Reviewerly/exHarmony huggingface dataset:
Pruning for Compression of Visual Pattern Recognition Networks: A Survey from Deep Neural Networks Perspective

Visual Pattern Recognition Networks (VPRN) delivers high performance using deep neural networks. With the advancements in deep neural networks VPR network has gained wide popularity. Continuous advancements will be nurtured with the availability of big data and enormous computing powers. However, such DNN based VPRN models are plunged with computational complexities, intense memory requirements, huge energy expenses which impedes its deployment in resource constrained, strict latency required environments such as edgeAI. For instance, the VGG-16 model needs 500 MB of storage space, has 138 million parameters and involves 15.5 billion Floating Point Operations (FLOPs) to classify a single image with a 32 bit floating point addition that consumes 0.9 pJ. Such overheads demand for compression of VPRN models without impairing its performance. Various compression methods are reported in literature. This survey paper presents a survey on pruning, a popular compression technique for DNN as applied for VPRN. This paper presents comprehensive survey, comparison and points further research direction.
Small Target Recognition and Tracking Based on UAV Platform

Target recognition and tracking based on multi-rotor UAVs have the advantages of low cost and high flexibility. It can monitor low-altitude targets with high intensity. It has great application prospects in national defense, military, and civil fields. The existing algorithms for aerial small target recognition and tracking have the disadvantages of slow speed, low accuracy, poor robustness, and insufficient intelligence. Aiming at the problems of existing algorithms, this paper first makes a lightweight improvement for the YOLOv4 network recognition algorithm suitable for small target recognition and tests it on the VisDrone dataset. The accuracy of the improved algorithm is increased by 1.5% and the speed is increased by 3.3 times. Then, by analyzing the response value, the KCF tracking situation is judged, and the template update of the adaptive learning rate is realized. When the tracking fails, the target is re-searched and tracked based on the recognition results and the similarity judgment. Finally, experiments are carried out on the multi-rotor UAV, and the adaptive zoom tracking strategy is designed to track pedestrians, cars, and UAVs. The results show that the proposed algorithm can achieve stable tracking of long-distance small targets.
One-way Einstein-Podolsky-Rosen steering beyond qubits

Quantum steering has been exploited as an important resource in modern quantum information processing. Owing to its directional nature, some quantum states that are asymmetric under the exchange of parties have been found to manifest steering only in specific direction, thus called one-way steering. Existing works focused on one-way steering in systems of qubits. Here we propose a family of two-party states that are one-way steerable in systems of $d$-dimension. In particular, we validate the one-way steerability of the states for $d=3$, and demonstrate how one-way steering parameter space manifests in two-qutrit system. A general numerical approach for characterizing higher-dimensional one-way steering is provided. Moreover, we develop a method to characterize one-way steering with the experimental loss taken into account, with which the tradeoff relation between losses and measurement settings in steering test in higher-dimensional system is investigated. Our loss-counted model works for finite-dimensional system with finite measurement settings.
Prediction of blood screening parameters for preliminary analysis using neural networks

Over time, the latest technologies and advancements in prediction algorithms are unmasking the innovational ways of diagnosing, predicting, and cure of diseases related to the blood. For this, artificial intelligence and neural networks have played a significant role. Monitoring blood parameters plays a vital role in detecting dormant diseases or any kind of complications. This chapter deals with the various proposed techniques used in the prediction of blood parameters. This chapter mainly presents the forecast of blood screening test features using the backpropagation neural network. The elements used in this paper are fibrinogen and globulin. The normal ranges of fibrinogen and globulin are 2–4 g/L and 20–35 g/L. The glycoprotein which circulates in the vertebrates of the human body is known as fibrinogen. Different congenital and disorders related to the acquired fibrinogen in humans can lead to dysfunctional or reduced fibrinogens. In the human body, the proteins in the blood except albumin are collectively known as globulin. Sixty percent of the proteins in the human body comprises of albumin. Various antibodies, enzymes, and all other categories of proteins fall under the globulins.
Coupling AR with Object Detection Neural Networks for End-User Engagement

