Reviewerly/exHarmony huggingface dataset:
Visually imperceptible adversarial patch attacks

The vulnerability of deep neural networks (DNNs) to adversarial examples has attracted more attention. Many algorithms have been proposed to craft powerful adversarial examples. However, most of these algorithms modified the global or local region of pixels without taking network explanations into account. Hence, the perturbations are redundant, which are easily detected by human eyes. In this paper, we propose a novel method to generate local region perturbations. The main idea is to find a contributing feature region (CFR) of an image by simulating the human attention mechanism and then add perturbations to CFR. Furthermore, a soft mask matrix is designed on the basis of an activation map to finely represent the contributions of each pixel in CFR. With this soft mask, we develop a new loss function with inverse temperature to search for optimal perturbations in CFR. Due to the network explanations, the perturbations added to CFR are more effective than those added to other regions. Extensive experiments conducted on CIFAR-10 and ILSVRC2012 demonstrate the effectiveness of the proposed method, including attack success rate, imperceptibility, and transferability.
Bayesian nonparametric hypothesis testing for longitudinal data analysis

A Bayesian nonparametric procedure for longitudinal data analysis is proposed. The procedure simultaneously tests for the effects in the mean structure preserving the main effects when interactions are present. The method is highly flexible in that it does not assume a particular distribution for the errors and random effects, as is usually done in longitudinal data analysis. The correlation between the repeated measurements is captured via a Markovian time-dependent Dirichlet process mixture. Specifically, when this latter is represented via a species sampling model with stick-breaking weights, the effect of predictors is driven by the underlying atoms, and the time evolution driven by time-dependent weights. The performance of the proposed method is illustrated using both simulated and real data sets.
A novel deep learning approach for sickle cell anemia detection in human RBCs using an improved wrapper-based feature selection technique in microscopic blood smear images

Sickle Cell Anemia (SCA) is a disorder in Red Blood Cells (RBCs) of human blood. Children under five years and pregnant women are mostly affected by SCA. Early diagnosis of this ailment can save lives. In recent years, the computer aided diagnosis of SCA is preferred to resolve this issue. A novel and effective deep learning approach for identification of sickle cell anemia is proposed in this work. Around nine hundred microscopic images of human red blood cells are obtained from the public database 'erythrocytes IDB'. All the images are resized uniformly. About 2048 deep features are extracted from the fully connected layer of pre-trained model InceptionV3. These features are further subjected to classification using optimization-based methods. An improved wrapper-based feature selection technique is implemented using Multi- Objective Binary Grey Wolf Optimization (MO-BGWO) approach with KNN and SVM for classification. The detection of sickle cell is also performed using typical InceptionV3 model by using SoftMax layer. It is observed that the performance of the proposed system seems to be high when compared to the classification using the original InceptionV3 model. The results are validated by various evaluation metrics such as accuracy, precision, sensitivity, specificity and F1-score. The SVM classifier yields high accuracy of about 96%. The optimal subset of deep features along with SVM enhances the system performance in the proposed work. Thus, the proposed approach is appropriate for pathologists to take early clinical decisions on detection of sickle cells.
Метод и результаты выделения линейных форм рельефа по данным анализа ЦМР на примере левобережья бассейна среднего течения реки Аракс

The article describes the method developed by the authors for revealing Earth surface regional linear morphological features (morphographic lineaments) by morphometric analysis of a digital elevation model (DEM). It also presents the results of the method application on the territory of Armenia in the Araks river basin. The method is based on the experience of a large number of studies devoted to this issue and includes the following successively performed procedures: 1) revealing of Earth surface linear features by formal morphometric indices extracted from the DEM, mainly based on the analysis of slope and aspect maps; 2) geomorphological identification of the revealed linear elements or their definition as real linear landforms; 3) structural identification of the revealed linear landforms, i.e. proving their identity with morphostructures according to geological features – their coincidence with the spatial localization of the epicenters of the historical earthquakes, ore deposits, mineral water springs, as well as the consistency of the identified morphostructures with the data of previously published articles and maps.
Classification, Application, Challenge, and Future of Midair Gestures in Augmented Reality

Augmented Reality (AR) technology provides many opportunities to enhance people’s experience in interacting with data. Midair gesture, a natural interaction mode in AR, interacts with virtual elements without any auxiliary devices. It has become a hot topic of interest for researchers with the development of gesture recognition technology. From the perspective of user experience, the types of midair gesture, gesture recognition technology, applications were discussed. The challenges of air gesture interaction from two aspects of user experience and technology were analyzed, and the importance of collaborative gesture and low-cost user experience in the future were emphasized. Finally, the application prospect of air gesture interaction distance education, medical health, industry, office, and so on is discussed.
Electromagnetic Noise as Entropy Source for Cryptographic System

