Reviewerly/exHarmony huggingface dataset:
A Foreground Mask Network for Cell Counting

Cell counting is important in medical image analysis for its meaningful information. In this paper, we propose a cell counting network to predict the number of cells in an image with the distribution of cells. The proposed network learns to predict the density map which has a direct relationship with the number of cells. A foreground mask is designed to filter the low-level feature maps and the favorable information is fed to the decoder to recover the spatial information better. The foreground mask is a probability map indicating the pixels are more likely to belong to cells. Experiments on three public datasets show that the proposed model can achieve promising performances. Especially the ablation study on the Adipocyte Cells demonstrates the necessity of the foreground mask.
A Type-2 Fuzzy Genetic Approach to Uncertain &amp; Dynamic Resilient Routing within Telecommunications Networks

Data and voice connections are an important part of any modern society, thus it is important for telecoms companies to provide resilience for network routing. Network conditions can fluctuate, redefining an optimal path through a network, which can introduce high levels of uncertainty. In this paper, we will present a type-2 fuzzy genetic system that can provide multiple routes between two locations with some ability to resist network fluctuations and uncertainty. We have tested the proposed system against a real world telecommunications data set with high levels of uncertainty, and compared this to a type-1 fuzzy system counterpart and the crisp system employed in the industry. The results indicate an average reduction in the occurrence of unique routes by 13%. Moreover, on average it increases the occurrence of a route occurring more than once by 26% when compared to a crisp version of the system. With Dijkstra's algorithm and A* being unable to perform the resilient routing task effectively.
Runtime Verification of Linux Kernel Security Module

The Linux kernel is one of the most important Free/Libre Open Source Software (FLOSS) projects. It is installed on billions of devices all over the world, which process various sensitive, confidential or simply private data. It is crucial to establish and prove its security properties. This work-in-progress paper presents a method to verify the Linux kernel for conformance with an abstract security policy model written in the Event-B specification language. The method is based on system call tracing and aims at checking that the results of system call execution do not lead to accesses that violate security policy requirements. As a basis for it, we use an additional Event-B specification of the Linux system call interface that is formally proved to satisfy all the requirements of the security policy model. In order to perform the conformance checks we use it to reproduce intercepted system calls and verify accesses.
Genuinity Detection of People: A Comparative Analysis on HOG and One Shot Learning

Financial companies that provide monetary services like loan approval, credit cards etc. need high assurance of the genuinity of customers which can be done by matching the information provided by the user and the details on their respective Identity cards. Manually checking up such details can be a tedious task and also lead to errors.This paper discusses a novel approach to check the genuinity by using different techniques of face recognition namely, Dlib frontal face, one shot learning and face matching techniques like cosine similarity. This paper also discusses the problems faced during the implementation of these techniques. The data set used, contains images of Indian Government Identity proofs like Aadhaar card, PAN card and Driving License. The basic idea is to crop the face from the images (ID cards) and match them.
A Three-Pronged Approach to Mitigate Web Attacks

In recent days, one of the popular Web servers used by the majority of Web sites in the globe is Apache. Furthermore, lack of proper tuning of these Web servers causes delay in the network by 40% approximately. The present digital world depends upon Web applications to deliver its functionality. Economies, governments, organizations, and ultimately people rely heavily on the performance of the Web applications. Hence, in real time, the practical problem arises in tuning of the Web servers. The Web servers and poor coding of Web applications are the source for the vulnerabilities in the Web. In this, paper focuses on defending the vulnerabilities in Web applications deployed on Apache Web server through protecting the HTTP header and implementing the secured configuration of Apache Web server and ModSecurity Web application firewall.
A Theoretical Study on Advances in Streaming Analytics

Streaming analytics is an ever-growing field in today's world of continuous data generation. Terabytes of data is generated every day, every second from modern digital technologies such as cloud computing and the Internet of Things. Analyzing this massive data require good knowledge of available tools and technologies, and their advantages and limitations. In order to address this issue, in this paper we analyze the advances in the field of streaming analytics. This study can act as a guide for researchers and organizations that plan on implementing real-time streaming analytics tools and frameworks. This study also points out the scope of future work for researchers, based on the limitations of currently available frameworks.
Does ensemble really work when facing the twitter semantic classification?