The mobile Augmented Reality (AR) technology offers an accessible, inexpensive, and rich user experience that has the potential to engage end-users to an immersive environment. Machine learning (ML) algorithms can be tailored and tuned to carry out a large variety of tasks pertaining to data coming from highly specific environments, thereby improving the decision-making process, and uncovering gaps and opportunities in a wide range of applied fields. High data availability and modern algorithms contribute to the changing way end-users interact with their environment to obtain information, train, and socialize. In this study, we leverage the widespread adoption of mobile phones in daily lives and their advanced features (such as connectivity and location-awareness along with powerful optical and image sensors) to design, develop and test, an AR application that offers a unique user-environment interaction through object detection functionalities. The intended use of the mobile AR application is to foster end-user engagement, enrich user experiences, improve self-efficacy and motivation, and contribute to dissemination and communication activities. Due to its technical and gamification features, and the ability to customize its content through a web-based Content Management Service, it has great potential to be exploited in a variety of contexts including education and training, touristic and cultural appreciation, art and heritage promotion, among others. This paper presents the development and testing of the mobile application and details its architecture and the pertaining implementation challenges. The design features and implementation details of our approach result in a 90% detection accuracy for common objects. Such a performance contributes to the discourse on the use of mobile AR coupled with ML functionalities as a tool stimulating end-user engagement and hands-on learning by facilitating environment exploration, and helping end-users to move from passive observation to creative interaction.
A Hybrid of Artificial Electric Field Algorithm and Differential Evolution for Continuous Optimization Problems

The artificial electric field algorithm (AEFA) is a minimum and maximum value charged-based function optimization scheme that was created for continuous optimization problems in the first place. The deep-rooted AEFA is always one of the best-designed techniques for discovering optimal solutions to real-world optimization problems. Differential evolution is one of the best established optimization algorithm in the literature. This article aims to utilize the potential of AEFA and DE together to produce an efficient hybrid. Both AEFA and DE are synchronized to sum their strengths. The performance of the proposed hybrid algorithm is validated on seventeen benchmark problems including IEEE-CEC 2019 single objective unconstrained optimization problems and obtained experimental results are compared with several optimization algorithms. We used a statistical test, the Wilcoxon signed-rank test, to assess the randomness of the results produced by the suggested AEFA-DE. The experimental results suggest that the AEFA-DE has superior performance than others.
Securing information using a proposed reliable chaos-based stream cipher: with real-time FPGA-based wireless connection implementation

In this paper, a robust chaos-based stream cipher (CBSC) is proposed. The novelty of this work is that it addresses all challenges confronting chaos-based cryptography. The PCBSC (proposed CBSC) has a robust synchronization circuit that mitigates the effect of channel noise, a perturbation block that overcomes the dynamical degradation, a robust encryption scheme, and an efficient control parameters' generator that generates strong keys. According to the complexity evaluation, the improved chaotic map provides good statistical properties. This can be confirmed by the obtained high values of the statistical metrics (largest Lyapunov exponent, approximate entropy, permutation entropy, and sample entropy) used for the evaluation. According to the security analysis, the PCBSC has good security features and provides strong keys that ensure confusion property, as well as enough space to withstand brute-force attacks. On the other hand, the proposed encryption scheme proves its efficiency; the result of the differential attack clearly shows that the diffusion property is guaranteed. Additionally, the original images' statistical properties are completely dispersed on the encrypted images. The obtained performance over noisy channels proves the synchronization circuit's efficiency. When compared to other proposals, the PCBSC provides the best results. In addition, the PCBSC is implemented on an FPGA and evaluated in real-time over a wireless link.
Grading Nursing Care Study in Integrated Medical and Nursing Care Institution Based on Two-Stage Gray Synthetic Clustering Model under Social Network Context

Establishing a scientific and sustainable grading nursing care evaluation system is the key to realizing the rational distribution of medical and nursing resources in the combined medical and nursing care services. This study establishes a grading nursing care index system for medical and nursing institutions from both medical and nursing aspects, and proposes a grading nursing care evaluation model based on a combination of interval-valued intuitionistic fuzzy entropy and a two- stage gray synthetic clustering model for interval gray number under a social network context. Through case analysis, the proposed method can directly classify the elderly into corresponding grading nursing care grades and realize the precise allocation of medical and nursing resources, which proves the feasibility of the method.
Quantum estimation of Kerr nonlinearity in driven-dissipative systems

We mainly investigate the quantum measurement of Kerr nonlinearity in the driven-dissipative system. Without the dissipation, the measurement precision of the nonlinearity parameter $\chi$ scales as "super-Heisenberg scaling" $1/N^2$ with $N$ being the total average number of particles (photons) due to the nonlinear generator. Here, we find that "super-Heisenberg scaling" $1/N^{3/2}$ can also be obtained by choosing a proper interrogation time. In the steady state, the "super-Heisenberg scaling" $1/N^{3/2}$ can only be achieved when the nonlinearity parameter is close to 0 in the case of the single-photon loss and the one-photon driving or the two-photon driving. The "super-Heisenberg scaling" disappears with the increase of the strength of the nonlinearity. When the system suffers from the two-photon loss in addition to the single-photon loss, the optimal measurement precision will not appear at the nonlinearity $\chi=0$ in the case of the one-photon driving. Counterintuitively, in the case of the two-photon driving we find that it is not the case that the higher the two-photon loss, the lower the measurement precision. It means that the measurement precision of $\chi$ can be improved to some extent by increasing the two-photon loss.
Towards Real-Time Human Detection in Maritime Environment Using Embedded Deep Learning

