Reviewerly/exHarmony huggingface dataset:
An Approximation to Gaussian Wavelet Suitable for Analog Filter Implementations

This paper presents a novel fourth order approximation function to 1 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">st</sup> Gaussian wavelet using time domain analysis of linear time-invariant (LTI) systems and the conditions for the transfer functions of realizable analog filters. Fourth order approximation is used for compact circuit implementations which might use a cascade of only two biquads. Transfer function for a realizable filter and the related Simulink model are also supplied in the paper with numerical results.
Anomaly Detection in Distributed Streams

Anomalies refer to any non-conforming patterns to the expected behavior in the system. The detection of anomaly in real time from logs arriving at very high velocity and are in huge volume requires a distributed framework with high throughput and low latency. In this research, statistical method has been implemented for finding the suspicious associations in Spark Streaming, a highly scalable distributed and streaming framework. The models were deployed in both local mode as well as in cluster mode to perform anomaly detection on server logs.
Improvements on k-error linear complexity of q-ary sequences derived from Euler quotients

In this paper, we generalize the results about the k-error linear complexity of binary sequences derived from Euler quotients modulo pr−1 on the q-ary sequences. We also continue the study of q-ary sequences derived from Euler quotients modulo 2p started by R. Mohammed et al. (LNCS, 11634, 2019) and investigate the k-error linear complexity of sequences derived from Euler quotients with a period 2pr. In particular, we generalize the results of Wu et al. (IEEE Access, 8, 2020) about k-error the binary sequences derived by Euler quotients modulo 2p on the same sequences with a period 2pr.
A Cooperative Tracker by Fusing Correlation Filter and Siamese Network

The robustness of model-free trackers is always supported by a model updater and a motion model. However, most state-of-the-art trackers (e.g. correlation-filter or Siamese-network based trackers) are unbalanced in both aspects. Consequently, they drift easily when encountering challenging scenarios such as fast motion, occlusion or background clutter. Inspired by the complementarity of different tracking mechanisms, we propose an adaptive cooperation tracker, where correlation filter and Siamese networks complement each other in their shortcomings. Specifically, our tracker consists of three components: a context-aware correlation filter network (termed as CaCFNet), a Siamese network and a tracking failure estimator. In the online tracking, the Siamese network component locates the target coarsely in a larger search region, and then CaCFNet refines the coarse position for higher accuracy. The Siamese network component is activated adaptively according to the result of failure estimator, which keeps the tracker in real time and avoids interference between two different mechanisms. Moreover, context-aware correlation filter network and Siamese network are trained offline for better feature representation for visual tracking task. Comprehensive experiments are performed on three popular benchmark: OTB2013, OTB2015, VOT2017 to demonstrate the effectiveness of the proposed tracker, and the proposed tracker achieves state-of-the-art results on these benchmark.
On the Development of the Industry Trend of “Ai+Education”

Artificial intelligence+education can redesign and present a new learning space and environment. With the help of Internet of things, big data, cloud computing, vr/ar and other technologies, through learning measurement and online learning platform, learners can acquire more and more effective learning experience and results, and get practical knowledge and skills of problem solving. At present, in the process of promoting “Ai+education” jointly by schools, enterprises and governments, there are many new educational industry trends, and all of them are developing towards the education of the masses, the third party educational services, the support of standard construction, and the formation of the educational industry chain.
CAPEs Advisory: A Conversational Agent Based on NLP Techniques for Professional Examinations Advisory

In our modern day and age, students with different abilities tend to undertake professional examinations (PEs) to obtain certifications that would prove their knowledge and expertise in their respective fields. This will help them when seeking a career boost in various domains. However, a challenging point arises in which many students express a lack of awareness about which PEs to consider, and which PE is best suited to their professional needs. In this research, a solution is proposed to overcome this challenge by designing and developing a web-based recommendation system based on a textual Conversational Agent (CA) called the Conversational Agent for Professional Examinations (CAPEs) Advisory. The CAPEs Advisory provides smart recommendations for better exam pathways that would suit the student’s various types of knowledge and skill level at University. The proposed architecture for the CAPEs Advisory uses Natural Language Processing (NLP) techniques by applying both Pattern Matching (PM) and a semantic similarity algorithm to extract keywords from the user’s utterances to match patterns in the scripted conversation. An evaluation methodology and experiments have been designed and conducted by using subjective and objective methods to evaluate the CAPEs Advisory components. The results showed a statistically significant impact on the effectiveness of the CAPEs Advisory engine in recognizing 97.36% of the utterances. In addition, the results show that the CAPEs Advisory is effective as a Professional Examinations Advisory with the majority user satisfaction being 83.3%.
Enhancing PSO for Dealing with Large Data Dimensionality by Cooperative Coevolutionary with Dynamic Species-Structure Strategy

