Reviewerly/exHarmony huggingface dataset:
Application of Classifier for Breast Cancer Cell Detection

Due to increased number of breast cancer cases, there is rise in concern about the disease. So, in today’s era, machine learning classification can play a vital role in classifying the degree of malignancy of the cancer. This paper uses four different classifiers such as KNN, logistic regression, naive Bayes and decision tree to classify the cancer into two classes of benign and malignant. The proposed model is simulated over Wisconsin diagnostic breast cancer dataset and evaluated by accuracy metric. The model classifies the disease based on the most accurate classifier. The KNN model shows significantly better result than the other classifier.
On/Off Covert Channel Capacity Limitation by Adding Extra Delays

Covert channel is a communication channel that was not designed for information transmission. Covert channels in IP networks can be implemented in a way that is difficult to detect. That is why the method of capacity limitation of a potential covert channel seems to be an effective counteraction that can be used when allowable level of covert channel capacity is set up. To effectively protect information from leakage, it is needed to estimate residual covert channel capacity when limitation method was implemented. Random delays before packet sending are considered as a tool of noise insertion into a covert channel. In this paper authors investigated the way to estimate covert channel capacity taking into account network load conditions and ways of random delay generation. The main topics of the research are cases when time intervals between packet sending and packet receiving in network comply with normal and exponential distributions.
CoUNet: An End-to-End Colonoscopy Lesion Image Segmentation and Classification Framework

Colonoscopy tissue slides examination can find cells of early-stage colon tumor, which is important to clinical diagnosis. DigestPath2019 in MICCAI is a recent challenge to automatically segment colonoscopy lesion regions and classify the whole slide image WSI into benign or malignant. To obtain high accuracy and refined boundary information for lesion segmentation, this paper proposes an end-to-end segmentation and classification framework CoUNet, which combines a segmentation model D-ResUNet and CRFasRNN. Our D-ResUNet combines Deformable ResNet and UNet structure, which modifies the last two residual blocks of ResNet into deformable residual blocks. This strategy can ensure extracting high-level semantic information and retaining low-level cell morphologic information to improve the edge and shape predictions. The connected CRFasRNN formulates the CRF as RNN to reduce noise prediction of segmentation model and refine final segmentation performance. We also propose WSI-level color normalization strategy to normalize each patch in WSI to relieve various staining problem and balanced dice loss for solving data-imbalanced problem. Experimental results on DigestPath2019 show the efficiency and robustness of our proposed framework.
Improved Parameters Estimation of Radar Pulses using Autocorrelation

Electronic Intelligence (ELINT) system provides significant advantages with enhanced range, high coverage, and portability. It has various challenges and is highly demanding. These systems should have a highly sensitive receiver with a capability to intercept, characterize and distinctly identify ground-based, shipborne, and airborne radars to meet the platform requirement. This paper describes an approach with low computations for real-time radar signal detection and pulse parameter estimation based on autocorrelation. The detailed feature extraction is carried out on the detected pulse data, leading to distinct identification of radar referred to as specific emitter identification.
Autonomous Navigation in Search and Rescue Simulated Environment using Deep Reinforcement Learning

Human assisted search and rescue (SAR) robots are increasingly being used in zones of natural disasters, industrial accidents, and civil wars. Due to complex terrains, obstacles, and uncertainties in time availability, there is a need for these robots to have a certain level of autonomy to act independently for approaching certain SAR tasks. One of these tasks is autonomous navigation. Previous approaches to develop autonomous or semi-autonomous SAR navigating robots use heuristics-based methods. These algorithms, however, require environment-related prior knowledge and enough sensing capabilities, which are hard to maintain due to restrictions of size and weight in highly unstructured environments such as collapsed buildings. This study approaches the problem of autonomous navigation using a modified version of the Deep Q-Network algorithm. Unlike the classical usage of the entire game screen images to train the agent, our approach uses only the images captured by the agent's low-resolution camera to train the agent for navigating through an arena avoiding obstacles and to reach a victim. This approach is a much more relevant way of decision making in complex, uncertain contexts; since in real-world SAR scenarios, it is almost impossible to have the area's full information to be used by SAR teams. We simulated a SAR scenario, which consists of an arena full of randomly generated obstacles, a victim, and an autonomous SAR robot. The simulation results show that the agent was able to reach the victim in 56% of the evaluation episodes after 400 episodes of training.
Research on Gesture Recognition Method Based on Deep Learning

