Reviewerly/exHarmony huggingface dataset:
Real-time measurements of phase steps out-of-range <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" altimg="si7.svg"><mml:mrow><mml:mo>(</mml:mo><mml:mn>0</mml:mn><mml:mo>,</mml:mo><mml:mn>2</mml:mn><mml:mi>π</mml:mi><mml:mo>)</mml:mo></mml:mrow></mml:math> by a dynamic self-calibrating generalized phase-shifting algorithm

Recently, a method for measuring phase steps in real-time from a dynamic interferogram within the range (0,2π) has been presented. It was named Dynamic Volume Enclosed by a Surface (DVES) method and was built on a very well known algorithm of type self-calibrating generalized phase-shifting interferometry relied on geometric concept of volume enclosed by a surface (VES), which is enough fast, and accurate. DVES is validated by the properties and conditions given by VES, so a measurement within the range (0,2π) is accepted, and out-of-range is rejected. In this manuscript, we restructure the DVES method to be able of measuring in real-time phase steps out-of-range (0,2π) rad, which is the best of our contributions. Besides, the VES conditions are dropped, except at t=0 when the system is calibrated, and the VES properties are synthesized mathematically to only a control signal, and two equations to correct the measured phase steps automatically when necessary. Finally, an unwrapping algorithm is applied to obtain a continuous curve. It makes the present proposal a very efficient method for real-time measurements without limitations in the phase-steps. The viability of our proposal is done with a numerical simulation, and the verification is carried out by retrieving experimentally a continuous curve of phase steps from a slide respecting to its rotation angle.
Efficient Training of Multi-Layer Neural Networks to Achieve Faster Validation

Artificial neural networks (ANNs) are one of the hottest topics in computer science and artificial intelligence due to their potential and advantages in analyzing real-world problems in various disciplines, including but not limited to physics, biology, chemistry, and engineering. However, ANNs lack several key characteristics of biological neural networks, such as sparsity, scale-freeness, and small-worldness. The concept of sparse and scale-free neural networks has been introduced to fill this gap. Network sparsity is implemented by removing weak weights between neurons during the learning process and replacing them with random weights. When the network is initialized, the neural network is fully connected, which means the number of weights is four times the number of neurons. In this study, considering that a biological neural network has some degree of initial sparsity, we design an ANN with a prescribed level of initial sparsity. The neural network is tested on handwritten digits, Arabic characters, CIFAR-10, and Reuters newswire topics. Simulations show that it is possible to reduce the number of weights by up to 50% without losing prediction accuracy. Moreover, in both cases, the testing time is dramatically reduced compared with fully connected ANNs.
Achieving IoT Devices Secure Sharing in Multi-User Smart Space

Multiple users often share their Internet of Things (IoT) devices in a smart space. However, existing IoT systems do not support IoT sharing between multiple users or take into account the security risks associated with using shared devices. We address this problem by proposing a new multi-user IoT Secure Sharing (IoTSS) system supported by a newly designed sharing policy language. Our approach treats the policies as constraints in the context of an optimisation problem to fulfil user activities using the least vulnerable devices. We show how IoT sharing can be transformed into an equivalent Integer Linear Programming (ILP) problem, which can be solved efficiently and effectively by off-the-shelf Integer ILP solvers. To study the practical feasibility of IoTSS, we have implemented a proof-of-concept proxy-based prototype for the popularly used Mozilla WebThings Gateway. We found that the proxy service can achieve policies enforcement without incurring statistically significant time overhead.
Deep Reinforcement Learning Applied to IEEE Very Small Size Soccer Strategy

This work concerns a virtual learning framework to train and develop specific behaviours for robots in the context of the IEEE Very Small Size Soccer competition based on Deep Reinforcement Learning. The agents are required to learn how to intercept the ball on the player's side of the field in the presence of an adversary robot nearby. The Proximal Policy Optimization algorithm, augmented by Curriculum Learning, is used during the training process. The performance of the trained agents was evaluated and compared to those, in the same scenario, but without the Curriculum Learning technique and also to agents following previously used heuristic behaviors. As a result, agents trained with Curriculum Learning outperformed, with an success rate of 79.90%, both agents trained without Curriculum Learning and agents that used the chosen heuristic behaviour, which attained a success rate of 72.10% and 45.50%, respectively.
Multimedia Communication Security in 5G/6G Coverless Steganography Based on Image Text Semantic Association

