Reviewerly/exHarmony huggingface dataset:
Towards a Learning-enabled Virtual Sensor Forensic Architecture Compliant with Edge Intelligence

While the Internet of Things (IoT), Wireless Sensor Networks (WSNs), and the techniques for extracting digital data have seen continuous advancements, so does the cyber-threat landscape. Virtual sensors which normally use abstraction layers that operate over a physical infrastructure to achieve their objectives, have seen rapid adoption, for example, it has aided in achieving manufacturing 4.0. This abstract layer has in the recent past seen tremendous proliferation within the sensor-based platform. Coupled with data pre-processing and key compliance with the guidelines for information security, incident investigation principles, and processes. This paper discusses a step towards a Learning-enabled (LE) Virtual Sensor Forensic (VSF) architecture that is compliant with edge intelligence technology, which is based on an initially proposed generic VSF architecture. Furthermore, apart from the learning capabilities, the LE-VSF architecture considers proactive and reactive investigation techniques by assuming an Internet of Vehicle (IoV) attack scenario, enhancing the reliability of a forensically sound data source. This proposition is essential in any sensor-based abstraction where the forensic analysis would otherwise be cumbersome and susceptible to noise.
Machine Learning Applications in Decision Intelligence Analytics

Writing software (programs) is an obstruction, we do not have so many good developers who can develop much more enhanced models and so, for this purpose today many use the data instead of people to perform the same task. According to the generations' needs, the programmers developed the machine learning approach to make the programming much more scalable and expandable in this domain. Before, machine learning traditional programming is a much more famous approach where programmers used to code each and every single line with their own and its main drawback is that it is not so much scalable. Here, in this chapter we are going to discuss various applications of machine learning and the algorithms they are using along with their advantage, disadvantage, and its working model of how much the particular application is scalable. Like we are going to discuss virtual personal assistants, email spam, online fraud detection, traffic predictions, social media personalization, and many more. In coming to algorithms, we will get to know about Naive Bayes algorithms, neural networks, KNN algorithms, linear regression model, logistic regression model, etc. On coming to today's need we are also going to discuss its applications in detection of COVID-19 defaulters by the use of semantic segmentation algorithms.
What Makes Consumers’ Intention to Purchase Paid Stickers in Personal Messenger? The Role of Personality and Motivational Factors

Many messengers and social networking services (SNSs) use emojis and stickers as a means of communication. Stickers express individual emotions well, allowing long texts to be replaced with small pictures. As the use of stickers increased, stickers were commercialized on a few platforms and showed remarkable growth as people bought and used stickers with their favorite characters, products, or entertainers online. Depending on their personality, individuals have different motivations for using stickers that determine the usefulness and enjoyment of stickers, affecting their purchase decisions. In the present study, participants (n = 302) who were randomly recruited from a university completed an online questionnaire assessing the Big Five personality characteristics, motivations for using stickers, and the technology acceptance model (TAM). Results using partial least squares structural equation modeling (PLS-SEM) revealed that each personality trait affected different motivations for using stickers. Moreover, motivations for using stickers also influenced different technology acceptance variables. Finally, perceived usefulness, enjoyment, and ease of use had a positive effect on the intention to purchase stickers. This study has implications in that it is an exploratory approach to the intention to purchase stickers, which has been investigated by few prior studies, and it sheds light on the relationship between personality, motivation, and TAM in purchasing stickers. It also suggests that personality and motivation factors can be considered in personalized recommendation services.
Distributed Meta-Learning with Networked Agents

Meta-learning aims to improve efficiency of learning new tasks by exploiting the inductive biases obtained from related tasks. Previous works consider centralized or federated architectures that rely on central processors, whereas, in this paper, we propose a decentralized meta-learning scheme where the data and the computations are distributed across a network of agents. We provide convergence results for non-convex environments and illustrate the theoretical findings with experiments.
Clustering based on adaptive local density with evidential assigning strategy

A new clustering algorithm, based on Adaptive Local Density (ALD) and Evidential K-Nearest Neighbors (EKNN), is proposed here. In density peaks clustering, many other density metrics fail to detect cluster centers on multi-density datasets, however the ALD deals with the tasks very well since it can better utilize the local information. To assign the remaining points after detecting the cluster centers, an assigning strategy in the framework of evidential theory, named EKNN, is created. The advantage of EKNN is twofold. Firstly, by fusing the information of K-Nearest Neighbors, it can reduce the risk of a phenomenon named domino effect: the drawback of one classical clustering, i.e., clustering by fast search and find of density peaks (always named as DPC). Secondly, it can detect border and noise points simultaneously since a credal partition is derived which can mine ambiguity and uncertainty of data structure. Simulations on both synthetic and real-world datasets demonstrate the outstanding performance of ALD-EKNN compared with DPC and some of its successors.
Code-Mixed NLG: Resources, Metrics, and Challenges