This paper presents a system for gathering electromagnetic noise to extract surrounding entropy at 430 MHz. The uniqueness of electromagnetic noise depends on geographical location, time, used technology, and a plethora of other variables. The proposed system consisting of telescopic whip antenna and software defined radio HackRF One takes advantage of the said fact to extract entropy and transform it into a random number which could be then used in cryptography. Said system is supposed to be low-cost and made from generally available equipment to ensure usability for the general population. Three experiments were devised to transform received noise into random numbers. The first experiment extracts entropy from the time domain by averaging the magnitude of the signal and comparing it to magnitude. The second experiment uses Fast Fourier Transformation to obtain the frequency composition of the received signal and extracts entropy out of magnitude of different frequencies. The last experiment combines the two through logical operation XOR to gain the random number. Obtained random numbers are then tested for randomness by checking entropy, monobit test from NIST standard, and proportion of logic ones and zeroes in the sequence.
A Novel Minimum Spanning Tree Clustering Algorithm Based on Density Core

Clustering analysis is an unsupervised learning method, which has applications across many fields such as pattern recognition, machine learning, information security, and image segmentation. The density-based method, as one of the various clustering algorithms, has achieved good performance. However, it works poor in dealing with multidensity and complex-shaped datasets. Moreover, the result of this method depends heavily on the parameters we input. Thus, we propose a novel clustering algorithm (called the MST-DC) in this paper, which is based on the density core. Firstly, we employ the reverse nearest neighbors to extract core objects. Secondly, we use the minimum spanning tree algorithm to cluster the core objects. Finally, the remaining objects are assigned to the cluster to which their nearest core object belongs. The experimental results on several synthetic and real-world datasets show the superiority of the MST-DC to Kmeans, DBSCAN, DPC, DCore, SNNDPC, and LDP-MST.
Gravity Search Algorithm-Based Path Planning of Single Humanoid Based on the Study of Different Artificial Intelligence Techniques

The paper presents a rigorous study of the different path planning approaches for humanoid navigation in an unknown space. Different reactive techniques along with classical techniques are discussed in the paper for navigation of the humanoid robot in different terrain. The paper deals with a comprehensive discussion of the above methods to identify the best intelligent technique for the navigation-based planning of humanoids. It was found that the convergence of the classical method is dependent upon the navigation environment and the level of selection of parameters. The paper introduces a new controller (based on the gravity search algorithm, GSO) to model humanoid navigation in cluttered terrain. The controller was tested in both simulation and experimental environments. It was found that the designed controller was effective in achieving optimized path navigation and the percentage error was within the acceptable limits.
Design of Snake Robot with 28015 PING Ultrasonic Distance Sensor and Arduino

This paper proposes snake robot, famously referred as SnakeBot, designed using an ultrasonic distance sensor and Arduino. The autonomous SnakeBot is highly fitted for confined surroundings as they can enter and travel through these places with a higher degree of freedom because of their small cross section and highly redundant kinematics. The first focus of SnakeBot is to copy the movement of the similar named reptile-the serpent. The discussed design of SnakeBot consists of a head-followed seven segments which together comprises the body of the robot. The 28015 PING RevC ultrasonic sensor is attached to a central unit consisting of Arduino, in the head segment which controls the SnakeBot. The sensor then determines whether or not there is a barrier in front of the robot and, if so, measures its distance. Analyzing this distance with the directions already coded in the central unit, the snake robot decides the direction to continue the movement. This design can prove beneficial in many real-life problems and can be further improvised by coupling with other technologies.
A Hybrid Algorithm Based Static Obstacle Avoidance for a Wheeled Base

Most of the path planning algorithms used for avoiding obstacles are prone to higher computational complexity. Hybrid algorithms employed for the static obstacle collision avoidance are proved to be efficient as compared to conventional algorithms. In this paper, a hybrid algorithm that consumes less computational time during static obstacle avoidance is introduced. A hybrid algorithm combining Lazy Probabilistic Road Map (PRM) algorithm as well as Life Long Planning (LPA) star algorithm is employed for generating an optimized path from start node to end node. Lazy PRM technique reduces the computational time for generating nodes and edges connecting the generated nodes. LPA star algorithm determines the optimized path connecting the local edges formed using Lazy PRM technique. A non-holonomic mobile platform designed for carrying upper body humanoid robot is used in this paper for the experimental evaluation of the proposed hybrid algorithm.
Mobile Robot Path Planning Using Neuro-Sugeno-Fuzzy Gravitational Technique in a Cluttered Environment

