Reviewerly/exHarmony huggingface dataset:
A Novel Evolution Strategy of Level Set Method for the Segmentation of Overlapping Cervical Cells

Development of an accurate and automated algorithm to completely segment cervical cells in Pap images is still one of the most challenging tasks. The main reasons are the presence of overlapping cells and the lack of guiding mechanism for the convergence of ill-defined contours to the actual cytoplasm boundaries. In this paper, we propose a novel method to address these problems based on level set method (LSM). Firstly, we proposed a morphological scaling-based topology filter (MSTF) and derived a new mathematical toolbox about vector calculus for evolution of level set function (LSF). Secondly, we combine MSTF and the mathematical toolbox into a multifunctional filtering algorithm 2D codimension two-object level set method (DCTLSM) to split touching cells. The DCTLSM can morphologically scale up and down the contour while keeping part of the contour points fixed. Thirdly, we design a contour scanning strategy as the evolution method of LSF to segment overlapping cells. In this strategy, a cutting line can be detected by morphologically scaling the union LSF of the pairs of cells. Then, we used this cutting line to construct a velocity field with an effective guiding mechanism for attracting and repelling LSF. The performance of the proposed algorithm was evaluated quantitatively and qualitatively on the ISBI-2014 dataset. The experimental results demonstrated that the proposed method is capable of fully segmenting cervical cells with superior segmentation accuracy compared with recent peer works.
Breast Cancer Classification Using Deep Learning

Cancer in any form is one of the most deadly illnesses in the world. Scientists are investigating into this disease and developing methods and treatments to fight it. The recent surveys show that breast cancer is also one of the major causes of mortality rate among female population around the world. Breast cancer’s definition may be explained as some old cells that aggressively grow out of control to form a population of a harmful mass in the breast tissue. Eventually, as a result they lead to the formation a malignant tumor. Deep learning (DL) that is the subfield of machine learning algorithms provides a powerful tool to help experts to analyze, model and make sense of complex clinical data across a broad range of medical applications. The aim of this study is to develop an efficient system to classify breast tumors as malignant and benign. This system is divided in two stages. The first stage is the normalization of the data. The second stage is the classification of tumors. The accuracy of the approach is 98.42%. The overall result showed that the DL outperformed the previous studies where the same data set was used.
Asset Security in Data of Internet of Things Using Blockchain Technology

In recent years, asset management gathered the attention of many enterprises to mark it as one of the key points to success. Blockchain and IoT are the propitious technologies which have the potential to manage the utilization of asset efficiently, properly, effectively, and economically. In the Internet of things (IoT), smart devices or sensors collect the information related to the asset. Since assets are the backbone of any enterprises, it is either tangible or intangible. Therefore, making these assets secure, self-organized, and self-adaptive, Blockchain comes into consideration. Blockchain is a distributed and decentralized ledger having blocks of transactions appended on each other in chronological order. It helps in federating the enterprises in one platform in terms of security and trust. The key value of this chapter is to establish trust among enterprises by using Blockchain and IoT technologies so that asset is regularized, deployed, operated, and maintained properly. This chapter basically centers on asset management in terms of viable usage, minimal maintenance charges by making use of Blockchain and IoT. With this research avenue, the present chapter proposes the asset security required in Blockchain and IoT techniques.
Color quotient based mask detection

The paper deals with mask detection in the age of COVID -19, by proposing a simple and efficient method to detect people not wearing mask.The approach includes a feature extraction step followed by a supervised learning model built with support vector machines.The features are formed of color information by considering red, green and blue channels for an RGB color image.Ratio of color channels is taken into account to discriminate between mask and non mask images.The approach has been tested on a set of 1211 facial images extracted from group of people wearing or not wearing a mask, by considering a 2 -class problem, where the mask class represents the positive examples, where the non-masked faces are negative examples.Part of the image data set is used to train the support vector machines for learning discriminant features for each class, followed by a prediction for each test sample.The image set for the mask class ranges from simple and common one-colored surgical masks to complex and challenging patterned masks.Cross-validation approach is adopted to test the approach, leading to 97.25 % as recognition rate.
An Empirical Study of Blockchain Technology, Innovation, Service Quality and Firm Performance in the Banking Industry

