Reviewerly/exHarmony huggingface dataset:
Sequential Inverse Covariance Intersection Fusion Kalman Filter for Networked Systems with Multiplicative Noises and Correlated Noise

In this paper, the problem of fusion estimation for networked multi-sensor systems with multiplicative noise and correlated noise is studied. First, the state space model is transformed into a new system with fictitious noises and local Kalman filter are obtained. Then the sequential inverse covariance intersection (SICI) fusion algorithm is applied and the SICI fusion estimator is proposed to avoid the computation burden of local estimators. Compared with the Sequential Covariance Intersection (SCI) fusion algorithm, SICI fusion algorithm is less conservative, and its estimation accuracy is higher than that of local filter and SCI fusion estimator. The validity and consistency of the proposed fusion estimator are verified by a simulation example.
Towards real-time activity recognition

Activity recognition relates to the automatic visual detection and interpretation of human behaviour and is emerging as an active domain of computer vision. It has important applications such as identifying individuals who are at risk of suicide in public locations such as bridges or railway stations. These individuals are known to exhibit easily observable activities and behaviours such as pacing, looking up and down the railway tracks, and leaving objects on the platform. In order to detect these behaviours, an approach to individual person activity recognition is needed which can run in real time and monitor multiple individuals in parallel. We present a method for human activity recognition using skeletal keypoints and investigate how using varying sample rates and sequence lengths impacts accuracy. The results show that for any given sequence length, optimising the sample rate can result in an overall increase in classification accuracy and improvement in run-time. Results demonstrate that finding the optimal time period over which to sample frames is more important than simply decreasing the number of frames sampled. Further, we show that keypoint based activity recognition approaches outperform other state of the art approaches. Finally, we show that this approach is fast enough for real time activity recognition when up to 14 people are present in the image whilst maintaining a high degree of accuracy.
Scanpaths Generation for Target Search Based on Deep Learning

Scanpath is a sequence of gaze fixations changing with time when browsing something, which records eyes' movement dynamically. Accurate prediction of scanpaths can help computers better predict which area human pay attention to and drive the development of next-generation systems that can understand human behavior and needs. Therefore, this paper introduces an algorithm of scanpaths generation for target search. Based on one-shot learning network, the algorithm extracts the target map with task information, and then imports it into a visual model of the superior colliculus to predict the task-drive scanpaths. The results are similar to the gaze behavior of human eyes in shape, direction and length. At the same time, the algorithm also has the ability to predict the scanpaths based on unseen categories.
Auto-scaling Applications in the Cloud by Simple Indexes with Complex Loads

Applications executed in the cloud can exploit its elasticity features, varying dynamically the amount of leased resources so as to adapt to load variations and to guarantee quality of service. As auto-scaling has implications on execution costs, making optimal scaling choices is of paramount importance. This paper presents an analysis method based on offline benchmarking and simple models that allows to evaluate performance indexes useful to define scaling policies to be used by auto-scalers. The proposed approach relies on a fixed set of benchmarks, to be executed off-line and a set of models that enable prediction of the same performance indexes under different workload conditions, enabling the analyst to perform parameter analysis when defining an auto-scaling policy.
Collaborative Collision Avoidance for CAVs in Unpredictable Scenarios

Modern connected and automated vehicles (CAV) are capable of making informed decisions in unexpected situations. CAVs can achieve this by collaborating with other CAVs using communication and sensing capabilities. This work discusses a partially-decentralized collaborative decision making approach for a coalition of CAVs in the presence of a misbehaving vehicle. A novel algorithm based on Monte Carlo Tree Search (MCTS) is presented for the CAV's planning problem of deriving mitigation action plans. This algorithm reduces the size of the search tree exponentially to overcome the computational limitations of MCTS for large action-agent sets. V2V communication is used to ensure that mitigation action plans chosen by coalition members are conflict-free when possible. The proposed method is evaluated for several conflict scenarios showing that the system can effectively avoid collisions in diverse situations.
A precise three-step phase retrieval algorithm suitable for the interferograms with less than one fringes numbers

In order to improve the accuracy and expand the range of the phase-shifting algorithm application, a precise three-step phase retrieval algorithm suitable for the interferograms with less than one fringes numbers is proposed. The proposed method improves the accuracy, on the one hand because normalization and subtraction are introduced to reduce background intensity fluctuation, on the other hand because Lissajous ellipse fitting (LEF) method is introduced to reconstruct phase maps from the normalized and Gram-Schmidt (GS) orthonormalized maps. Because the approximation operations for cumulative sum aren't used in the GS method, the proposed algorithm is suitable for not only interferograms that the fringes numbers are more than one but also interferograms that fringes numbers are less than one. Moreover, the proposed method can be applied to simple and complex patterns. Thus, the proposed algorithm has a wider range of applications. The proposed algorithm's reliability and performance are confirmed by simulations and experiments.
Regulating Digital Transformation