Abstract Gestures are necessary and indispensable in people’s ordinary life communication. And gestures have become an important part of human-computer interaction. With the gradual maturity of human-computer interaction systems, the role of gestures in human-computer interaction is becoming more and more important. Due to the variety of gestures in practical use, coupled with factors such as environment and light changes, it makes it very difficult for computers to recognize the correct gestures from image information. Deep learning has achieved certain research results through several years of development, which provides new research ideas for gesture recognition. This paper firstly introduces the common methods and processes of gesture recognition, and then proposes a new convolutional neural network structure by studying convolutional neural networks and adding Inception structure to the network to improve the performance of the network. Finally, experimental validation is conducted, and the experimental results show that the new convolutional neural network achieves an average recognition rate of 97.8% on the test set, which is a good result.
Spark-based Distance Weighted Recommendation System Research

For the traditional recommendation system for big data computing, the processing speed is slow, the recommendation accuracy is not high, and the proximity principle cannot be reflected when recommending tourist attractions. The advantages of Spark parallelized computing and memory iteration operation are used, at the same time, the user similarity is added to the nearest neighbor characteristics and the weighted coefficient of the user score multiplied by the geographical location of the scenic spot, and the weighted recommendation system model based on Spark geographic location is proposed. According to the data set provided by the Tianchi data of Alibaba cloud, the system is 2.45 times faster than the traditional recommendation system running under MapReduce, and the recommended accuracy is significantly improved.
6DoF Tracking in Virtual Reality by Deep RNN Model

In this paper, a novel coordinates tracking method is proposed for Virtual Reality (VR) environment using sensor signals. The purpose is to extend movement tracking in VR from 3 Degrees of Freedom (DOF) of rotation to 6DOF of position plus rotation. As a result, we can track VR coordinates without using controller or handler provided by VR devices. An RNN-based model is proposed to predict displacement of positions in each timestamp given measured acceleration and Euler angles from sensor signals. Experiments demonstrate that it is effective to predict correct position displacement, which not only models the relationship between sensor signals and displacement but also handles the cumulative errors during tracking.
PGGAN: Improve Password Cover Rate Using the Controller

Abstract Password generation model based on generative adversarial network usually has the problem of high duplicate rate, which further leads to low cover rate. In this regard, we propose PGGAN model. It sets up an additional controller network which is similar to the discriminator in the aspect of structure and function. The discriminator and the controller respectively learn the measure between the distribution of generated password with the real password distribution and the uniform distribution, and then use two measures to teach generator meanwhile. By changing the activation function and loss function of the controller, different measure functions can be selected. The experimental results show that compared with GAN, our PGGAN performs better both in cover rate and duplicate rate. Moreover, Wasserstein distance usually has a better effect to the other measure in model. Specifically, PGGAN with Wasserstein distance can increase the cover rate by 3.57% and reduce the duplicate rate by 30.85% on rockyou dataset.
Reflection on Teaching Research of English Applied Linguistics Based on Big Data

Abstract The traditional English teaching is mainly dominated by the teacher and instilled in teaching. The students trained by this teaching mode have a low ability to acquire knowledge, and they often become dumb when they communicate with each other. With the continuous development of society, big data has been applied to all aspects of our life, constantly changing our existing education mode. Based on this, this paper establishes the teaching platform of English applied linguistics, and combines the teaching of English Applied Linguistics with big data in combination with the current teaching situation of English applied linguistics. In this paper, the students of grade 2020 are used as the control group, and the network teaching platform based on big data is used for teaching. The control group is the students of grade 2019, and the traditional teaching mode is used for teaching. More than 90% of the students’ oral ability can be improved by more than 90% of the students’ oral English in 2020.
Effective Transmission with Color Plane Correlations for Block Compressed Sensing

Effective transmission of multimedia plays an important role in modern-day communications. Besides, efficient compression scheme forms an inevitable part for multimedia communication systems. We select block compressed sensing for compressing color images, and then transmit compressed media over binary symmetric channels. Color planes in the color images are highly correlated. We expect to look for schemes to utilize the correlations with multiple description coding and polar codes. Experimental results have presented the enhancements under different channel conditions. The quality of reconstructed color images with our scheme outperform those without any protection applied.
Summarizing Online Product Reviews in Bengali Based on Similarity Using Sequence to Sequence RNNs