Despite the potential promises that blockchain technology (BT) offers to the financial services sector, its large-scale implementations are still in a nascent stage. There is no consensus on what benefits BT may bring, and there is always a possibility of difference between expected benefits and experienced real-world impact. Since the actual impact can be assessed only after large-scale implementations by financial institutions, there is little empirical evidence available in the literature. In this context, this research seeks to explore the potential impact of BT by developing and empirically testing a model. For this purpose, we have identified four dimensions of BT, namely, Decentralization, Transparency, Trustlessness, and Security. The impact of BT on innovation, service quality, and firm performance is assessed based on the extent to which these dimensions are present in the organization. The linkages of the latent constructs are estimated by analyzing the primary data collected from senior managers of various banks in India. The findings of this study provide several important considerations regarding the implementation of BT.
RETRACTED: Human motion image detection and tracking method based on Gaussian mixture model and CAMSHIFT

In the Image detection scene analysis of human motion based on motion feature extraction and recognition, there is a lot of information. Much work, trying to hide behind the gesture mode, sudden movement and walking, then recognizes people by analyzing movement behavior. Work focuses on some of the enhancements and motion extraction and tracking Compared with traditional methods. The first step in the analysis is the behavior of the Image detection extraction region movement. The second stage is dedicated to motion tracking. The level of performance is based on two Visual quality and quantity. Detection and tracking of human motion analysis. A system is a person for detecting and monitoring all automated Image detection surveillance systems, indoor and outdoor environments. Detection and tracking are achieved in several steps. Gaussian combination model is utilized to speak to the likelihood of a common dispersion model subsets inside the populace all in all. When all is said in done, the model permits the model mixture programmed preparing parcel, not knowing the subset of these information focuses have a place. This establishes a type of solo learning; for example, the sub-bunch task is obscure. The model used to obtain the use of background subtraction of foreground pixels. Clear noise and object detection applied to the human body model and then recognized that a person is walking or running, and monitoring in human activities. It proposed a changed variant CAMSHIFT calculation can be utilized for following of staff. Join the movement data of the proposed calculation it permits to monitor the individuals CAMSHIFT calculation, and Even on the off chance that it is an achievement of blockage circumstance. Analysis of the outcomes has demonstrated the consistency of the calculation. For instance, the Image location has been utilized for the primary test basic CAMSHIFT. There is no development data in the calculation. CAMSHIFT No on the development data isn't a tracker. After conclusion, this implies shading Information isn't sufficient to monitor the individuals that have been obstructed. It is utilized to test the proposed following calculation a similar Image location Color histogram and simultaneously utilize the movement data.
New method for improving add round key in the advanced encryption standard algorithm

(2021). New method for improving add round key in the advanced encryption standard algorithm. Information Security Journal: A Global Perspective: Vol. 30, COVID-19 Pandemic Crisis and its Implications on Cybersecurity, pp. 371-383.
Improving KNN Algorithm Based on Weighted Attributes by Pearson Correlation Coefficient and PSO Fine Tuning

Assigning proper weights to attributes in some datasets according to their importances can significantly improve the classification accuracy. Weighted attributes can support the classification methods effectively if their weights truly represent by their importances. In this research, we improve the K - Nearest Neighbors (KNN) algorithm by using Pearson correlation coefficient along with Particle Swarm Optimization (PSO) to find the optimal set of weights for attributes in the dataset. The experimental results show that the proposed method can significantly improve the classification accuracy when compared to the traditional KNN algorithm.
Actively Articulated Wheel-on-Limb Mobility for Traversing Europa Analogue Terrain

Exploring the water-rich surface of Europa is a desirable planetary exploration objective for the coming decades. Knowledge of the Europan terrain is relatively uncertain and therefore to perform mobile surface operations a versatile mobility system capable of adapting its locomotion is desirable. Our work summarizes preliminary efforts to evaluate a variety of wheel-on-limb mobility modes on Europan analogue terrain populated with unstructured salt-evaporite formations. A redundant wheel-on-limb quadruped, RoboSimian, has been adapted to perform traverses over unstructured terrain with obstacles up to 50 cm tall (1.6 times its wheel diameter). Multiple mobility modes that are combinations of wheeled and limbed motion primitives are proposed and evaluated over varying roughness terrain as a function of energy usage. In combination with these mobility modes, a mode-agnostic actively articulated suspension control scheme that maintains ground traction, regulates chassis orientation and prevents suspension saturation is presented. The controller uses an inverse kinematics solver that leverages pre-computed lookup-tables for each limb. This is shown to be simple yet effective in the field and is able to scale to a robot with 32 degrees-of-freedom.
Reinforced Online Parameter Adaptation Method for Population-based Metaheuristics