Digital transformation is the core value of the 4th industrial revolution or Industry 4.0. Although the efforts are focused on utilization and usage of technologies like artificial intelligence, internet of things, cloud computing, and many other technologies. We should focus in parallel on regulating the technologies to provide the proper road map and put the proper regulatory frameworks to remove uncertainties in development, investment, or implementation of technologies. Regulatory effectiveness is very important in saving time and rolling out technologies. In one initiative, regulatory technology (RegTech) will utilize the technology to enhance the regulatory process in financial sector; Regulation 4.0 is another; however, plenty of work is still ahead. Some regulators are keen to facilitate the emerging technologies and to see the consequences immediately, thus they use sandbox thinking in evaluating the impact at a small scale. In summary, this chapter will highlight the different practices and methodologies to regulate the digital transformation in the best way.
Multiple agents cooperative control based on QMIX algorithm in SC2LE environment

This paper studies the problem of multiple agents cooperative control in some complex scenarios. We use the currently value based multi-agent reinforcement learning algorithm QMIX to realize collaborative control. In this paper, we focus on the solution of traditional centralized training with decentralized execution problem (CTDE) in the multi-agent reinforcement learning tasks and the performance of algorithm in some complex environments. We conduct our experiments in the StarCraft II micromanagement scenarios based on the SC2LE environment, the results verify that QMIX algorithm adopted in this paper can obtain more satisfactory training results compared with other reinforcement learning algorithms and effectively improve the effect of multi-agents cooperative control.
Application of the Fisher-Yates Shuffle Algorithm in the Game Matching the World Monument Picture

The application of the concept of artificial intelligence is very helpful in various randomization problems. One example of the application of artificial intelligence to the problem of randomization is in the game of matching images of world monuments. In the game matching world monument images, a randomized picture sequence will appear, and then the player will click on the image to open the image, and then click on another image for the same or matching image. The picture order for the new game will be randomized, so the image order display will not be the same as the previous image order display. In this study, the process of randomizing this game using randomization algorithms that exist in the science of artificial intelligence is the Fisher-Yates Shuffle algorithm. Shuffling algorithm Fisher-Yates Shuffle can randomize the order of data entered into the array because this algorithm is biased or it is unlikely to appear in the same order. This game matching application of world monuments can support learning in honing children's memory skills. This game consists of 3 levels. At the easy level, there are 16 boxes containing 8 pairs of images, at the moderate level there are 20 boxes containing 10 pairs of images, while at the difficult level contains 24 boxes containing 12 pairs of images that must be matched.
Performance Comparison: Virtual Machines and Containers Running Artificial Intelligence Applications

With the continuous growth of data that can be valuable for companies and scientific research, cloud computing has shown itself as one of the emerging technologies that can help solve many of these applications that need the right level of computing and ubiquitous access to them. Cloud Computing has a base technology that is virtualization, which has evolved to provide users with features from which they can benefit. There are different types of virtualization and each of them has its own way of carrying out some processes and of managing computational resources. In this paper, we present the comparison of performance between virtual machines and containers, specifically between an instance of OpenStack and docker and singularity containers. The application used to measure performance is a real application of artificial intelligence. We present the obtained results and discuss them.
Image-Type Labellings/Colorings In Graph Homomorphisms And Graphic Lattices

In topological authentication for encrypting networks wholly we define matchings of dual-type and image-type labellings/colorings, such as totally-dual (W <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">f</sub> , W <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">g</sub> )-type total-image labellings/colorings, uniformly dual-image (W <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">f</sub> , W <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">g</sub> )type labellings/colorings; and moreover we define graph edge-homomorphism from a graph to another graph, graph edge-homomorphism, dual-image graph base-homomorphism, matching of (W <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">f</sub> , W <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">g</sub> )-type number strings. The Number String Decomposition Problem is proposed for resisting possibly the attack of supercomputers and quantum computing.
Dense stereo fish-eye images using a modified hemispherical ASW algorithm

