A new method is planned in this paper for color picture indexing by developing the ease of SVM system. A New technique is proposed on this paper for coloration photograph indexing via exploiting the simplicity of the Histogram equalization (HE) method. In this algorithm, now we have proposed a mixture of color, shape and texture features. In this approach, the previous work is enhanced to achieve better precision. In this paper we propose HE to enhance picture quality and connected separation network to improve result than base work. Then we encrypt the three channels separately. Here feature Extraction (FE) was viewed as the binary classification issue and SVM was utilized for arrangement this issue and the procedure of grouping is given to the entire image which are extracted after the feature extraction process.
SVM: An Approach to Detect Illicit Transaction in the Bitcoin Network

Blockchain Technology is one of the innovation projects launched in 2008 by Satochi Nakamoto, which makes this technology face to a set of attacks like money laundering, DDOs attack and illicit activity ... In this paper, we compare a Support Vector Machine algorithm with Random Forest and Logistic Regression to detect illicit transaction in the bitcoin network. The proposed approaches use the elliptic dataset. The used dataset contains 203.769 nodes and 234.355 edges, it allows to classify the data into three classes: illicit, licit or unknown. In this study, we consider unknown classes as licit. The accuracy reaches the 98.851% using Random Forest. The recall does not exceed 45% and the precision reaches 65.90% using Random Forest.
Secure Group Data Sharing with an Efficient Key Management without Re-Encryption Scheme in Cloud Computing

Cloud computing becomes an essential tool for internet users which provide various computing resources over the cloud. Providing on-demand storage is being one of the major services which is effective to meet out the expectations of an organization when utilizes the computational resources while accessing large amount of data. Storing the data on cloud is being a big challenge for the researchers in terms of maintaining the protection and security of data. Sharing of the data among a group can lead to insecurity of data from external and internal threats. This paper introduces a protected data sharing framework in the cloud storage that maintains the privacy and confidentiality of data. The proposed method uses the MD5 hash function that is relatively faster than the SHA256 hash function for checking the data integrity. The framework also provides a check on accessing and sharing of data. Exhaustive re-encryption computations are avoided and a single encryption key is used to encrypt the entire file. There are two distinct key shares for each of the users and user is allowed to share one at a time get access data. In this way having access to a single portion of a key permit the framework to safeguard the data against internal threats. Cryptographic server is used to store the other key share and performs all the expensive computations which are considered as a trusted third party. Proposed work also measure the efficiency of the model based on the time taken to execute the various operations.
Low-Resolution Face Recognition System Using Siamese Network

Practically face recognition is applied to a not really high-resolution face image due to hardware limitation such as surveillance camera or low-quality webcam. Low resolution face recognition system is a system consists of several key components which are face detection, super resolution, and face recognition. Siamese network is useful to be implemented as a part of face recognition component for recognizing unbounded face identity and doing one shot learning to record newly recognized identity. In this paper, there are three main goals to be achieved in the end of the research which are: 1) developed a complete low resolution face recognition system, 2) compared quantitatively between every techniques for each component, 3) using the best technique to be implemented for each component in the system. We have successfully achieved 93% accuracy for 36×36 face image, 84% for 24×24, and 56,2% for 12×12 with 0.086s execution time.