Reviewerly/exHarmony huggingface dataset:
A Prototype of Multi Functional Rescue Robot Using Wireless Communication

A rescuer robot is a robot that is basically designed to assist rescue efforts by searching, removing rubble, mapping, and by providing medical treatment or evacuating casualties. This paper presents a prototype of multi-functional robot and the purpose of it is to go to dangerous areas like collapsed places, areas attacked by terrorists to collect various data & sent it via wireless communication system for the next step. The main phenomenon of this robot is to help the rescuer team by providing various information which is very difficult & risk to collect via a human. Such as, if there is any gas leakage issues or ammunition or any human is alive inside the rubble etc. It is very risky for any man to go inside the rubble that's why this rescue operation system is divided into three major parts. They are 1. Quadcopter 2. Rover 3. Nanobot. Each of these parts has a live-action go pro camera whose output can easily available in the monitoring room. The quadcopter provides the mapping from above, Rover is going to start collect data and also remove small rubble at a time as it is containing a mechanical claw and nanobot at a time find a hole inside rubble and start searching, this nanobot also contains a thermal camera to see if there are any alive people stuck inside the rubble. The whole system operates wirelessly and sends the data of each second to the rescue team through wireless communication.
Adaptive variational preparation of the Fermi-Hubbard eigenstates

Approximating the ground states of strongly interacting electron systems in quantum chemistry and condensed matter physics is expected to be one of the earliest applications of quantum computers. In this paper, we prepare highly accurate ground states of the Fermi-Hubbard model for small grids of up to six sites (12 qubits) by using an interpretable, adaptive variational quantum eigensolver (VQE) called ADAPT-VQE [H. R. Grimsley et al., Nat. Commun. 10, 3007 (2019)]. In contrast with nonadaptive VQE, this algorithm builds a system-specific ansatz by adding an optimal gate built from one-body or two-body fermionic operators at each step. We show this adaptive method outperforms the nonadaptive counterpart in terms of fewer variational parameters, short gate depth, and scaling with the system size. The fidelity and energy of the prepared state appear to improve asymptotically with ansatz depth. We also demonstrate the application of adaptive variational methods by preparing excited states and Green's functions using a proposed ADAPT-SSVQE algorithm. Lower depth, asymptotic convergence, noise tolerance of a variational approach [A. Peruzzo et al., Nat. Commun. 5, 4213 (2014); J. R. McLean et al., New J. Phys. 18, 023023 (2016); K. Sharma et al., New J. Phys. 22, 043006 (2020)], and a highly controllable, system-specific ansatz make the adaptive variational methods particularly well suited for noisy intermediate-scale quantum devices.
Visual Saliency via Multiscale Analysis in Frequency Domain and Its Applications to Ship Detection in Optical Satellite Images

This article proposes a bottom-up visual saliency model that uses the wavelet transform to conduct multiscale analysis and computation in the frequency domain. First, we compute the multiscale magnitude spectra by performing a wavelet transform to decompose the magnitude spectrum of the discrete cosine coefficients of an input image. Next, we obtain multiple saliency maps of different spatial scales through an inverse transformation from the frequency domain to the spatial domain, which utilizes the discrete cosine magnitude spectra after multiscale wavelet decomposition. Then, we employ an evaluation function to automatically select the two best multiscale saliency maps. A final saliency map is generated via an adaptive integration of the two selected multiscale saliency maps. The proposed model is fast, efficient, and can simultaneously detect salient regions or objects of different sizes. It outperforms state-of-the-art bottom-up saliency approaches in the experiments of psychophysical consistency, eye fixation prediction, and saliency detection for natural images. In addition, the proposed model is applied to automatic ship detection in optical satellite images. Ship detection tests on satellite data of visual optical spectrum not only demonstrate our saliency model's effectiveness in detecting small and large salient targets but also verify its robustness against various sea background disturbances.
Universality for Langevin-like spin glass dynamics

We study dynamics for asymmetric spin glass models, proposed by Hertz et al. and Sompolinsky et al. in the 1980’s in the context of neural networks: particles evolve via a modified Langevin dynamics for the Sherrington–Kirkpatrick model with soft spins, whereby the disorder is i.i.d. standard Gaussian rather than symmetric. Ben Arous and Guionnet (Probab. Theory Related Fields 102 (1995) 455–509), followed by Guionnet (Probab. Theory Related Fields 109 (1997) 183–215), proved for Gaussian interactions that as the number of particles grows, the short-term empirical law of this dynamics converges a.s. to a nonrandom law μ⋆ of a “self-consistent single spin dynamics,” as predicted by physicists. Here we obtain universality of this fact: For asymmetric disorder given by i.i.d. variables of zero mean, unit variance and exponential or better tail decay, at every temperature, the empirical law of sample paths of the Langevin-like dynamics in a fixed time interval has the same a.s. limit μ⋆.
Cyber Security and Social Engineering