In this paper, we concentrate on dense estimation of disparities between fish-eye images without corrections. Because of the distortions, fish-eye images cannot be processed directly utilizing the classical adaptive support weight (ASW) method for perspective images. To address this problem, we propose a modified hemispherical ASW method in a hemispherical framework. First, 3D epipolar curves are calculated directly on a hemispherical model to deal with the problem that 2D epipolar curves cannot cover the whole image disc. Then, a modified ASW method with hemispherical support window and hemispherical geodesic distance is presented. Moreover, a three-dimensional epipolar distance transform (3DEDT) is proposed and fused into the matching cost to cope with the textureless region problem. The benefit of this approach is demonstrated by realizing the dense stereo matching for fish-eye images using a public fish-eye data set, for which both objectively evaluated as well as visually convincing results are provided.
Automatic Modulation Format Classification of USRP Transmitted Signals Based on SVM

Automatic modulation classification (AMC) plays a key role in the cognitive radio systems. However, Quadrature Amplitude Modulations (QAM) are hard to distinguish by the AMC technology. What’s worse, most AMC models only function well in the Additive White Gaussian Noise (AWGN) channels where frequency offset is not considered. Hence, the performance of these models in frequency selective real-world scenario needs to be double-checked. In this paper, we propose an automatic modulation classification (AMC) method based on SVM. It analyzes the baseband signal and chooses four statistical features of it as the input of the SVM model. In order to have a better result to classify the 16QAM, 64QAM and 256QAM signals, the number of peaks in the distribution of amplitude is chosen as one of the features. In the presence of frequency off-set, the proposed method can classify seven kinds of modufation formats: BPSK (Binary Phase Shift Keying), QPSK (Quadrature Phase Shift Keying), 16QAM, 64QAM, 256QAM, GMSK (Gaussian Filtered Minimum Shift Keying) and OFDM (Orthogonal Frequency Division Multiplexing). The proposed method has been tested experimentally and shows attractive prospect.
Assessing User Experience of a Secure Mobile Exam Application using UEQ+

Online examination gains popularity during the COVID-19 pandemic due to the massive adoption of online learning. In the online examination, human proctoring activity is hardly possible, and therefore an alternative way to keep academic integrity is needed. Currently, camera, microphone, and gyro sensors have been standard embedded sensors in smartphones. This research utilizes those sensors to detect the possibility of cheating in the online examination. A mobile application with two main functions was developed to record the user activity during the exam as the background and carry out the examination function on the foreground task. Consequently, users may feel uncomfortable due to activity recording requirements. This research reveals many aspects of user convenience during the examination of varied user locations, available bandwidth, and gadgets. A user experience questionnaire plus (UEQ+) was used to understand user feelings while engaging with the application. Our analysis shows no strong correlation between key performance indicator achievement and the smartphone specification. According to the questionnaire analysis, the user considered that the secure exam application achieves 1.61 points in a range of -3 to 3. It is indicated that the users, in general, have a positive experience with the mobile exam application.
Learning Success Prediction Model for Early Age Children Using Educational Games and Advanced Data Analytics

The early years of a child’s life greatly affects education potential and furthermore potential of educational achievements in adulthood. The brain develops faster during early age, and missed cognitive opportunities in that period are difficult to make up for. In this research, we have developed a machine learning model based on the data points obtained from the educational game, with aim to predict how many attempts are necessary for an individual child to complete the task or assessment as part of educational game. In-game assessments are based on the skills that the child already possess and those developed while playing the game. Training of the machine learning model is based on collected and processed data points (features), while model interconnections are related to the factors of the child’s cognitive growing up process. Model performance benchmarks are elaborated in results and conclusion section of the paper as quality measures of the forecast indicators.
Post-Quantum Lattice-Based Cryptography

Quantum cryptography is a branch of cryptography that is a mixture of quantum mechanics and classical cryptography. The study of quantum cryptography is to design cryptographic algorithms and protocols that are against quantum computing attacks. In this chapter, the authors focus on analyzing characteristics of the quantum-proof cryptosystem and its applications in the future internet. Lattice-based cryptography provides a much stronger belief of security, in that the average-case of certain problems is equivalent to the worst-case of those problems. With the increase in cryptanalytic attacks conventional cryptographic schemes will soon become obsolete. As the reality of quantum computing approaches, these cryptosystems will need to be replaced with efficient quantum-resistant cryptosystems. We need an alternate security mechanism which is as hard as the existing number theoretic approaches. In this chapter, the authors discuss the security dimension of lattice-based cryptography whose strength lies in the hardness of lattice problems and also study its application areas.
On unlinkability and denial of service attacks resilience of whistleblower platforms