Online parameter control has proved to offer significant performance boost in metaheuristics. Common parameterization methods (tuners) typically involve their own parameters that affect their dynamics. Although the potential of completely removing the user out of the tuning cycle is questionable, taking more informative autonomous decisions is highly desirable as part of the general pursuit of artificial intelligence. Towards this goal, we propose an enhanced version of a recently proposed gradient-based parameter adaptation method that can be used by any population-based metaheuristic. The enhancement lies in the use of reinforcement learning to adapt the tuner's parameters, offering overall performance gains. The REINFORCE family of algorithms is considered in the present paper as the proof-of-concept approach. The proposed method is demonstrated on the differential evolution algorithm, which stands among the most popular metaheuristics. Experimental assessment on an established test suite offers promising results, also verifying previous evidence regarding the tuner's robustness and simplicity.
A Yoking-Proof Based Remote Authentication Scheme for Cloud-Aided Wearable Devices (YPACW)

The developments made in IoT applications have made wearable devices a popular choice for collecting user data to monitor this information and provide intelligent service support. Since wearable devices are continuously collecting and transporting a user's sensitive data over the network, there exist increased security challenges. Moreover, wearable devices lack the computation capabilities in comparison to traditional short-range communication devices. In this paper, authors propounded a Yoking Proof based remote Authentication scheme for Cloud-aided Wearable devices (YPACW) which takes PUF and cryptographic functions and joins them to achieve mutual authentication between the wearable devices and smartphone via a cloud server, by performing the simultaneous verification of these devices, using the established yoking-proofs. Relative to Liu et al.'s scheme, YPACW provides better results with the reduction of communication and processing cost significantly.
Extended variational inference for gamma mixture model in positive vectors modeling

Bayesian estimation of finite Gamma mixture model (GaMM) has attracted considerable attention recently due to its capability of modeling positive data. With conventional variational inference (VI) frameworks, we cannot derive an analytically tractable solution for the variational posterior, since the expectation of the joint distribution of all the random variables cannot be estimated in a closed form. Therefore, numerical techniques are commonly utilized to simulate the posterior distribution. However, the optimization process of these methods can be prohibitively slow for practical applications. In order to obtain closed-form solutions, some lower-bound approximations are then introduced into the evidence lower bound (ELBO), following the recently proposed extended variational inference (EVI) framework. The problem in numerical simulation can be overcome. In this paper, we address the Bayesian estimation of the finite Gamma mixture model (GaMM) under the EVI framework in a flexible way. Moreover, the optimal mixture component number can be automatically determined based on the observed data and the over-fitting problem related to the conventional expectation–maximization (EM) is overcome. We demonstrate the excellent performance of the proposed method with synthesized data and real data evaluations. In the real data evaluation, we compare the proposed method on object detection and image categorization tasks with referred methods and find statistically significant improvement on accuracies and runtime.
HyperCon: Image-To-Video Model Transfer for Video-To-Video Translation Tasks

Video-to-video translation is more difficult than image-to-image translation due to the temporal consistency problem that, if unaddressed, leads to distracting flickering effects. Although video models designed from scratch produce temporally consistent results, training them to match the vast visual knowledge captured by image models requires an intractable number of videos. To combine the benefits of image and video models, we propose an image-to-video model transfer method called Hyperconsistency (HyperCon) that transforms any well-trained image model into a temporally consistent video model without fine-tuning. HyperCon works by translating a temporally interpolated video frame-wise and then aggregating over temporally localized windows on the interpolated video. It handles both masked and unmasked inputs, enabling support for even more video-to-video translation tasks than prior image-to-video model transfer techniques. We demonstrate HyperCon on video style transfer and inpainting, where it performs favorably compared to prior state-of-the-art methods without training on a single stylized or incomplete video. Our project website is available at ryanszeto.com/projects/hypercon.
How should theory guide a ‘shift to digital’ in mobile game-based learning?

This paper provides a theory-based critique and response to the Giannakas et al. [Educational Technology Research and Development, 66, 341-384 (2018). 10.1007/s11423-015-9396-3] critical review of mobile game-based learning. Within the framework of shifting-to-digital, the response discusses impact/value of the paper, possible application of the ideas found in the paper, limitations and constraints associated with the paper, and future suggestion for how the ideas in the paper should guide design and research.
SliceNets — A Scalable Approach for Object Detection in 3D CT Scans