By text summarization, large texts or documents can be concise while preserving the context of the main document. This research work is focused on summarizing online product reviews in Bengali Language. By using online user feedback, future buyers will be able to take decisions whether to buy the product or not. However, it is very difficult for a buyer to decide by reading manually all the reviews. Machine learning techniques are available nowadays for text summarization which is a promising solution for extracting information from those user reviews. There are many well-known summarization tools for English language but in Bengali, there are very few and insufficient tools for text summarization. In this paper, a model has been for abstract Bangla text summarization on online product reviews using a Recurrent Neural Network(RNN). Long ShortTerm Memory (LSTM) and Sequence-to-Sequence (Seq2Seq) based RNN has been applied here. Experimented results underscore that the training loss is reduced to 0.0034 and able to generate a frequent predictive summary from original texts or documents. This research work also includes a Word Mover's Distance(WMD) method which checks the similarity between human and machine summary. It is observed that, the applied WMD in this work for text similarity model has been able to achieve better performance than the Jaccard method.
Solving Dynamic Bag-of-Tasks Scheduling Problem in Heterogeneous Multi-cloud Environment Using Hybrid Bi-Level Optimization Model

Task scheduling problem has attracted a lot of attention since it plays a key role to improve the performance of any distributed system. This is again more challenging, especially for multi-cloud computing environment, mainly based on the nature of the multi-cloud to scale dynamically and due to heterogeneity of resources which add more complexity to the scheduling problem. In this paper, we propose, for the first time, a new Hybrid Bi-level optimization model named HB-DBoTSP to solve the Dynamic Bag-of-Tasks Scheduling Problem (DBoTSP) in heterogeneous multi-cloud environment. The proposed model aims to minimize the makespan and the execution cost while taking into consideration budget constraints and guaranteeing load balancing between Cloud’s Virtual Machines. By performing experiments on synthetic data sets that we propose, we demonstrate the effectiveness of the algorithm.
Enhancements of Data Hiding Applications with QR Code Concealment

Quick response codes (QR codes) are commonly seen nowadays. They can be captured by mobile phone cameras, and the information therein, such as some designated website, can be interpreted and then be linked shortly. In this paper, we propose hiding QR codes, which act as watermarks, into original image. The QR codes contain information corresponding to the original image. The first and the second QR codes serve as robust watermark and fragile watermark, respectively. After experiencing possible attacks, the QR codes may fail to be accessed, and we propose the scheme to conceal the first QR code for robust watermarking. Simulation results have presented the effectiveness with our scheme.
Design and FPGA Implementation of an Efficient 8×8 Multiplier Using the Principle of Vedic Mathematics

This paper is regarding the design and analysis of an efficient 8×8 Vedic multiplier by the principle of Vedic mathematics []. Here, a unique Vedic multiplier architecture is adapted, which is not based on the regular method of multiplication (addition, shifting). The design is as per the "Urdhwa-Tiryakbhyam Sutra" of Vedic mathematics. Two numbers (binary of 8-bit each) are multiplied using the methodology of this principle/sutra. "Urdhwa-Tiryakbhyam" means "vertically and crosswise", wherein the partial products are computed at once, thus lessening the delay and hence making the multiplication faster. This 8 by 8 bit multiplier is coded in Verilog HDL and tested on a DE10-lite FPGA kit.
Analysis Of Methods Of Illegal Personal Data Distribution Of Messengers, Social Networks And Search Engines Users

This paper analyzes data leaks for different countries, taking into account the period of the Covid pandemic. The main dangers to society are described in the case of an increasing trend of personal data leakage of company customers and ordinary network users. The statistics are presented of accidental and intentional leaks of personal data in Russia and the world. The authors carried out the analysis and described the cases of user data transmission through popular search engines and IM services, including some of the flaws and security bottlenecks of applications. The analysis is carried out of possible threats, consequences of collection, theft and dissemination of personal data and methods of protection against them. Recommendations for protecting the corporate sector from data theft are described. The paper also offers relevant and accessible to everyone ways to combat the illegal distribution of personal data.
Detecting and learning from unknown by extremely weak supervision: exploratory classifier (xClass)

Abstract In this paper, we break with the traditional approach to classification, which is regarded as a form of supervised learning. We offer a method and algorithm, which make possible fully autonomous (unsupervised) detection of new classes, and learning following a very parsimonious training priming (few labeled data samples only). Moreover, new unknown classes may appear at a later stage and the proposed xClass method and algorithm are able to successfully discover this and learn from the data autonomously. Furthermore, the features (inputs to the classifier) are automatically sub-selected by the algorithm based on the accumulated data density per feature per class. In addition, the automatically generated model is easy to interpret and is locally generative and based on prototypes which define the modes of the data distribution. As a result, a highly efficient, lean, human-understandable, autonomously self-learning model (which only needs an extremely parsimonious priming) emerges from the data. To validate our proposal, we approbated it on four challenging problems, including imbalanced Faces-1999 data base, Caltech-101 dataset, vehicles dataset, and iRoads dataset, which is a dataset of images of autonomous driving scenarios. Not only we achieved higher precision (in one of the problems outperforming by 25% all other methods), but, more significantly, we only used a single class beforehand, while other methods used all the available classes and we generated interpretable models with smaller number of features used, through extremely weak and weak supervision. We demonstrated the ability to detect and learn new classes for both images and numerical examples.
Channel Prediction and Transmitter Authentication With Adversarially-Trained Recurrent Neural Networks