With the rapid development of Internet social media, twitter has gradually become the most mainstream information release and information sharing platform. A large number of twitter users use the platform to express their views, emotions and opinions. However, it is still a challenge on twitter semantic classification based on the observation that Twitters are short, noisy, arbitrary, etc. Thus, we seek in the mainstream NLP algorithms to find out which algorithm performs best in this problem. After that, we analysis the ensemble methods on the former encode expand to get a better result. However, we find that it dosen't work well as we expected. we analysis the reason and give the potential explain. The extensive experiments have shown that the LCF-BERT based model performs best over the mainstream algorithms and the ensemble model on the Twitter dataset.
IoT Inflated Defense Surveillance Entity Applying Human Intelligence via Faster R-CNN: An Approach

With a deep learning perspective, we describe a novel approach towards object detection effectively at sensitive border control regimes (the so-called Line of Control) by using a neural network approach viz. region convolutional neural network (R-CNN). This approach facilitates faster, accurate and efficient Smart Intrusion Disclosure Entity (SIDE) using Human Intelligence via Internet of Things (IoT). To achieve this goal, we propose an algorithm that displays image processing and deep learning positioned data to separate intruders as a hazard and alerts the defense control center accordingly. The image captured is discursive towards elements by utilizing Faster R-CNN innovation. The lineaments are eradicated from the training posture by utilizing a unique model. Classification is done by using Faster R-CNN algorithm. The percentage accuracy of an image is analyzed in terms of precision, accuracy, recall, specificity and F1 score. This demonstrates that the intended algorithm is robust, accurate and precise. Our technique accomplishes the exactness focused on the Faster R-CNN innovation which is faster during both training and inference.
Artificial Intelligence, Marketing, and the Fourth Industrial Revolution

Artificial intelligence has been part of the world of marketing for some time now. This chapter will look at how artificial intelligence is defined and classified, illustrating its potential for the marketing domain with a variety of examples from various industries and sectors. Ethical concerns arising from the application of AI marketing will be discussed in the second part of this chapter. Before concluding, three brief case studies will give further insights, looking in detail at the AI activities of Airbnb, NYC's Metropolitan Museum of Artificial Intelligence, and retail giant Walmart.
Object Removal and Image Restoration within Subspaces by Prioritized Patch Optimization

The purpose of this work is to develop a robust technique for image inpainting to restore small cracks as well as large regions those include the regions developed by object removal. The image to be restored is transformed into sub-bands by the use of DWT (Discrete Wavelet Transform). These sub-bands are reconstructed back to spatial domain to obtain the subspaces images that are at the same scale as the original image to be restored but having different resolutions. These subspace images are then subjected individually to ‘prioritized exemplar approach’ to fill-in different structures and textures simultaneously. We also optimize the patch size to cope up different sizes of textures, structures and varying resolution of the subspace images. These restored subspace images are superposed to obtain the final restored image. A number of images with changing complexion are used to estimate the effectiveness of the algorithm. The results shows visually plausible background where from the object is removed in variety of images with different structures and textures. The RMSE (Root Mean Squared Error) and PSNR (Peak Signal to Noise Ratio) measures are used to quantify the improvement over visual quality of the restoration.
Monte Carlo Tree Search for the Game of Diplomacy

Monte Carlo Tree Search (MCTS) is a decision-making technique that has received considerable interest in the past decade due to its success in a number of domains. In this paper, we explore its application in the “Diplomacy” multi-agent strategic board game, by putting forward and evaluating eight (8) variants of MCTS Diplomacy agents. In the core of our MCTS agents lies the well-known Upper Confidence Bounds for Trees (UCT) bandit method, which attempts to strike a balance between exploration and exploitation during the search tree creation. Moreover, we devised a heuristic weighting system for prioritizing the tree nodes’ actions, and used it to effectively incorporate high-quality domain knowledge in some of our agents. We provide a thorough experimental evaluation of our approach, in which we systematically compare the performance of our agents against each other and against other opponents, including the state-of-the-art Diplomacy agent, DBrane. Our results verify that several of our agents are highly competitive in this domain, exhibiting as they do performance which is comparable to, and in some instances superior to, that of DBrane. Interestingly, the MCTS approach consistently outperforms all others in tournaments in which one MCTS agent faces one D-Brane agent and several other opponents.
Design and Implementation on EMBA Authentication models

Authentication systems in general have faced long-term security deficit despite convoluted sophistication within various systems for decades. Unfortunately, humans serve as the weakest prey for exploitation within any security chain. Unskillful and inefficient communications between systems and humans give rise to multiple vulnerabilities related to any security model. Additionally, the Passwords/Personal Identification Numbers (PIN's) fail to provide idealistic security due to the lack of flawlessness in traditional password systems. On the contrary, since eye movements serve as a natural interaction modality, it was observed that the amalgamation of eye-tracking models enhances the overall security. In this context, the work presented in this paper leads to an extensive study and survey of many research works pertaining to existing Eye Movement authentication models. Besides, high-level overview of many conventional authentication methodologies have been discussed that can be launched in such EMBA models. Additionally, comparative analysis and performance metrics of different EMBA system is discussed. It was observed that the eye-password method has highest accuracy (97%) and eye motion based techniques has lowest accuracy (60%). Finally, an eye pupil tracking based authentication model has been proposed with accuracy of Eye detection, Eye open or closed detection and Eye pupil tracking detection are 98%, 92.51% and 96.25% respectively.
A Deep Learning Approach for Efficient Registration of Dual View Mammography