Paper shows the contribution of the neuro-Sugeno-fuzzy gravitational technique towards path navigation. Fire bird-V robot is used in the above approach. The work is done in both simulation and experimental environment using simulation tools. The results of the simulation and experiments are compared and expressed in tabular form. The percentage deviation in the result was within the acceptable limits. The reason for the deviation was various external reasons, and slippage at the contact between the wheel and the floor. The proposed controller was found to be effective in preventing collision with different obstacles at different locations. Arena selected was a static environment.
A Sustainable Approach Toward Tangible Interactive Setup for Improving the Learning Experience of Primary School’s Children in Rural India

This paper presents a work in progress being done to overcome educational challenges faced by Rural India and utilizes the Stanford design thinking practices for the development of a solution that focuses on the sustainable approach for an interactive and fun learning medium. A field survey was conducted using stratified random sampling covering N = 11 schools on the outskirts and in remote areas of Dehradun, Uttarakhand. Data was collected in the form of observations and open-ended interviews with N = 8 school teachers and N = 2 NGO’s to better understand the teaching process and classroom dynamics. Contextual inquiries were carried out among a total of N = 30 students considered as slow learners among a wider population of students from these schools. Insights reveal that children are inherently curious and imaginative, but due to the lack of infrastructure and financial aids such as innovative and interactive learning mediums to every school, it is the biggest challenge for them. So, to identify this problem into which equity in education should be provided to everyone, this paper discusses the approach of sustainability in rural primary education, to make it more feasible and equitable for everyone.
Can AI predict epithelial lesion categories via automated analysis of cervical biopsies: The TissueNet challenge?

The French Society of Pathology (SFP) organized its first data challenge in 2020 with the help of the Health Data Hub (HDH). The organization of this event first consisted of recruiting nearly 5000 cervical biopsy slides obtained from 20 pathology centers. After ensuring that patients did not refuse to include their slides in the project, the slides were anonymized, digitized, and annotated by expert pathologists, and finally uploaded to a data challenge platform for competitors from around the world. Competing teams had to develop algorithms that could distinguish 4 diagnostic classes in cervical epithelial lesions. Among the many submissions from competitors, the best algorithms achieved an overall score close to 95%. The final part of the competition lasted only 6 weeks, and the goal of SFP and HDH is now to allow for the collection to be published in open access for the scientific community. In this report, we have performed a "post-competition analysis" of the results. We first described the algorithmic pipelines of 3 top competitors. We then analyzed several difficult cases that even the top competitors could not predict correctly. A medical committee of several expert pathologists looked for possible explanations for these erroneous results by reviewing the images, and we present their findings here targeted for a large audience of pathologists and data scientists in the field of digital pathology.
Construction of Neural Conditioning Vocal Training System based on Neural Computing and Applications (NCAA) Model

Construction of neural conditioning vocal training system based on neural computing and applications (NCAA) model is studied in the paper. For the processing technology of digital video special effects, in addition to adding video transition effect and video effect, an equal attention should also be given to the technical editing of video, find the best editing point according to the needs of the plot and the rhythm point of music, and add special effects such as the frame freeze frame, grid, corner positioning and transformation. In the neural network structure constructed by gene circuit, a complex neuromorphic computing process is realized through gene expression and regulation. With these integration, the NCAA model is implemented. With the in-depth study of memristor, many device characteristics such as the resistance value and switching voltage of memristor are found to have great variability, and then the proposed model is applied into the neural conditioning vocal training system. Through the implementation of the system, the performance is tested.
Learner Performance-Based Behavior Optimization Algorithm: A Functional Case Study

A novel algorithm called learner performance-based behavior algorithm (LPB) was proposed for single and multi-objective by Chnoor M. Rahman and Tarik A. Rashid in 2021. LPB proved its ability to deal with complex optimization problems compared to the dragonfly algorithm (DA), genetic algorithm (GA), and particle swarm optimization (PSO). This paper presents and explains the implementation of the LPB algorithm, and it applies it as a model in a case study to maximize a fitness function. As a result, the LPB algorithm is successfully improved the initial population and achieved the optimal solution.
Solar Car Parking for Maximum Power Utilization at the United Arab Emirates University: Case Study