Nowadays, it is quite common to use modern information technology at work, at school and in private life. Shared information can be used not only for advertising and statistics purposes. Low levels of awareness, inattention and trust can lead to the misuse of this information, using computers and smart mobile devices using the Internet. This article aims to summarize in the introduction the most common methods used to misuse information and what user behavior leads to this. Furthermore, the article then describes the purpose for which this information is misused. The conclusion then describes the methods of protection. It is necessary to constantly draw attention to this issue so that users themselves focus on information security in the environment of information and communication technologies.
Image Processing for the Rehabilitation Assessment of Locomotion Injuries and Post Stroke Disabilities

In this paper an evaluation method for neuromotor rehabilitation level is proposed. The method is completely non-invasive, based on the analysis of video sequences in which the patient performs certain relevant activities. In the first step, the position of the patient who is walking in the frontal plane is estimated in all frames of the video sequence using a pre-trained VGGNet convolutional neural network. Then, the joint angles between the body segments are computed. In this approach, the patient&#x0027;s condition is assessed by studying the variation of angles between the leg segments during movement. A statistic was made for these angles and the appearance of abnormal values by their number and amplitude indicates either the level of recovery or the presence of locomotion injuries. The method was applied for a reduced number of cases, but the work is still in progress and many improvements can be made.
Design and implementation of a new fractional-order Hopfield neural network system

Abstract In this work, a novel chaotic system of fractional-order based on the model of Hopfield Neural Network (HNN) is proposed. The numerical solutions of the 4-neurons-based HNN fractional-order chaotic system are obtained by using the Adomain decomposition method. The dynamical performances of the 4-neurons-based HNN fractional-order chaotic system are explored through attractor trajectories, bifurcation diagrams, Lyapunov exponents, SE complexity and chaotic diagram based on SE complexity. In addition, the 4-neurons-based HNN fractional-order chaotic system is implemented based on the Multisim platform. The experimental results indicate that the 4-neurons-based HNN fractional-order chaotic system has rich dynamic behavior, and the influence of different order on the dynamical properties of the system are particularly great. This research will provide theoretical foundation and experimental basis for the hardware implementation and application of the 4-neurons-based HNN fractional-order chaotic system.
The Impact of the Fourth Industrial Revolution on Ethical Leadership

Objectives: ethical leadership is a widely discussed topic, but so far there are still debates around this topic, even arguing that ethical leadership is just statements of right and wrong or that it is a style “leadership style”. Now, the impact of the fourth industrial revolution will make ethical leadership even more essential to improving leadership performance. Therefore, the aim of the study is to show the advantages and challenges of the fourth industrial revolution, these impacts on ethical leadership, so that the effects are useful to leadership performance. Focus on developing ethical leadership. Methods / Analysis: this study conducts analysis and evaluation from data sources on Science Direct, Web of Science, and Scopus, thereby showing the basic characteristics of leadership, ethics, ethical leadership, fourth industrial revolution, advantages and challenges, discussions on the impact of the fourth industrial revolution on ethical leadership. Findings: The findings of the study indicate that the ethical leadership qualities are quite stable, such as perseverance, service spirit, tolerance, power sharing, fairness, etc. The impact of the fourth technological revolution will make ethical leaders more effective in their roles, making it easier for leaders to overcome challenges, contributing to improving leadership performance. Novelty / Improvement: research shows that ethical leaders will be dedicated leaders, service-minded leaders, impacted by the fourth technological revolution, making leadership even more powerful. Virtue becomes more and more important. Therefore, in order to successfully implement the fourth industrial revolution, it is necessary to pay attention to developing ethical leadership. Doi: 10.28991/HEF-2021-02-03-05 Full Text: PDF
Assessment of Heavy Metal Pollution Using the Geo-accumulation Index (I-Geo), Pollution Load Index (PLI) and Potential Ecological Risk Index (RI) in Paddy Field Soils Adjacent to Ultrabasic Soils