In a standard mammography study, two views are acquired per breast, the Cranio-Caudal (CC) and Mediolateral-Oblique (MLO). Due to the projective nature of 2D mammography, tissue superposition may both mask or mimic the presence of lesions. Therefore, integrating information from both views is paramount to increase diagnostic confidence for both radiologists and computer-aided detection systems. This emphasizes the importance of automatically matching regions from the two views. We here propose a deep convolutional neural network for the registration of mammography images. The network is trained to predict the affine transformation that minimizes the mean squared error between the MLO and the registered CC view. However, due to the complex nature of the breast glandular pattern, deformations due to compression and the paucity of natural anatomic landmarks, optimizing the mean squared error alone yields suboptimal results. Hence, we propose a weakly supervised approach in which existing annotated lesions are used as landmarks to further optimize the registration. To this aim, the recently proposed Generalized Intersection over Union (GIoU) is exploited as loss. Experiments on the public CBIS-DDSM dataset show that the network was able to correctly realign the images in most cases; corresponding bounding boxes were spatially matched in 68% of the cases. Further improvements can be expected by incorporating an elastic deformation field in the registration network. Results are promising and support the feasibility of our approach.
Fault Diagnosis Method of Shortwave Transmitting System Based on IAGA-BP Neural Network

In this paper, a diagnosis model of shortwave transmitting system based on improved adaptive GA-BP neural network is designed, aiming at numerous varieties and unobvious difference of the state of shortwave transmitting system. The idea of adaptive adjustment of crossover probability and mutation probability in genetic algorithm is introduced into the optimization of fault diagnosis model based on BP neural network. The experiments on the monitoring data of the shortwave transmission system show that the improved fault diagnosis algorithm has stronger convergence ability and recognition accuracy, which can effectively improve the fault diagnosis efficiency of the monitoring system and better enhance the practical value of the monitoring system.
Privacy information antistealing control method of medical system based on cloud computing

Summary With the development of cloud computing, the security of information has gradually become the focus of people. The patient information processed by the medical system has a very high sensitivity and privacy. If there is a leak, it will have a great impact on the patients. The purpose of this study is to explore the privacy information antistealing control method of medical system based on cloud computing. In this paper, through big data analysis, experimental research, variable comparison, and other methods, combined with modern security management means, we carry out the algorithm experiment and research on the privacy information antistealing control means. The result is that the running time of the three algorithms is 18, 16, and 10 s in turn when the algorithm runs 1000 times. It can be seen that the operation time of the enhanced encryption algorithm in this study is less than the initial algorithm by 8 s, the speed is increased by more than 40%. The conclusion is to improve the efficiency of data computing time and make the encryption process more efficient and accurate. The privacy data protection of medical system plays a good role.
New Methods and Abstractions for RSA-Based Forward Secure Signatures

We put forward a new abstraction for achieving forward-secure signatures that are (1) short, (2) have fast update and signing and (3) have small private key size. Prior work that achieved these parameters was pioneered by the pebbling techniques of Itkis and Reyzin (CRYPTO 2001) which showed a process for generating a sequence of roots $$h^{1/e_1}, h^{1/e_2}, \ldots , h^{1/e_T}$$ for a group element h in $$\mathbb {Z}_N^*$$ . However, the current state of the art has limitations. First, while many works claim that Itkis-Reyzin pebbling can be applied, it is seldom shown how this non-trivial step is concretely done. Second, setting up the pebbling data structure takes T time which makes key generation using this approach expensive (i.e., T time). Third, many past works require either random oracles and/or the Strong RSA assumption; we will work in the standard model under the RSA assumption. We introduce a new abstraction that we call an RSA sequencer. Informally, the job of an RSA sequencer is to store roots of a public key U, so that at time period t, it can provide $$U^{1/e_t}$$ , where the value $$e_t$$ is an RSA exponent computed from a certain function. This separation allows us to focus on building a sequencer that efficiently stores such values, in a forward-secure manner and with better setup times than other comparable solutions. In addition, our sequencer abstraction has certain re-randomization properties that allow for constructing forward-secure signature schemes with a single trusted setup that takes T time and afterward individual key generation takes $$\lg (T)$$ time. We demonstrate the utility of our abstraction by using it to provide concrete forward-secure signature schemes. We first give a random-oracle construction that closely matches the performance and structure of the Itkis-Reyzin scheme with the important exception that key generation can be realized much faster (after the one-time setup). We then move on to designing a standard model scheme. We believe this abstraction and illustration of how to use it will be useful for other future works. We include a detailed performance evaluation of our constructions, with an emphasis on the time and space costs for large caps on the maximum number of time periods T supported. Our philosophy is that frequently updating forward secure keys should be part of “best practices” in key maintenance. To make this practical, even for bounds as high as $$T=2^{32}$$ , we show that after an initial global setup, it takes only seconds to generate a key pair, and only milliseconds to update keys, sign messages and verify signatures. The space requirements for the public parameters and private keys are also a modest number of kilobytes, with signatures being a single element in $$\mathbb {Z}_N$$ and one smaller value.
Super Sort Algorithm Using MPI and CUDA