Recently, researchers have shown that coverless steganography is relatively safe. On this basis, to improve the payload of the coverless steganography, a novel semiconstruction coverless steganography algorithm is introduced in the paper. Firstly, web crawler technology is applied to crawl a wide range of small icons and hot news images from the Internet. These icons can be used as the training subset, and the hot news can be designed according to construction rules. Secondly, the Alex-Net network is introduced for training in the algorithm, and the adversarial samples are added to the training set. Thirdly, using the preset template, certain small icons and a hot news image are spliced into a secret carrier image according to the construction principle. The hot news image is in the top half of the carrier, and those small icons are in the bottom half. The image on the upper part of the carrier and the icons of the lower part can be connected by image and text semantics, and the semantic matching can be realized between image semantics and explanatory. The experimental results and analysis show that the proposed algorithm can resist steganalysis tools effectively and has good robustness against various image attacks. Meanwhile, the secret information payload has been greatly improved, the maximum payload can reach 180 bits of a single <math xmlns="http://www.w3.org/1998/Math/MathML" id="M1"> <mn>512</mn> <mo>×</mo> <mn>512</mn> </math> image. This promising algorithm can be applied to build covert communications.
Design of an active triangulation based measurement device for pavement surfaces

In this study, a pavement surface texture measurement device is designed and fabricated based on active triangulation. The device acquires 3D surface texture data in a non-contact manner. The device is designed to be reconfigurable, such that there is flexibility in the choice of parameters like data resolution, evaluation area, and speed of data acquisition. The data collected is validated using actual pavement surface. The cost of the fabricated device is also low.
Using geophysical survey as a tool for resolving issues of the structure of the built‐up area of the early medieval centre at Pohansko near Břeclav, Czech Republic

Abstract Early medieval strongholds are a significant phenomenon in the archaeology of East Central Europe, as they mirror the political and socio‐economic changes occurring towards the end of the Early Middle Ages. This contribution is concerned with the site of Břeclav‐Pohansko (Czech Republic), which graphically illustrates the rise and fall of the Moravian principality during its short existence from the second half of the 9th to the beginning of the 10th century. The site has been systematically excavated over more than 60 years. The excavation work has been accompanied by numerous geophysical surveys. Based on comparing previous knowledge of settlement on the site and the results of magnetic prospection, we evaluated similarities and differences in the central district of the stronghold using statistics and spatial analysis. Upon analysis of the success rate of the applied geophysical method, we present the resulting model of the settlement structure. The results confirm that the structure of the built‐up area in the central district at Pohansko follows an original pattern which has no analogues in the Central Danubian region in the given period.
Interactive visual clustering and classification based on dimensionality reduction mappings: A case study for analyzing patients with dermatologic conditions

Multidimensional data sets are becoming more frequent in practically all research fields, and require complex data analysis techniques in order to extract knowledge from them. In this paper, we propose an interactive visualization tool for performing exploratory data analysis. The tool combines unsupervised and supervised dimensionality reduction methods, such as linear discriminant analysis, or t-SNE, with clustering and classification techniques. Analysts can use several machine learning methods for extracting data structure, and can group data into clusters interactively or through clustering algorithms. In addition they can visualize projections of the data to evaluate the quality of obtained clusters, and to analyze the performance of classification methods. We have applied this tool to analyze a clinical data set related to patients with dermatologic conditions that are under photodynamic therapy. The analysis allowed medical doctors to identify several clinically interesting patient groups. In addition, clinicians discovered a greater efficacy in the treatment of patients with the photosensitizer 5-aminolaevulinic acid nanoemulsion gel compared to those treated with methyl-5-aminolaevulinate cream.
Application of DBSCAN to Anomaly Detection in Airport Terminals

Early identification of abnormal flight conditions is extremely critical in terms of safety precautions and preventing possible incidents. Because there is a massive amount of raw data streaming daily via the ADS-B to Flightradar24, which often approaches hundreds of millions, dealing with such unorganized details and identifying abnormal cases is extremely difficult. In this paper, the DBSCAN algorithm proposes to analyze and identify unknown events within routine flight data without previous knowledge of historical flight data. DBSCAN does not need a complex configuration or consumes a significant amount of time to implement, but rather relies on the appropriate selection of features and also on the particular threshold (ε), The algorithm analyzed three dataset types for Najaf International Airport consisting of 41 B738 flights (DXB), 39 B738 flights (IST), and 29 A310 flights (IKA). The percentage of anomalies in these airlines was as follows: Fly Dubai 1.32 percent, Istanbul Airlines 0.32 percent, while Iranian Airlines recorded the highest anomalies at 1.66 percent. The results of these analyzes must be made available to airspace experts and pilots to assess the value of safety risks.
Some Multilinear Variants of Principal Component Analysis: Examples in Grayscale Image Recognition and Reconstruction