This tutorial will elaborate on various available resources for the natural language generation (NLG) tasks in code-mixed languages. We will also discuss the adaptability, limitations, and challenges with various evaluation metrics for the code-mixed NLG. In addition, we will put forward a set of open research questions pertaining to the tasks, resources, experiments, and metrics for the code-mixed NLG.
Engineering the boosting of the magnetic Purcell factor with a composite structure based on nanodisk and ring resonators

The dependence of the optical communication interconnects on the electronics technology is a limitation for ultra-high-speed communications. Nanophotonics promises intriguing technologies to exceed these limitations. In this context, a silicon-based composite nanostructure is proposed to boost the magnetic Purcell enhancement one order of magnitude higher than a single hollow-disk resonator. The boosting effect relies on the near-field interaction between a hollow disk resonator and two larger rings. The engineering of the device is presented via simulations with the aim of obtaining the highest Purcell enhancement. Additionally, to reveal the underlying mechanism, the contribution of each multipole mode to the scattered intensity and near-field enhancement properties are analysed. Finally, the dependence of the Purcell factor on the position of the source is investigated. The results of this study may have a great potential to realize nanoscale light sources to be used in all-photonic chips for ultra-high-speed optical networks.
Sign Language Recognition

Mode of communication for people with disabilities like hearing and speaking has always been controversial. Sign language has proven to be an incredibly beneficial method. It deals with recognizing gestures which are then comprehended as words or alphabets. The majority of the crowd are not often exposed to the world of sign language significantly. This causes issues for people with disabilities which results in deprived communication with the majority. The purpose of this work is to provide a real-time system which can convert our own set of signs into text. First, we create a database as an act of extending a step forward in this field and then various image preprocessing and feature extraction techniques were performed to obtain reasonable result. Initially as an easy approach we went ahead experimenting with just up to 5 different classes/words whose self-made images were fed into our CNN model resulting in a classification accuracy of 97%. As an addition to the work that we accomplished on static images, we created a live demo version of the project which can be run at a little less than 2 seconds per frame to classify signed hand gestures from any person.
Problems and Countermeasures of Information Technology in College English Teaching

In recent years, information technology has developed vigorously and is gradually being popularized in the field of education. The use of information technology in college English teaching has also produced good teaching results. Information technology has mobilized students’ interest in learning in many aspects, such as vision and hearing, which stimulates the motivation of students’ learning, improves students’ independent learning ability, and induces students. The divergent thinking of optimizing students’ overall quality is of great significance. However, there are many problems, including the complete replacement of traditional teaching by information technology, and the excessive amount of information. This article proposes specific countermeasures for these problems in order to promote the effective integration of information technology and high school English teaching and further improve the quality of teaching.
Texture Image Classification and Retrieval Using Multi-resolution Radial Gradient Binary Pattern

In image classification and retrieval applications, images are represented by a set of features. These features are extracted from both spatial and wavelet transformed input images. The wavelet transform decomposes images into multiple resolutions by separating smooth and sharp information in individual channels to give more details about the image. Each level of smooth and sharp decomposed channels is individually involved in texture feature extraction. It is difficult to achieve better results in image classification and retrieval applications with fewer discriminant details of the image. Even though each level of the smooth channel correlates with its successive decomposition levels, these feature extraction methods do not consider the relationship between the multiple levels of decomposed images. This motivates the proposed work to extract the correlation between the different levels of wavelet decomposed images. The proposed work (i) encodes the local difference obtained from multiple radii across the different levels of wavelet decomposed channels, (ii) assesses the proposed texture feature extraction method using classification and retrieval experiments with five different wavelet filters over twelve image databases and (iii) analyses the importance of similarity measures involved in the proposed feature-based image retrieval experiments.
Virtual Reality Assessment and Customization Using Physiological Measures: A Literature Analysis