Sorting algorithms have been a subject of research. Throughout the years various sorting algorithms have been implemented and their performance has been evaluated by comparing the space and time complexity. In this paper the super sort sorting algorithm with time complexity O(nlogn) has been implemented with MPI and CUDA. The intention is to compare the time taken by super sort algorithm when executed sequentially using C program and the time taken when implemented using CUDA and MPI.
Research on Image Encryption in Secure Communication System of Space Laser Chaos Keying

In this paper, short reference code index differential chaotic shift keying can enhance the security performance of the system in the space laser communication system. Through the transmission of picture information, the confidentiality of the system is analyzed. The pixel correlation analysis, statistical histogram, and plaintext sensitivity prove that the system has high security performance.
Fast moving and deformational target tracking approach based on heterogeneous features fusion

Aiming at the unstable performance on fast-moving and deformational target tracking in current kernel correlation filter (KCF), an innovative approach based on heterogeneous features fusion is proposed in this paper. Firstly, a histogram of oriented gradient is utilized to cater for motion state change in complex surveillance background. Combined with a colour-free template as a novel heterogeneous feature, the proposed approach improves the tracking performance on the fast-moving target in KCF. Subsequently, the optimized spatial regularization and quadruple block method are implemented in order to solve the difficulties of scale change and boundary effect in the cyclic matrix. The simulation results indicate that the proposed approach has better precision and success rate than other popular tracking algorithms when dealing with both fast motion and deformation on the platform of the object tracking benchmark and actual traffic datasets.
Field Extraction and Logo Recognition on Indian Bank Cheques Using Convolution Neural Networks

A large number of bank checks are processed manually every day, across the world. In a developing nation like India, cheques are significant instruments for achieving cashless transactions. Cheque processing is a tedious task that can be automated with advanced deep learning architectures. Cheque automation involves selecting the Regions Of Interest (ROI) and then analyzing the contents in the ROI. In this paper, we propose a novel approach to extract ROI (fields) on the cheque using a Convolutional Neural Network (CNN)-based object detection algorithms like YOLO. By virtue of employing a CNN-based model, our approach turns out to be scale, skew, and shift invariant. We achieved a mean average precision (mAP) score of 86.6% across all the fields on a publicly available database of cheques. On the extracted logo field from YOLO, we performed logo recognition using VGGnet as a feature extractor and achieved an accuracy of 99.01%.
Object Tracking Platform for Color Object Detection using Genetic Algorithm Optimization

The aim of this work is to resolve typical tracking's challenge, which is object detection in the scene. In this context, a new robotic system to detect objects in unknown indoor and outdoor environment is realized. The developed robotic system is equipped with ultrasonic sensor and camera, gives visually information about the environment around detected object. Therefore, the realized system works in real time by analyzing camera data making it possible to detect color, size and position of the object. The aim of the proposed strategy is to find a compromise between robustness and processing speed of color detection based on rapid threshold, which produces a much smaller number of edge pixels compared to standard approaches based on a simple threshold. This reduction significantly reduces the number of votes required for robust real-time detection of object parameters. The approach consists of two main phases. In the first calibration phase, which takes place offline, a heuristic method of color classification using genetic algorithms is learned from a copy of an image of our colored environment. Then, in the real-time monitoring phase, the color classification is applied to the input images, the object is detected and its position is returned. To boost the detection accuracy the fixed and adaptive threshold methods are tested and compared with the genetic algorithm method, where the last method presents a good results. Accordingly, an exact determination of position, orientation of mobile platform, and accurate determination of color object in the environment is succeed.