Principal component analysis (PCA) has long been used in computer vision applications such as face recognition. Here, we present an overview of some variants of PCA, including 2D PCA (2DPCA), bidirectional 2DPCA (B2DPCA), and coupled subspace analysis (CSA). Unlike conventional PCA, the variants 2DPCA, B2DPCA, and CSA preserve the original image structure, often providing better recognition and reconstruction results than those obtained with PCA. This article considers the background for these techniques and steps involved in applying these methods, including typical preprocessing of sample images, algorithm description, and classification. These variants of PCA have been successfully used in a number of different areas such as identification of wood species, biometrics (not limited to face recognition), medical imaging, and image compression, to name a few examples; we briefly mention some of these to provide an idea of the scope of applications. We address some advantages and disadvantages of these variants in relation to PCA. Utilizing the Modified National Institute of Standards and Technology (MNIST) digits and Fashion-MNIST image sets, we demonstrate application of CSA for image recognition and reconstruction compared to PCA. Finally, we mention how these PCA variants fit into a more general framework using tensors.
Anomaly Detection and Sampling Cost Control via Hierarchical GANs

Anomaly detection incurs certain sampling and sensing costs and therefore it is of great importance to strike a balance between the detection accuracy and these costs. In this work, we study anomaly detection by considering the detection of threshold crossings in a stochastic time series without the knowledge of its statistics. To reduce the sampling cost in this detection process, we propose the use of hierarchical generative adversarial networks (GANs) to perform non-uniform sampling. In order to improve the detection accuracy and reduce the delay in detection, we introduce a butter zone in the operation of the proposed GANbased detector. In the experiments, we analyze the performance of the proposed hierarchical GAN detector considering the metrics of detection delay, miss rates, average cost of error, and sampling ratio. We identify the tradeoffs in the performance as the butter zone sizes and the number of GAN levels in the hierarchy vary. We also compare the performance with that of a sampling policy that approximately minimizes the sum of average costs of sampling and error given the parameters of the stochastic process. We demonstrate that the proposed GAN-based detector can have significant performance improvements in terms of detection delay and average cost of error with a larger butter zone but at the cost of increased sampling rates.
Adapting the Learning Rate of the Learning Rate in Hypergradient Descent

Gradient descent is a widely used optimization method. The adjustment of the learning rate is an important factor in improving its performance, and many researchers have investigated methods for automatically adjusting the learning rate. One such method, hypergradient descent, automatically adjusts the learning rate by using gradient descent. However, it introduces the “learning rate of the learning rate,” and an appropriate value for the learning rate of the learning rate must be chosen in order to effectively adjust the learning rate. We investigated the use of two datasets and two optimization methods for doing this and achieved an effective adjustment of the learning rate when the objective function was convex and <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$L$</tex> -smooth.
A hybrid optimizer based on backtracking search and differential evolution for continuous optimization

This paper introduces a novel hybridisation technique combining the Backtracking Search (BS) and Differential Evolution (DE) algorithms. The proposed hybridisation executes diversity loss and stagnation detection mechanisms to maintain the diversity of the populations, in addition, modifications are done over the mutation operators of the component algorithms in order to improve the search capability of the proposal. These modifications are self-adapted and implemented simultaneously. Extensive experiments to establish the optimal configuration of the parameters are also presented through the introduced technique. The proposed hybridisation approach has been applied to five classical versions and two state-of-the-art variants of DE and tested against 28 well-known benchmark functions with different dimensions, each type of which highlights a different set of characteristics and provides a baseline measurement to validate the performance of the algorithms. In order to further test the proposal, the four outstanding algorithms in the state of the art have also been included in the comparisons. Experimental results show the effectiveness of the proposed hybrid framework over the compared algorithms.
Efficient Deep Structure Learning for Resource-Limited IoT Devices

Nowadays, deep neural networks (DNNs) have been rapidly deployed to realize a number of functionalities like sensing, imaging, classification, recognition, etc. However, the computational-intensive requirement of DNNs makes it difficult to be applicable for resource-limited Internet of Things IoT devices. In this paper, we propose a novel pruning-based paradigm that aims to reduce the computational cost of DNNs, by uncovering a more compact structure and learning the effective weights therein, on the basis of not compromising the expressive capability of DNNs. In particular, our algorithm can achieve efficient end-to-end training that transfers a redundant neural network to a compact one with a specifically targeted compression rate directly. We comprehensively evaluate our approach on various representative benchmark datasets and compared with typical advanced convolutional neural network (CNN) architectures. The experimental results verify the superior performance and robust effectiveness of our scheme. For example, when pruning VGG on CIFAR-10, our proposed scheme is able to significantly reduce its FLOPs (floating-point operations) and number of parameters with a proportion of 76.2% and 94.1%, respectively, while still maintaining a satisfactory accuracy. To sum up, our scheme could facilitate the integration of DNNs into the common machine-learning-based IoT framework, and establish distributed training of neural networks in both cloud and edge.
High Speed Tracking with Machine Intelligence