As wireless communications and interconnected networks become ubiquitous and relied upon, they must also remain secure. Advanced communication systems that use techniques to improve data throughput and minimize latency lend themselves to physical-layer authentication. The stochastic and dynamic nature of the wireless mobile channel provides features that can be extracted through deep learning. We propose a novel method to authenticate transmitters at the physical layer by leveraging channel state information to predict future channel impulse responses. Specifically, we compare the use of recurrent neural networks (RNNs) using long-short term memory (LSTM) and gated recurrent unit (GRU) cells with variations of a conditional generative adversarial network (CGAN) to authenticate transmitters in a mobile environment. Our evaluation shows that standalone RNNs using LSTM and GRU cells are adept at predicting future channel responses, however a CGAN-trained discriminator using GRU cells is able to match the authentication accuracy of a standalone network without using a predefined channel prediction error threshold. Using a discriminator trained by a CGAN with binary cross entropy loss in the discriminator and mean squared error loss in the generator, the neural network was able to authenticate at a 98.5% rate.
Application of Elastic Shape Analysis to User Authentication and Identification

Different methods of user identification and authentication are widely used in security systems. Touch screen-based methods are very convenient for users and do not require specialized equipment, although commonly used pattern locks alone provide a low level of security. Analysis of biometric features of performed gestures can greatly improve the security of touch screen-based identification and authentication, however typically a large amount of training samples is needed to build an accurate model. In this study a novel method based on Elastic Shape Analysis and covariance shrinkage is introduced to overcome this limitation. Two dataset (one with finger gestures and one with stylus gestures) were used. It is shown that 4 training samples are sufficient for identification among 24 people with accuracy of 84.7% using elastic Linear Discriminant Analysis. In user authentication, the area under the curve (AUC) score of 0.986 was reached using an elastic variant of Principal Component Analysis and 8 training samples per user. The approach can be easily adapted as a part of a touch-screen based user authentication or identification system.
Advances and Challenges in Unsupervised Neural Machine Translation

Unsupervised cross-lingual language representation initialization methods, together with mechanisms such as denoising and backtranslation, have advanced unsupervised neural machine translation (UNMT), which has achieved impressive results.Meanwhile, there are still several challenges for UNMT.This tutorial first introduces the background and the latest progress of UNMT.We then examine a number of challenges to UNMT and give empirical results on how well the technology currently holds up 1 .
Introduction to Blockchain Technology

In recent years, many technologies have emerged as a result of technological innovation. In the past few years, Blockchain technology or the technology of secure ledger has gained much attention. In the field of computing, Blockchain technology has been characterized as the fifth disruptive innovation. In a way, we can say that it is a distributed ledger of records which are absolute and certifiable. The technology of Blockchain is fundamentally a record of the distributed database or it is a public ledger of all the dealings or proceedings that are executed digitally and shared with other entries that are participating. Every transaction made in the public ledger is certified by mutual agreement of all the contributors in the arrangement. And after the entry of the information, it can never be erased. Each transaction made in the system can be easily verified and recorded in the case of Blockchain technology. After the advent of Blockchain technology in the year 2008, the concept of Blockchain technology has been used and applied in many different ways. The interest in this technology has increased due to its unique attributes and features of providing security, secrecy, and integrity of data without the intervention of the third party controlling the transaction, and therefore, it motivates many researchers to research to understand this technology by understanding its challenges, applications, and limitations. The most visible impact of the Blockchain technology can be witnessed as a multitude of cryptocurrencies that have emerged up. Furthermore, it is quite pertinent that the application of Blockchain technology is far ahead of cryptocurrency and much deeper than simple distributed ledger storage. This technology has been used by many sectors such as finance, manufacturing, education, and medicine to utilize the unique profits offered by this technology due to its unique technology. Blockchain technology offers unique benefits such as trust ability, collaboration, organization, identification, credibility, and transparency. Additionally, Blockchain technology the most frequent use of Blockchain is in the area of finance and banking, and also many experiments have been done by big corporate in other domains as well. This chapter will focus on various sectors and areas where the Blockchain technology has an impact and also discusses future implementation in different sectors.