The demand for electricity and power from fossil fuels has been increasing in recent decades. The Greenhouse gases generated by such processes significantly impact the atmosphere. In addition, the cost of electricity production from these sources is very high; therefore, it is not affordable in the long run. Such a situation can be solved by utilizing renewable energy sources. This article presents a case study that is implementing a solar parking lot to generate energy by using existing car parking roofs at United Arab Emirates University. The generated power shall be fed to a grid. We have used PVSyst software to simulate various factors, including the shadow effect, and select proper solar parking that guarantees maximum solar power generation. Two cases with the fixed and the tracked PV panels were analyzed, considering power generation and the corresponding economic evaluation. The results suggested that the tracking PV panel produced more power and had less payback time energy than the fixed one.
The Case for Stringent Regulations of Stablecoins

Decisions on how to regulate stablecoins, and other parts of the crypto industry, should be based on what we know about crypto’s past and present and discount starry-eyed forecasts of its future. The past demonstrates public blockchain cryptocurrencies are highly volatile; main blockchains have no mechanisms for ensuring stability; and that after 13 years there are unfulfilled promises and no widespread use of cryptocurrencies for productive purposes. The present shows that speculation is the main use case for currencies with the leading exchanges investing in feeding hype with celebrity-studded ads among other things. Crypto exchanges and other participants with a stake in the continued trading of currencies are now selling the “vision thing”: a vague and distant future of a decentralized internet and financial system. Now we also have hard evidence that lax regulations of stablecoins, combined with the inherent volatility of the native cryptocurrencies have resulted in the classic systemic financial risks from runs and contagion. This article advocates for stringent regulation of stablecoins: fully backed with cash and short-term instruments, with a trustee, and in a regulated bank. It also argues for regulators going further to ensure stablecoins are not used to support unsafe applications.
effect of work environment on the job performance among nurses at Makkah City, Saudi Arabia

Background: Nurses represent the most significant proportion of health care professionals. Nurses work in different healthcare organisations and play a vital role in improving healthcare quality. The healthy nursing practice environments are significant to recruiting and retaining nursing professionals in in Saudi Arabia, because the country facing a critical nursing shortage. Nurses are generally eager to have a healthy work environment that provides them with a sense of worth, and a favourable work environment means better hospital performance. In this regard, it is essential to study the factors influencing nurses’ job performance. Aim: To determine the effect of the work environment on the job performance of nurses working in selected hospitals in Makkah, Saudi Arabia. Design: This study employed a quantitative, descriptive, cross-sectional research design to assess the effect of the work environment on nurses’ job performance. Sample: The convenience sampling method was chosen to collect and analyse the data from professional nurses who have worked for at least six months in the selected hospitals, from King Faisal Hospital and King Abdulaziz Hospital in the Makkah region of Saudi Arabia.
Factors Influencing Information Security Standards and Policies Compliance by Nigeria Bank Employees

It is common knowledge that cyber-attacks, theft, and internet fraud are widespread in the banking industry. This is no different in the Nigerian banking sector, where a lack of employee compliance with standards and policies exacerbates the situation. This paper reports on the effect of motivating factors on Nigerian bank employees&#x0027; intention to comply with Information Security Standards and Policies (ISSsPs). The study uses multiple regression analysis of data obtained from 370 employees of selected banks in Nigeria. The findings indicate that behavioural factors such as normative beliefs and awareness of information security threats significantly influence employees&#x0027; intention to comply with ISSsPs. In contrast, the severity of penalty and certainty of detection did not influence employees&#x0027; intention to comply.
Application and Research of Text Analysis in Public Digital Model of Energy Industry

A new text analysis method is proposed and an analysis model based on the power energy industry is designed, and the relevant models are verified by experiments. The method proposed in this paper can extract key influencing factors from the relevant information of the power and energy industry, and can become an important tool in the energy public digital model, which has practical significance.
Analysis of slope and azimuth angle of solar panel with different solar trackers

The solar panels are operated without tracking system or with tracking system for producing electrical power. The solar trackers are used in solar panels to extract the sunlight in an effective manner. The solar panels operated to satisfy the consumption of load without polluting the environment. The variation of azimuth angle and solar panel slope in solar panel are analyzed based on average solar output power. The hourly variation of azimuth angle and solar panel slope in solar panel operated vertical axis and horizontal axis tracking system over an entire year are analyzed. Similarly, in two axis tracking system, the hourly variation of azimuth angle and solar panel slope over a year is analyzed. The analysis of different parameters in tracking system are conducted using HOMER software. The different sensitive cases like solar reflectance and solar panel slope which has an impact on solar power production and solar capacity also analyzed.