It is widely recognized that the performance of PSO in dealing with multi-objective optimization problem is deteriorated with an increasing of problem dimensionality. The notions of cooperative coevolutionary (CC) allow PSO to decompose the large-scale problem into the multiple subcomponents. However, the combination of CC and PSO tends to perform worse if interrelation among variables is exhibited. This paper proposes the dynamic species-structure strategy for improving search ability of CC and incorporates it with PSO algorithm. The resulting algorithm, denoted as “DCCPSO”, is tested with the standard test problems, widely known as “DTLZ”, with 3–6 optimized objectives. In the large-scale problem setting, the experimented results reveal that our proposed decomposition strategy helps enhancing performance of PSO and overcoming problems pertaining to interrelation among variables issues.
Modeling and Performance Evaluation of Auction Model in Cloud Computing

Cloud computing addresses various issues in modern technology such as resource allocation, resources adaption, and resource provisioning. To provide better services to users in cloud computing, economic models have been used. This research work provides the definition of game theory and Pareto optimality. The proposed work in cloud computing (CC) is performed for users who are using cloud for the execution of their applications and uses game theory auction model to achieve outcomes and proposes metrics such as resource utilization and profit maximization. The proposed metrics are simulated using Cloudsim simulator for parallel workflow tasks and achieve Nash equilibrium point for non-cooperative users even when users have insufficient knowledge of the environment.
Epilogue. RESETTING smart city citizenship: Amidst the post-COVID-19 hyperconnected-virialised societies

This epilogue concludes the book by pointing out the current reset the global world through its smart cities is facing. The epilogue suggests that smart city citizenship may evolve from the present 'the new normal' towards new emerging typologies of citizenship amidst the post-COVID-19 hyperconnected and virialised societies, including pandemic, digital, algorithmic, and liquid citizenship. As such, the epilogue states the term algorithmic nations as a geopolitical term by which the new global order may be established. In this changing global realm, at least in Europe, technological sovereignty principle could be intensified to protect citizens' digital rights by embodying through platform and data co-operatives stemming from data altruism and donation policy schemes. Ultimately, the epilogue suggests the inevitable obligation to do the right thing after the reset.
Intelligent modulation recognition algorithm for optical communication

In order to improve the modulation recognition algorithm effect of an optical communication system, an improved modulation recognition algorithm technology based on wavelet transform on the time-varying optical communication system is designed. Firstly, considering the time-varying effect of noise on the communication system, wavelet transform is introduced to pre-process the signal of the optical communication system, extracting the characteristic number of modulation recognition of optical communication system, and a classifier of modulation recognition is established by data mining technology to realize modulation recognition technology of optical communication system. Finally, several typical modulation modes of the optical communication system are tested by simulated experiments. In this paper, several typical modulation modes are identified accurately, and real-time on-line analysis of modulation recognition is realized. In the optical communication system, the recognition accuracy of the modulation pattern is higher than other modulation recognition technologies, which has high practical application value.
Holistic Siamese Model Optimized for Aged Face-Sketch Similarity Detection

The proposed work presents an Holistic approach for Face-sketch matching using a Siamese scenario. Proposed scheme is simple yet effective in overcoming the computational complexity of the feature based scheme and also provides a F1 score of 0.73 and accuracy of nearly 74%. The results are in demanding circumstances of using ageing effects in sketches. Optimum model from Siamese linkages is acquired with augmented sketches on CUHK dataset. The sketches are trained under augmented versions of zooming, distortion and cropped versions of them. The validation curve with merely L1 regularization peaks at 73% while it finds one at 77% with a combined L1 and L2 regularization for an average batch size of eight. The efficacy of the regularization in conjunction with augmentation prevents overfitting and provides effective results in ageing sketches.
Survival Loss: A Neuron Death Regularizer

We found that combining the L2 regularizer with Adam kills up to 60% of filters in ResNet-110 trained on CIFAR-100 as opposed to combining L2 with Momentum. It does not have a significant impact in terms of accuracy though, where both reach similar values. However, we found that this can be a serious issue if the impaired model is used as a pre-trained model for another more complex dataset (e.g. larger number of categories). This situation actually happens in continual learning. In this paper we conduct a study on the impact of dead filters in continual learning when the dataset increases its difficulty over time and more power from the network is required. Furthermore, we propose a new regularization term referred to as survival loss, that complements L2 to avoid filters to die when combined with Adam. We show that the survival loss improves accuracy in a simulated continual learning set-up, with the prospect of higher improvements as more iterations arrive.
Comparative Analysis of FPGA Based Hardware Design of Dynamic Time Warping Algorithm using Different Multiplier Architectures