Marine search and rescue missions necessitate a lot of effort and expenses. The use of technological advancements facilitates discovering and locating individuals and aids in the directing of rescuers and medical teams. This has the potential to save human lives while also lowering costs. The characteristics of the marine environment create additional challenges for computer vision techniques used to detect the presence of human in a scene. Currently, artificial intelligence (AI) techniques based on convolution neural networks (CNNs) provide solid solutions to detect and locate objects. In this paper, the relevance of the emergent You Only Look Once (YOLO) in detecting humans in maritime environment is investigated. The available models of YOLOv4 are trained using a custom dataset. The trained models are evaluated using recognized evaluation parameters. In addition, the inference speed is reported targeting embedded low-power hardware platforms dedicated for AI applications.
Adaptive fast desensitized ensemble Kalman filter for uncertain systems

The classical desensitized ensemble Kalman filter can effectively tolerate uncertain parameters of the system. However, the method of solving the gain matrix by algebraic equations must be solved, and the sensitivity-weight must be adjusted in real-time during the filtering process. An adaptive fast desensitized ensemble Kalman filter (AFDEnKF) is proposed to reduce computational cost and adaptively adjust the sensitivity-weight in real-time. In case of the AFDEnKF, an analytical gain matrix is obtained by desensitizing the sensitivity of the state estimation error to the uncertain parameters and minimizing cost functions, and an adaptive factor is introduced to adjust automatically sensitivity-weight by using the orthogonal principle of measurement residuals. Three numerical examples are employed to demonstrate the effectiveness of the proposed AFDEnKF. The simulation results show that the AFDEnKF can effectively improve the computational efficiency and the state estimation accuracy.
The Effect of Perceived Stress and Digital Literacy on Student Satisfaction with Distance Education

To examine the relationship between perceived stress/digital literacy and student satisfaction in health science college students in the distance education process.The cross-sectional study was conducted by collecting data from 842 students. The dependent variable was student satisfaction in distance education. For the analyses, t-test, ANOVA (post hoc: Bonferroni), and linear regression methods were used.Distance education student satisfaction was 178.21 ± 48.64. Student satisfaction was low among those who think that distance education is not more effective than face-to-face education, live in villages/towns, and have high perceived stress. Student satisfaction was high among those who do not have limited internet access, can access the internet via computer, follow the lessons regularly every week, think distance education is applicable in the health domain, can acquire instant feedback from the instructor, and have increased digital literacy.Distance education student satisfaction was found to be moderate. Student satisfaction in distance education increases as perceived stress levels decrease and digital literacy levels increase.
Towards robust and stealthy communication for wireless intelligent terminals

Fifth-generation (5G) wireless systems provide an opportunity for improving the existing Voice over Internet Protocol communication service's user experience. To mitigate the security risk of 5G data leakage, building covert channel is an alternative approach of providing confidential data transmission. Due to the high transmission rate of 5G, the interpacket intervals become small and derandomized, this caused the encoding phase of the covert timing channel imports relatively large modulation errors. rearranging is a widespread phenomenon that is occurred over the data communications. In this paper, we propose a rearrangement covert channel approach named Hybrid Variable-length Packet Rearrangement Covert Timing Channel (HVPR-CTC), which artificially chooses the delimiter packets and identification (ID) packets from the overt traffics, and encodes the packet sending order between adjacent delimiter packets according to a generated hybrid variable-length codeword dictionary, and embeds the secret message by rearranging the sending order of the ID packets. The experiments demonstrate that the HVPR-CTC scheme can effectively perform strategy adjustment: its minimum Location Square Deviation is 0.832 and minimum Swap Deviation is 139. the maximum throughput reaches 14.37 bps, and the optimal Bit Error Rate is 3.27% and 9.70% for low-channel noise and high-channel noise communication conditions, respectively.
EnHAT — Synergy of a tree-based Ensemble with Hoeffding Adaptive Tree for dynamic data streams mining