This paper presents High Speed Tracking. High Speed tracking is the term we understand by tracking objects at a very high-speed environment. The core problem with trackers today is to distinguish between the target and the environment surrounding it. Here, in this writing we will determine how the problem of detecting distinguishable objects can be solved, which is an effective and simple framework is to check the current results and respond to it accordingly when needed. The purpose of this system will help ensure laws where the cars are speeding across the roads without any regards to the traffic rules. This system will detect road accidents, detect speed via IR sensor using distance and time at the point they will pass alerting the people to slow down from speeding accidents enforcing the laws on the citizen making them understanding they are not above the law.
Investigation of the possibilities for application of DLT in the production of electricity from biogas

The article lists the sources that generate carbon and sulfur emissions in the production of electricity worldwide. As carbon emissions lead to a greenhouse effect to achieve the recommendations of environmentalists to reduce the temperature by 2°C it is necessary to reduce them. The main methods for processing and reducing CO <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> are indicated. One of them is the production of electricity from biogas. A study was conducted on all partners responsible for the production, trade and consumption of the electricity from biogas. An analysis of the scheme for the production of electricity from biogas revealed opportunities for the implementation of DLT, despite the available technical, technological and financial barriers. A UML diagram for the communication flows is made. Specific examples are given for other possible applications.
An Efficient Task Scheduling Using GWO-PSO Algorithm in a Cloud Computing Environment

Nowadays, cloud computing becomes a major technology that provides services to users by integrating Internet technology. Scheduling of user tasks plays a vital role in a cloud environment. An efficient task scheduling method is required to manage the resources for executing user tasks in order to attain better result in QoS parameters. Task scheduling is a kind of non-deterministic polynomial (NP) hard problem, which can be solved using optimization algorithms. This paper presents a novel hybrid method by combining grey wolf optimization (GWO) and particle swarm optimization algorithm (PSO). The proposed method optimizes the result in order to maintain the makespan as a minimum. PSO improves the optimization performance of grey wolf optimization in the proposed method. The proposed algorithm performance is evaluated and analysed with GA and GWO algorithms. The proposed GWO-PSO algorithm results are better than the GWO and GA algorithms in terms of response time and makespan.
A control parametrization based path planning method for the quad-rotor uavs

&lt;p style='text-indent:20px;'&gt;A time optimal path planning problem for the Quad-rotor unmanned aerial vehicles (UAVs) is investigated in this paper. A 3D environment with obstacles is considered, which makes the problem more challenging. To tackle this challenge, the problem is formulated as a nonlinear optimal control problem with continuous state inequality constraints and terminal equality constraints. A control parametrization based method is proposed. Particularly, the constraint transcription method together with a local smoothing technique is utilized to handle the continuous inequality constraints. The original problem is then transformed into a nonlinear program. The corresponding gradient formulas for both of the cost function and the constraints are derived, respectively. Simulation results show that the proposed path planning method has less tracking error than that of the rapid-exploring random tree (RRT) algorithm and that of the A star algorithm. In addition, the motor speed has less changes for the proposed algorithm than that of the other two algorithms.&lt;/p&gt;
Attention as Activation

Activation functions and attention mechanisms are typically treated as having different purposes and have evolved differently. However, both concepts can be formulated as a nonlinear gating function. Inspired by their similarity, we propose a novel type of activation units called attentional activation (ATAC) units as a unification of activation functions and attention mechanisms. In particular, we propose a local channel attention module for the simultaneous non-linear activation and element-wise feature refinement, which locally aggregates point-wise cross-channel feature contexts. By replacing the well-known rectified linear units by such ATAC units in convolutional networks, we can construct fully attentional networks that perform significantly better with a modest number of additional parameters. We conducted detailed ablation studies on the ATAC units using several host networks with varying network depths to empirically verify the effectiveness and efficiency of the units. Furthermore, we compared the performance of the ATAC units against existing activation functions as well as other attention mechanisms on the CIFAR-10, CIFAR-100, and ImageNet datasets. Our experimental results show that networks constructed with the proposed ATAC units generally yield performance gains over their competitors given a comparable number of parameters.