Dynamic Time Warping algorithm is used widely in speech recognition applications to provide a measure of similarity between two time-varying sequences. It finds its use in the speech processing applications due to the fact that the two sequences to be compared may be of different lengths and Euclidean distance computation may not give a true picture of the diversity between the two sequences. This algorithm involves a lot of computational complexity because of the large number of multiplication operations involved in its implementation. Also, the time delay is quite significant. In this work, hardware implementation of DTW matrix computation along with a comparative analysis using different multipliers for studying the hardware utilization statistics and time delay involved in the operation has been done. Verilog Hardware Description language (HDL) has been used and all the simulations have been performed on Model Sim.
Maximizing the Impact of Mobile Devices

The authors feature the M-Learning Framework as a way to view the effective use of mobile devices. Video cases demonstrate the three key features of the framework: (1) authenticity, (2) personalization, and (3) collaboration.
A Meta-Analysis of the Effects of Inputand Output-Based FonF in Korean Language Education

This study aims to systematically examine the effects of input- and output-based Focus on Form (FonF). A meta-analysis was conducted of a total of 113 effect sizes taken from 47 studies, and the main findings are as follows. First, while the effect size of output-based FonF (0.798) was greater than that of input-based FonF (0.750), the difference was not statistically significant. Second, upon analyzing effect size according to moderator variables, the proficiency variable influenced the effect sizes of both input-based and output-based FonF, and the higher the proficiency, the greater the effect size. The variables of students per class and level of treatment intensity only influenced the effect size of input-based FonF, where the effect size grew with a larger class and higher level of treatment intensity. Furthermore, the duration of the treatment variable showed a statistically significant difference only for the effect size of outputbased FonF, where longer duration displayed higher effectiveness.
VtNet: A neural network with variable importance assessment

The architectures of many neural networks rely heavily on the underlying grid associated with the variables, for instance, the lattice of pixels in an image. For general biomedical data without a grid structure, the multi‐layer perceptron (MLP) and deep belief network (DBN) are often used. However, in these networks, variables are treated homogeneously in the sense of network structure; and it is difficult to assess their individual importance. In this paper, we propose a novel neural network called Variable‐block tree Net (VtNet) whose architecture is determined by an underlying tree with each node corresponding to a subset of variables. The tree is learned from the data to best capture the causal relationships among the variables. VtNet contains a long short‐term memory (LSTM)‐like cell for every tree node. The input and forget gates of each cell control the information flow through the node, and they are used to define a significance score for the variables. To validate the defined significance score, VtNet is trained using smaller trees with variables of low scores removed. Hypothesis tests are conducted to show that variables of higher scores influence classification more strongly. Comparison is made with the variable importance score defined in Random Forest from the aspect of variable selection. Our experiments demonstrate that VtNet is highly competitive in classification accuracy and can often improve accuracy by removing variables with low significance scores.
Hierarchical Modeling with Neurodynamical Agglomerative Analysis

We propose a new analysis technique for neural networks, Neurodynamical Agglomerative Analysis (NAA), an analysis pipeline designed to compare class representations within a given neural network model. The proposed pipeline results in a hierarchy of class relationships implied by the network representation, i.e. a semantic hierarchy analogous to a human-made ontological view of the relevant classes. We use networks pretrained on the ImageNet benchmark dataset to infer semantic hierarchies and show the similarity to human-made semantic hierarchies by comparing them with the WordNet ontology. Further, we show using MNIST training experiments that class relationships extracted using NAA appear to be invariant to random weight initializations, tending toward equivalent class relationships across network initializations in sufficiently parameterized networks.
Design and Implementation of an Air Simulated Shooting Training System

Air simulated firing training system design, is for a certain type of machine can't air simulated firing training and development of embedded weapon firing training simulation system for special purpose, and specially designed for the system to the infinite launches air simulation training, the training on the system design is consistent with the real platforms in the air to shot, effectively improve the effect of simulated firing training, after simulated firing training, firing live ammunition to start hitting the target, greatly reduce the training costs, and improve the training efficiency, for other aerial platform development similar simulated firing training system have great reference value.
Using Pupil Light Reflex for Fast Biometric Authentication