The content of heavy metals was appraised at paddy fields, from a total of 54 soil samples gathered from two locations in Ranau, Sabah and one from the UKM experimental paddy plot in Peninsular Malaysia, during2014 and 2015.Through an appraisal evaluate of contamination using the Index of Geo-accumulation (I geo) it was noted that Chromium and Nickel levels in Ranau field 1 came under grade 6 (I-geo > 5 is considered to be very contaminated), showing that the soil was heavily tainted with Cr and Ni, whilst Cobalt was discovered to be at the grade 3 level (medium to strong contamination).On the other hand, Ranau field 2's I-geo for other metals came under grade 1(which means medium to no contamination), highlighting the fact that the soils in these areas were tainted with the examined metals, while the control field was found to be grade 0 (no contamination).Ranau field 1 showed strong levels of the contamination factor (CF) for Co, Cr, and Ni, and its Pollution Load Index was(PLI) was> 1, denoting a steady degradation in the area, with high levels of pollution compared to those in the other tested areas.The average monomial ecological risk for Ni was found to be between (40 -80), highlighting that Ni contamination was considered to be of medium danger to the surrounding environment.Through the Potential Ecological Risk Index, it was seen that Ni was a likely hazard and presented a substantial danger.The RI values for the test sites were as follows: season 2015 (2.89) < control for season 2014 (3.18).
Machine Learning

In addition to possible application in player and object tracking in digital image sequences, machine learning methods are used to extract information from a wide range of data that is collected before, during and after team games. The chapter summarizes basics and principles of machine learning and discusses approaches for analyzing different data categories. Video data form the basis for the automatic classification of game scenes, while position data is used to identify types and intensities of movement and to identify events and tactical behavior. Additional or different data categories (physiological data, fitness data, scouting data, coaching data, crowd data, etc.) are evaluated if questions about the player rating and ranking, team selection and formation, player recruitment, talent identification or injury risk assessment are to be answered. Finally, issues of prediction of the game result may be assessed.
An Efficient Authenticated Key Agreement Protocol for D2D Communication

In view of the security and efficiency of key agreement in D2D communication, we propose an authenticated key agreement protocol without bilinear pairing. The proposed protocol, which adopts the certificateless cryptosystem, realizes the authentication and key agreement based on the ECC discrete logarithm problem. Theoretical analysis shows that the protocol is secure and reliable under the general certificateless model, which is able to resist replay attack, man-in-middle attack, key leakage attack, with the forward and backward secrecy. Simulation results show that the protocol has advantages in negotiation time and computation cost compared with similar protocols.
Development of Deep Learning Models for Predicting In-Hospital Mortality Using an Administrative Claims Database: Retrospective Cohort Study

Administrative claims databases have been used widely in studies because they have large sample sizes and are easily available. However, studies using administrative databases lack information on disease severity, so a risk adjustment method needs to be developed.We aimed to develop and validate deep learning-based prediction models for in-hospital mortality of acute care patients.The main model was developed using only administrative claims data (age, sex, diagnoses, and procedures on the day of admission). We also constructed disease-specific models for acute myocardial infarction, heart failure, stroke, and pneumonia using common severity indices for these diseases. Using the Japanese Diagnosis Procedure Combination data from July 2010 to March 2017, we identified 46,665,933 inpatients and divided them into derivation and validation cohorts in a ratio of 95:5. The main model was developed using a 9-layer deep neural network with 4 hidden dense layers that had 1000 nodes and were fully connected to adjacent layers. We evaluated model discrimination ability by an area under the receiver operating characteristic curve (AUC) and calibration ability by calibration plot.Among the eligible patients, 2,005,035 (4.3%) died. Discrimination and calibration of the models were satisfactory. The AUC of the main model in the validation cohort was 0.954 (95% CI 0.954-0.955). The main model had higher discrimination ability than the disease-specific models.Our deep learning-based model using diagnoses and procedures produced valid predictions of in-hospital mortality.
Analyzing the impact of the MPI allreduce in distributed training of convolutional neural networks

For many distributed applications, data communication poses an important bottleneck from the points of view of performance and energy consumption. As more cores are integrated per node, in general the global performance of the system increases yet eventually becomes limited by the interconnection network. This is the case for distributed data-parallel training of convolutional neural networks (CNNs), which usually proceeds on a cluster with a small to moderate number of nodes. In this paper, we analyze the performance of the Allreduce collective communication primitive, a key to the efficient data-parallel distributed training of CNNs. Our study targets the distinct realizations of this primitive in three high performance instances of Message Passing Interface (MPI), namely MPICH, OpenMPI, and IntelMPI, and employs a cluster equipped with state-of-the-art processor and network technologies. In addition, we apply the insights gained from the experimental analysis to the optimization of the TensorFlow framework when running on top of Horovod. Our study reveals that a careful selection of the most convenient MPI library and Allreduce (ARD) realization accelerates the training throughput by a factor of $$1.2\times $$ compared with the default algorithm in the same MPI library, and up to $$2.8\times $$ when comparing distinct MPI libraries in a number of relevant combinations of CNN model+dataset.
A Study of Automatic Modulation Classification for Rayleigh Fading Channel