The goal of this paper is to improve the predictive accuracy of data streaming algorithms without increasing the processing time of the incoming data. We propose the EnHAT (Ensemble Combined with Hoeffding Adaptive Tree) algorithm, which combines the state-of-the-art Hoeffding Adaptive Tree (HAT) algorithm with an ensemble of J48 decision trees induced from sequential chunks of the data stream. The slack time of HAT adaptation to a new window of incoming records is utilized in parallel for building a decision-tree ensemble. In our experiments on 4 benchmark streaming datasets and 4 synthetic datasets with different types of concept drift, EnHAT has reached the highest predictive accuracy in 23 out of 26 cases compared to an ensemble of J48 trees, HAT, and a single J48 model induced from the last sliding window. Thus, we can conclude that the ensemble/HAT synergy yields better prediction results than each one of the two approaches on its own. The higher accuracy does not come at the expense of any additional computational effort beyond the model induction times of the combined algorithms.
Cognitive motivations and foundations for building intelligent decision-making systems

Abstract Concepts based on psychology fit well with current research trends related to robotics and artificial intelligence. Biology-inspired cognitive architectures are extremely useful in building agents and robots, and this is one of the most important challenges of modern science. Therefore, the widely viewed and far-reaching goal of systems research and engineering is virtual agents and autonomous robots that mimic human behavior in solving known and unknown problems. The article proposes, at a high level of generality, an operational cybernetic model of the human mind, developed with the use of carefully selected ideas taken from psychological knowledge. In particular, the work combines extensive knowledge drawn from both the theory of developmental cognitive psychology and the theory of motivation. The proposed mathematically developed operating blocks create a coherent and functional decision-making system containing all the elements necessary in autonomous robotics. The ISD system is under development. There is still a long way to go to full validation. However, as shown in several articles, the basic subsystems of the ISD system, i.e. motivational and emotional, have already been positively verified in operation. The overall purpose of this article is to show a blueprint of the overall concept of the entire ISD.
Multi-modal cognitive computing

The human brain perceives its surroundings through multiple sensory organs and integrates these multi-sensory perceptions to generate a comprehensive understanding. Inspired by synaesthesia, multi-modal cognitive computing endows machines with multi-sensory capabilities and has become the key to general artificial intelligence. With the explosion of multi-modal data such as image, video, text, and audio, a large number of methods have been developed to address this topic. However, the theoretical basis of multi-modal cognitive computing is still unclear. From the perspective of information theory, this paper establishes an information transmission model to profile the cognitive process. Based on the theory of information capacity, this study finds out that multi-modal cognitive computing helps machines extract more information. In this way, multi-modal cognitive computing research is unified by the same theoretical basis. Then, the development of typical tasks is reviewed and discussed, including multi-modal correlation, cross-modal generation, and multi-modal collaboration. Finally, focusing on the opportunities and challenges faced by multi-modal cognitive computing, some potential directions are discussed in depth, and several open-ended questions are considered.
Active Pedestrian Detection for Excavator Robots based on Multi-Sensor Fusion

As a common multi-functional engineering equipment, excavators are widely used in civil construction, coal mining, power engineering, etc. The excellent performance of the excavator not only greatly improves the work efficiency during the construction process, but also effectively saves labor costs. However, due to the complexity of the working environment of the excavator and the blind area of the excavator itself, the driver cannot make timely judgments on the surrounding environment, which may cause potential threats to pedestrians. In response to such problems, this paper proposes a multi-sensor fusion detection method applied to excavators to provide vision assistance for excavator drivers, thereby reducing the risk of pedestrian casualties. Based on the results of the joint calibration, the transformation relationship between the camera and lidar coordinate systems is determined. Combining the detection results of the pedestrian detection algorithm YOLO-v5 and the segmented image information, the position of the pedestrian in the image can be inversely mapped to the 3D point clouds via the matrix transformation, which can accurately display the position of the pedestrian in the point clouds, consequently making up for the lack of depth information in the image. The experimental results show that our method can effectively extract the location information of pedestrians from the complex background environment and realize timely pedestrian alarm.
Performance Evaluation of DLT systems based on Hyper ledger Fabric

Blockchain applications are increasing steadily in real-world applications, and in recent times, there has been a notable increase for permissioned blockchain applications. Hyperledger Fabric (HLF) is one of the Linux Foundation’s permissioned blockchains. Many researchers developed blockchain-based systems on HLF, but the main concern for such systems is performance analysis. The performance analysis of the HLF based systems is essential for accessing their applicability and usefulness. In this paper, we conducted an overview of the various parameter taken into account while implementing the blockchain system on HLF, followed by the study of various metrics taken by the various researchers to analyze the performance of the blockchain system developed on HLF. Caliper has been identified as a prominent performance analysis tool for evaluating the performance of blockchain implementations. Finally, we analyze the performance of the blockchain system in terms of Transaction throughput, latency, success rate and resource consumption using the hyperledger caliper benchmarking tool.