The prevalence of using biometric for user authentication has attracted much attention. On the other hand, mobile devices such as smart glasses and VR headsets have gained great popularity among recent years. It is desirable to integrate authentication into these mobile devices to offer security, which has become more and more indispensable. However, existing biometric techniques (e.g. fingerprint or iris) and traditional methods (e.g. PINS or passwords) are spoofing vulnerability because of their traits. Because their information can easily be leaked or mimicked by artificial means. This study devises an authentication system for Smart Helmet devices. We build upon the fact that Pupil Light Reflex (PLR) can be reflexive. The expectation is that even if you know the fact that when your pupil is stimulated by the light, the pupil size will decrease and vice versa. It is challenging for someone to manipulate our pupil to control the pupil size since it belongs to the body mechanism change, human beings are unable to control. In this paper, to evaluate the reliability, we design an experiment device to perform a series of user experiments with 20 volunteers from the general public. We achieved the False Accept Rate (FAR) of 0.19% and the False Reject Rate (FRR) of 6.67%. Furthermore, we lower the authentication times to 2s. To the best of our knowledge, it is the first research study on Pupil Light Reflex for secure mobile devices authentication.
Locally Consistent Constrained Concept Factorization with Lp Smoothness for Image Representation

Matrix factorization based on image representation algorithms have been widely used to deal with high-dimensional data. Previous studies have shown that matrix factorization methods can achieve remarkable performances in clustering. In this paper, we propose a novel method for image representation, called Locally Consistent Constrained Concept Factorization with Lp Smoothness (LCCCF-LS). The main contributions of our proposed LCCCF-LS method mainly include as follows: Firstly, the local geometric structure of the data is effectively explored using a graph regularizer. Secondly, the label information of the concepts is consistent with known label information without additional parameters. Finally, we add the Lp smoothness constraint to produce a smooth and more accurate solution, and thus ensure the smoothness of the coefficient matrix. Comprehensive experiments on several image datasets manifest the superiority of the proposed LCCCF-LS method.
Text Mining Analysis of the King James Version &amp; New International Version: Concerns and Implications for ESL Readers

English as a Second Language (ESL) students are often expected to read and use English translations of the Bible for academic and worship purposes. The purpose of this study was to explore the writing style of the King James Version and the New International Version in terms of each translation’s formality, readability, and sentiment using quantitative text mining analysis tools. Results indicated that the KJV used more formal language, had a higher grade level of readability and used slightly more positive wording than the NIV. In addition, for both translations the Old Testament was much more negative in terms of sentiment than the New Testament. Lastly, a moderate positive relationship was found between readability and formality for both translations.
Visual Privacy Management

In this chapter, we focus on the privacy and the security analyses of public administration (PA) systems regarding the privacy concerns and requirements. As it has been described in the architecture of VisiOn, the VisiOn Privacy Platform is composed of two frameworks and a common back-end that provides internal functionalities, data storage, etc.
Dynamic Integrity Verification of Cloud Storage Data Based on Data Proxy

To solve the problems of frequent changes of cloud storage data blocks and high audit overhead, a data agent-based dynamic data integrity verification scheme is proposed, which supports public verification and meets dynamic operation requirements. Fine-grained data-based signature methods have significant advantages in significantly reducing signature-related costs. Besides, users can set the number of sub-blocks according to their own needs in the application process, and the scalability also reaches a high level. Under the lattice-based cryptosystem, this scheme is much more efficient than the traditional one because it mainly uses linear computation. The security of this scheme is mainly related to the decimal integer solution on the lattice. High security can affect the resistance against quantum attack and has high application value.
Path Planning of the Spherical Robot based on Improved Ant Colony Algorithm

With the development and progress of science and technology, the requirement of intelligent algorithm performance in the field of path planning is constantly improved. It is very important to improve the performance of intelligent algorithm and apply it in the field of path planning. In order to overcome the problem that ant colony algorithm tends to fall into local optimal in the early stage and converge slowly in the late stage. This paper proposed an ant colony algorithm based on adaptive pheromone updating strategy and enhanced negative feedback mechanism. The simulation results show that the improved ant colony algorithm is superior to the traditional ant colony algorithm and solves the problem of insufficient convergence in the early stage and low convergence in the late stage. Finally, the algorithm is tested by spherical robot. The experimental results show that the improved ant colony algorithm overcomes the shortcomings of traditional algorithms and verifies the effectiveness and convergence of the algorithm.
Multi-city street-sidewalk imagery from pedestrian mobile cameras

This paper presents TerraFirma, a multi-city dataset which captures street and sidewalk imagery from the pedestrians' perspective. Motivated by challenges in the realm of pedestrian safety, we present a diverse and extensive dataset that provides a foundation for the design and validation of pedestrian safety systems that rely on street-sidewalk imagery. The data was collected by 9 volunteers in 4 metropolitan cities across the world. Volunteers carried mobile cameras or smartphones in a texting position, such that the rear camera was directed to the ground in front of them. TerraFirma classifies images by the material used for street/sidewalk construction in each city. The detailed description of dataset accrual is accompanied by the public release of the dataset.