The automatic modulation recognition is an important research problem with potential applications in the defense and aerospace applications. In this paper, we present a study of automatic modulation classification approach. The scheme presented is simple to implement with low complexity. It is based on fourth-order cumulants. We aim to identify four types of digital modulation techniques. These include BPSK, PAM-4, 8-PSK, QAM-4. A data set of Rayleigh fading channel along with the confusion matrix is generated of size 4x4 and the overall accuracy of the identification problem is computed. The accuracy is measured for varied settings of SNR ranging from -10dB to 20dB. The data on the signal and noise is also generated characterizing the real part and imaginary part for the Rayleigh channel modeling. The generated dataset may be used for training of deep learning model for automatic modulation detection.
Mechanical failure of the Aircraft Landing Gear using Fuzzy Cognitive Mapping.

One of the most Important Components in the Entire aircraft is the Landing Gear. In the recent years there has been many incidents were the landing gear has failed both mechanically and also due to pilot's intervention. Taking only the mechanical cau
Implicit and Explicit Attention for Zero-Shot Learning

Most of the existing Zero-Shot Learning (ZSL) methods focus on learning a compatibility function between the image representation and class attributes. Few others concentrate on learning image representation combining local and global features. However, the existing approaches still fail to address the bias issue towards the seen classes. In this paper, we propose implicit and explicit attention mechanisms to address the existing bias problem in ZSL models. We formulate the implicit attention mechanism with a self-supervised image angle rotation task, which focuses on specific image features aiding to solve the task. The explicit attention mechanism is composed with the consideration of a multi-headed self-attention mechanism via Vision Transformer model, which learns to map image features to semantic space during the training stage. We conduct comprehensive experiments on three popular benchmarks: AWA2, CUB and SUN. The performance of our proposed attention mechanisms has proved its effectiveness, and has achieved the state-of-the-art harmonic mean on all the three datasets.
Deep News Recommendation with Contextual User Profiling and Multifaceted Article Representation

News recommendation is a new challenge in the current age of information overload. Making personalized recommendations from the sources of condense textual information is not trivial. It requires the understanding of both the news article’s semantic meaning, and the user preferences via the user’s history records. However, many existing methods are not capable to address the requirement. In this paper, we propose our novel news recommendation model called CUPMAR, that not only is able to learn the user-profile’s preferences representation in multiple contexts, but also makes use of the multifaceted properties of news articles to provide personalized news recommendations. The main components of the CUPMAR model are the News Encoder (NE) and User-Profile Encoder (UE). The NE uses multiple properties of a news article with advanced neural network layers to derive news representation. The UE infers a user’s long-term and recent preference contexts via her reading history to derive a user representation, and finds the most relevant candidate news for her. We evaluate our CUPMAR model with extensive experiments on the popular MIND dataset and demonstrate the strong performance of our approach. Our source code is also available online for the reproducibility purpose.
Person Gender Classification on RGB-D Data With Self-Joint Attention

Automatic gender classification has many potential applications including automatic annotation of images, video surveillance, security, and human-computer interaction. In the last decades, many research works focused on classifying gender using the cues from 2D images of the person’s frontal view. This limits their application in the real world. Also, classifying a person’s gender from a different view, pose and scale are still challenging problems. RGB-D images, containing color and depth images, have more immunity to this problem than 2D images. Recent approaches using RGB-D images explored different combinations of features descriptors from depth and color images to classify the gender. These methods used low-level features extracted from the depth and color separately, ignoring the inter-dependency between the two modalities. In this article, we proposed a deep learning-based approach using a self-joint attention mechanism for human gender classification on RGB-D images. The proposed attention mechanism is designed to encode the inter-dependent information between the depth and color images to enhance the feature discrimination power. We benchmarked our method on a challenging gender dataset that consists of different views, poses, and scales. The presented method outperforms the state-of-art methods with an improvement in the accuracy of 5.2%, 7.5%, and 8.7% on three different test data.
Blockchain Technology

This chapter offers exploratory remarks with reference to healthcare system by discussing whether blockchain can help organizations attain sustained competitive advantage in view of its increased applications and untapped potential, given that organizations are yet to test its utilization as an intangible strategic resource. Design of the chapter constitutes qualitative method as a pragmatic approach to generate evidence and offer exploratory remarks for this intangible resource gaining increasing importance in today’s era of knowledge-based and technology-driven modern economy. This chapter concludes that blockchain with huge popularity as a technological innovation has immense potentialities. It presents itself to be considered as a strategic intangible resource for organizations in healthcare ecosystem for attaining sustained advantage with several cautionary measures. Nonetheless, the chapter limits itself throwing light on the strategic aspects only attempting to touch upon those aspects of blockchain, which are perceived to be helpful in understanding it as a strategic resource to attain sustained competitive advantage.