This work explores how to enhance pseudonymous whistleblower submission systems, specifically by supporting protocol level unlinkability, while also making the system resilient against (distributed) denial of service attacks. To that end, we propose a blind signature based protocol which facilitates assignment of trust to anonymous posters in a manner which depends on the quality of prior posts, yet unlinkable to said posts or corresponding poster. This (multi-level) trust is leveraged to prioritize the posts, thus mitigating the effect that spam posts may have on the party reviewing the posts. We design and carry out simulations to explore the resilience of the whistleblower submission system against denial of service attacks while applying the proposed approach. Our experiments affirm that for a range of realistic scenarios the proposed approach provides reasonable mitigation.
An Intelligent Digital Secretary for Design of Electric Power Engineering

In order to improve the digital level of design of electric power engineering and ensure the accuracy and reliability, this paper proposes an intelligent digital secretary. It clarifies the construction ideas and applications of this secretary from three aspects: the architecture, functions and intelligent business services. In intelligent business services, this paper proposes for the first time the application of feature learning based on Auto-encoder for electric power engineering design. Then, the feature parameters learned by Auto-encoder are trained through BP neural network to get the values that meet design standards. Furthermore, this paper constructs the loss function based on the optimal bias to get the results more in line with actual design requirements. Automated design algorithm based on the error model is also provided in this paper. The proposed intelligent digital secretary provides a feasible idea for realizing the leap from current manual design mode to digital design mode.
Gifts Recommendation System Based on the Public Big Data of Social Networks

Gifts play a pivotal role in people's daily life. Some people want to give others gifts, but they don't understand other's preferences. This makes giving gifts a difficult problem. Traditional gift recommendation systems can only recommend gifts when users are known to have certain needs. This does not solve the user's recommendation needs for gifts in scenarios where the user's preferences are unknown. According to this demand, this paper proposes a gifts recommendation system based on the public big data of social networks to solve the problem of users' difficulty in selecting gifts.
Visualization of Data Structures with Animation of Code

Sparsha, P. Harish, P. B. Kumar, N. S.Data structures play a major role in software development. However, data structures can be very tricky to understand. The best way to understand data structures is to incorporate them into action. Commonly, the data structures are learned by implementing in ‘C’. Pointers in ‘C’ create another hurdle in learning. The proposed research work has developed a tool that helps in the interactive visualization of a user input code. Here, this paper proposes a web-based tool that takes ‘C’ program as the input from the user and does computations on it and finally creates animation of the respective data structures as well as the code. This helps the user to execute the one line code at a time and view the corresponding changes to the data structures. It provides features such as the animation of simple user programs in ‘C’ consisting of pointers, user-defined structures, and operations such as traversal, insertion and deletion in linked lists. This research work proposes to show the synchronized animation of code and schematic diagram for the linked list as a proof-of-concept.
Colour Neural Descriptors for Instance Retrieval Using CNN Features and Colour Models

Image representations in the form of neural activations derived from intermediate layers of deep neural networks are the state-of-the-art descriptors for instance based retrieval. However, the problem that persists consists of how to retrieve identical images as the most relevant ones from a large image or video corpus. In this work, we introduce colour neural descriptors that are made of convolutional neural networks (CNN) features obtained by combining different colour spaces and colour channels. In contrast to previous works, which rely on fine-tuning pre-trained networks, we compute the proposed descriptors based on the activations generated from a pretrained VGG-16 network without fine-tuning. Besides, we take advantage of an object detector to optimize our proposed instance retrieval architecture to generate features at both local and global scales. In addition, we introduce a stride based query expansion technique to retrieve objects from multi-view datasets. Finally, we experimentally proved that the proposed colour neural descriptors, obtain state-of-the-art results in Paris 6K, Revisiting-Paris 6k, INSTRE-M and COIL-100 datasets, with mAPs of 81.70, 82.02, 78.8 and 97.9, respectively.
Application of Convolutional Neural Networks in Radio Station Link Establishment Behaviors Recognition

Abstract For the problem that it is difficult to recognize the type of link establishment (LE) behaviors of radio station whose communication protocol is unknown, a method is proposed to solve the problem by using convolutional neural network (CNN) to recognize LE behaviors. Themethod processes physical layer signals directly and breaks through limitation of unknown protocol standard. Three classical CNN models are optimized through experiments so that they become more suitable for recognition of one-dimensional time series signals. Experimental results show CNN can effectively recognize the different link establishment behaviors with a large number of training samples. Moreover, DenseNet whose recognition accuracy can reach 96% when the signal-to-noise ratio (SNR) is 0dB has the best performance compared to GoogLeNet and ResNet.