One of the most promising approaches for automated detection of guns and other prohibited items in aviation baggage screening is the use of 3D computed tomography (CT) scans. However, automated detection, especially with deep neural networks, faces two key challenges: the high dimensionality of individual 3D scans, and the lack of labelled training data. We address these challenges using a novel image-based detection and segmentation technique that we call the slice-and-fuse framework. Our approach relies on slicing the input 3D volumes, generating 2D predictions on each slice using 2D Convolutional Neural Networks (CNNs), and fusing them to obtain a 3D prediction. We develop two distinct detectors based on this slice-and-fuse strategy: the Retinal-SliceNet that uses a unified, single network with end-to-end training, and the U-SliceNet that uses a two-stage paradigm, first generating proposals using a voxel labeling network and, subsequently, refining the proposals by a 3D classification network. The networks are trained using a data augmentation approach that creates a very large training dataset by inserting weapons into 3D CT scans of threat-free bags. We demonstrate that the two SliceNets outperform state-of-the-art methods on a large-scale 3D baggage CT dataset for baggage classification, 3D object detection, and 3D semantic segmentation.
Sentimental Analysis of “Aarogya Setu”

Identifying the emotional tone behind text or simply opinion mining is in vogue these days and Twitter has been proved as the primary choice when it comes to analyzing the real live data or sentiments of its users regarding anything such as tracking of seismic activities, flu epidemic, automotive industry, academic researches, etc. In today's world, where most of the data is online on social media in an unstructured and scattered form, opinion mining turns out to be a suitable method to understand the public's views and moods. Our paper aims to study the impact of the alleged hacking of the “Aarogya Setu” application (app) on opinions/sentiments of people towards it using sentimental analysis on twitter data. The key finding of our study is the rise in negative emotions against the Aarogya Setu app after its alleged hacking.
Albedo Data Sets for Bifacial PV Systems

For use by the PV and financial communities to better estimate the performance and to reduce the risk of bifacial PV systems, data sets of ground albedo and associated meteorological data were developed by using existing measurement network data and data contributed by the PV industry. The data sets include time-series data as well as summary information of tabular monthly and yearly data and plots of monthly and hourly albedo values. Complete information is presented in a user's guide and data are available for download from NREL's DuraMAT website.
An Adversarial Risk Analysis Framework for Batch Acceptance Problems

We provide an adversarial risk analysis framework for batch acceptance problems in which a decision maker relies exclusively on the size of the batch to accept or reject its admission to a system, albeit being aware of the presence of an opponent. The adversary acts as a data-fiddler attacker perturbing the observations perceived by the decision maker through injecting faulty items and/or modifying the existing items to faulty ones. We develop optimal policies against this combined attack strategy and illustrate the methodology with a review spam example.
Implementation of Human Detection on Raspberry Pi for Smart Surveillance

With the rapidly increasing rate of theft and insecurity, surveillance cameras have become a piece of conventional household equipment for many. Although these cameras just record the footage and make it conceivable to review these recordings after a sense of malicious activity being spotted by somebody. There have been approaches to robotize this procedure by introducing a movement discovery framework and utilizing other IoT based sensors. Such solutions were not reliable as, in adverse conditions, these systems could not adequately detect the human presence and during the mishap the individuals deemed necessary were not alerted. In this paper, we introduce a compact and complete solution to this problem, all packed in single Raspberry Pi by implementing an effective object detection model using Machine Learning and integrating it with advanced Image Enhancement technology to detect only the human activity even under adverse conditions. Also, the entire data that is being collected by this system has been end-to-end encrypted by using Cryptography in order to prevent Man in the middle attacks.
Design of basketball game image acquisition and processing system based on machine vision and image processor

This article proposes the following video global stabilization framework using two top-notch advanced cameras for indoor gaming accessories. From the following predictions, it is doubtful that the system consolidation strategy considers closing the world. The release of this framework can be a useful check for the development of smart players. Implementation has been completed as a product framework that scientists should use in the game as an effective guide. The tough competition of the movement should not be explored; based on non-invasive techniques. In this unique context, scheduling information can be used as a framework for obtaining valuable distinctions between vision, image processing and strategy players. In this task, defining a mandatory combination must proceed and deal with the problem in real B balls. Reduce coordinate re-planning errors when expanding the number of b-ball blocking authorization releases.
Development of a MIDI Synthesizer for Test Signals to a Wireless Acoustic Sensor Network

The following topics are dealt with: commerce; risk management; adsorption; Internet; energy storage; regression analysis; sustainable development; finite element analysis; business data processing; logistics.
Online Handwritten Mongolian Character Recognition using CMA-MOHR and Coordinate Processing

With the objective of improving the performance of online handwritten traditional Mongolian recognition, this paper proposes a segmentation-free, lightweight online handwritten Mongolian character recognition model network structure CMAMOHR and coordinate processing methods applied to online handwritten Mongolian recognition jobs. A new method for resampling coordinate sequences was used in the preprocessing. We also built a large dataset of Mongolian online handwriting. Compared to the baseline model, our model had a 28.94% and 15.65% improvement in whole word recognition topl and toplO, respectively. Our method enables higher recognition rates and faster convergence.