Understanding the user experience requires gathering self-reported and physiological information. In Virtual Reality (VR), heart rate, skin responses, gaze, brain, and muscle activity can provide a better understanding of how users process perceptual cues and respond to them. While the use of physiological measures is typically done for the assessment and improvement of the user experience, recent trends have shown their application for customizing VR experiences to the unique responses of the users. The purpose of this literature review is to analyze the use of physiological measures in VR customization to identify the state of the art, gaps, and opportunities. A total of 25 articles from indexed journals found in Scopus, PubMed, Web of Science, and IEEE, in addition to top tier conferences such as IEEE VR and CHI within the last five years were reviewed. The review indicates that while assessment continues to be a main field of application of physiological measures, the recent availability of consumer-level VR and the growth of ubiquitous physiological sensors are enabling their use as user input devices for customized and adaptive user experiences.
Radio Modulation Classification Using STFT Spectrogram and CNN

Radio modulation classification is significant in wireless communication. Conventional methods require expert knowledge for feature designing which is difficult to adapt to the complex non-cooperative radio environment. In this paper, a novel scheme with short-time-Fourier-transformation (STFT) and convolutional neural network (CNN) is provides. Signal intra-pulse sequences are first transformed into gray-scaled STFT spectrograms, followed with bi-cubic interpolations, to compress to a fixed image size. Therefore, the classification of radio modulations is equivalent to the spectrogram image recognition problem. Then a CNN is designed to automatically utilize intra-pulse feature extraction and classification. A dataset of 16 different modulation types is provided for experiment, the results demonstrate that our method achieve a significantly improvement than a conventional method, with the classification accuracy increased from 74.3% to 99.0%. The experimental results also indicate that our method can distinguish individuals of the same modulation type.
Entangling power of symmetric two-qubit quantum gates and three-level operations

The capacity of a quantum gate to produce entangled states on a bipartite system is quantified in terms of the entangling power. This quantity is defined as the average of the linear entropy of entanglement of the states produced after applying a quantum gate over the whole set of separable states. Here we focus on symmetric two-qubit quantum gates, acting on the symmetric two-qubit space, and calculate the entangling power in terms of the appropriate local-invariant. A geometric description of the local equivalence classes of gates is given in terms of the $\mathfrak{su}(3)$ Lie algebra root vectors. These vectors define a primitive cell with hexagonal symmetry on a plane, and through the Weyl group the minimum area on the plane containing the whole set of locally equivalent quantum gates is identified. We give conditions to determine when a given quantum gate produces maximally entangled states from separable ones (perfect entanglers). We found that these gates correspond to one fourth of the whole set of locally-distinct quantum gates. The theory developed here is applicable to three-level systems in general, where the non-locality of a quantum gate is related to its capacity to perform non-rigid transformations on the Majorana constellation of a state. The results are illustrated by an anisotropic Heisenberg model, the Lipkin-Meshkov-Glick model, and two coupled quantized oscillators with cross-Kerr interaction.
Gluing Reference Patches Together for Face Super-Resolution

Face super-resolution is a domain-specific super-resolution task to generate a high-resolution facial image from a low-resolution one. In this paper, we propose a novel face super-resolution network, called CollageNet, to super-resolve an input image by exploiting a reference image of an identical person at the patch level. First, we extract feature pyramids from input and reference images to exploit multi-scale information hierarchically. Next, we compute the patch-wise similarities between input and reference feature pyramids and select the K most similar reference patches to each input patch. Then, we compose a collaged feature pyramid by gluing those selected patches together. Finally, we obtain a super-resolved image by blending the collaged feature pyramid and the input feature. Experimental results demonstrate that the proposed CollageNet yields state-of-the-art performances.
Design and Implementation of Data Mining Technology in Music Education Platform

As an indispensable medium between teaching theory and teaching practice, teaching mode can not only effectively guide teaching activities in theory, but also formulate effective operation process and Implementation Countermeasures for specific teaching practice. Music curriculum model based on cloud computing and data mining technology is a new type of music teaching mode, which is closely around the technical guideline of “learning with technology”, based on the education cloud architecture integrated by multimedia music curriculum resources, and reasonably using data mining technology, integrating information technology into interactive music teaching, It not only helps students to better understand the teaching content and grasp the internal laws of the knowledge they have learned, but also effectively solves the shortcomings of the traditional teaching mode of teaching, playing demonstration and students’ imitation. In this paper, the effective use of database, education cloud and other information means, in-depth research based on cloud computing and data mining technology of music education model, in order to provide schools, teachers and students with better information services
A good neighbor is a great blessing: Nearest neighbor filtering method to remove impulse noise

Impulse noise is one of the common noise types that affect images. Median filtering denoising method has been widely used for impulse noise. However, as the noise density increases beyond 50%, the noise reduction quality of median filter reduces sharply. In this paper, a very simple nearest neighbor filtering method1 is proposed for impulse noise reduction. The proposed method exploits the inter-pixel correlation in images. More spatially closer the pixels within an image, more the correlation amongst them. The filter window is partitioned into multiple clusters based on their distance from the pixel that needs to be corrected. The closest non-noisy pixel is searched and is used to replace the corrupted pixel. The proposed method has been simulated for grayscale as well as color images. The simulation results show that the proposed method outperforms the state-of-the-art methods. Also, the computational complexity of the proposed methods is very small as compared to the state-of-the-art denoising methods. The only method that outperforms the proposed method is median filtering method.
A simulation-based study of ZIP regression with various zero-inflated submodels

In this paper, we are interested in the robustness of the estimation in the Zero-Inflated Poisson regression model, when varying the class membership model of the underlying mixture. We propose an estimation procedure based on the maximum likelihood estimator. Simulations are used to examine the performance of the MLE. The results suggest that maximum likelihood allows for accurate inference. Using simulated datasets, we show that the proposed alternative link functions are quite flexible and outperform the standard link function. Also, an application to a real dataset is provided.
Edge detection with attention: From global view to local focus

Recently, edge detection models with convolutional neural networks (CNNs) have achieved significant advances, mostly by convolutional pyramid features and multi-path aggregation to generate accurate boundaries. However, it is still challenging for them when facing complex structure, weak context and dense boundaries. In this paper, we propose an Edge Attention Network (EdgeAtNet) from the viewpoint of attention mechanism. EdgeAtNet is derived from the richer convolutional features (RCF) basic architecture. On the low-level features, a global view attention block is inserted to the bottleneck to capture the long-range dependency of edge features, and on the high-level features, a local focus attention is designed for crisp boundary representation. Using ResNet101 as the backbone, we achieve state-of-the-art (SOTA) performance on several benchmarks. When evaluated on the well-known BSDS500 benchmark dataset, EdgeAtNet achieves the Optimal Dataset Scale (ODS) F-measure of 0.825. On the NYUDv2 and BIPED benchmark datasets, EdgeAtNet obtaines ODS F-measure of 0.764 and 0.868, respectively, and it outperforms the existing most methods.
Colour Image Encryption Using DNA Coding and Logistic Diffusion

The necessity to meet the security demands of digital images has led to effective encryption methods. The suggested encryption technique combines image encryption utilizing chaotic transforms and DNA (Deoxyribonucleic acid) coding procedures. DNA computing techniques are 100 times faster and have less power consumption than other algorithms in modern computers. Using a suitable chaotic map, the encryption technique is done separately on the color image's R, G, and B channels. After chaotic encryption, a technique called DNA encoding is used. A novel conditional shift based on Mandelbrot Set is presented to apply confusion to the three channels successfully. The suggested encryption method, according to security analysis, performs admirably in terms of security.
Smart Contract Fuzzing for Enterprises: The Language Agnostic Way

Blockchain based applications backed by smart contracts are becoming increasingly popular in various domains. Smart contracts are vulnerable to attacks due to bugs in them and such attacks resulted in huge monetary losses, disruption in operation and so on in the past. Fuzz testing is one of the prominent methods used for identifying bugs in blockchain smart contracts. Multiple fuzzers are used for fuzzing smart contracts written in different programming languages. However, maintenance of multiple fuzzers become prohibitively difficult in an enterprise DevOps setup, in terms of skills, time, and efforts required in patching and keeping them up to date. Hence, we propose a novel vulnerability detection framework which uses a single fuzzer to fuzz smart contracts written in different programming languages, using LLVM IR. In this paper, we validated the proposed framework by testing Hyperledger Fabric smart contracts using fuzzing tools AFL++ and Honggfuzz and presented the results.
The acoustics of a plucked harp string

The vibration response of a small harp is studied, by a combination of measurement, theoretical modelling and model-based sound synthesis. The harp’s distinctive angle between strings and soundboard is shown to have little impact on the results of linear modelling, but to have a significant effect on sound through a nonlinear phenomenon. Generation of axial force in the string through geometric nonlinearity is shown to produce very prominent effects, helped by the efficient coupling of longitudinal force to soundboard motion. The detailed pattern of the resulting “phantom partials”, already familiar from work on piano strings, is shown to be sensitive to the exact boundary condition between the string and the soundboard: better agreement between experiment and modelling is obtained by assuming a clamped boundary than a pinned boundary. It is suggested that the relative prominence of nonlinear effects in the harp, compared to other plucked-string instruments using non-metallic strings, may be one reason why harpists still prefer the sound of gut strings over nylon strings.