Reviewerly/exHarmony huggingface dataset:
Multi-Features Capacitive Hand Gesture Recognition Sensor: A Machine Learning Approach

Gesture recognition technology enables machines to understand human gestures. The technology is considered as a key enabler for gaming and virtual reality applications. In this paper, we propose an effective, low-cost capacitive sensor device to recognize hand gestures. In particular, we designed a prototype of a wearable capacitive sensor unit to capture the capacitance values from the electrodes placed on finger phalanges. The sensor captures finger capacitance values. Each gesture has specific finger capacitance values. We applied a running median filter to the output of the sensor and extracted 15 features for gesture classification training and testing tasks. Subsequently, various analyses were performed to provide more insights into the sensing data. We applied and compared two machine learning algorithms: Error Correction Output Code Support Vector Machines (ECOC-SVM) and ${K}$ -Nearest Neighbour (KNN) classifiers. The training and testing recognition rates were observed for both intra-participant and inter-participant data sets. Further, we introduced a feature compression approach derived from correlation analysis to reduce the complexity of the machine learning algorithms. Using cross validation, we achieved a classification rate of approximately 99% for intra-participant data. We achieved a lower recognition rate of 97% (average cross validation testing) for compressed feature data set using both machine learning approaches. For the inter-participant data, the recognition rate was 99% (normalized feature data) using KNN and 97% using ECOC-SVM. The research findings show that our recognition system is competitive and has an immense potential for further study.
Deep embedding clustering based on contractive autoencoder

Clustering large and high-dimensional document data has got a great interest. However, current clustering algorithms lack efficient representation learning. Implementing deep learning techniques in document clustering can strengthen the learning processes. In this work, we simultaneously disentangle the problem of learned representation by preserving important information from the initial data while pushing the original samples and their augmentations together in one hand. Furthermore, we handle the cluster locality preservation issue by pushing neighboring data points together. To that end, we first introduce Contractive Autoencoders. Then we propose a deep embedding clustering framework based on contractive autoencoder (DECCA) to learn document representations. Furthermore, to grasp relevant document or word features, we append the Frobenius norm as penalty term to the conventional autoencoder framework, which helps the autoencoder to perform better. In this way, the contractive autoencoders apprehend the local manifold structure of the input data and compete with the representations learned by existing methods. Finally, we confirm the supremacy of our proposed algorithm over the state-of-the-art results on six real-world images and text datasets.
Education in the Digital Age: Learning Experience in Virtual and Mixed Realities

In recent years Virtual Reality has been revitalized, having gained and lost popularity between the 1960s and 1990s, and is now widely used for entertainment purposes. However, Virtual Reality, along with Mixed Reality and Augmented Reality, has broader application possibilities, thanks to significant advances in technology and accessibility. In the current study, we examined the effectiveness of these new technologies for use in education. We found that learning in both virtual and mixed environments resulted in similar levels of performance to traditional learning. However, participants reported higher levels of engagement in both Virtual Reality and Mixed Reality conditions compared to the traditional learning condition, and higher levels of positive emotions in the Virtual Reality condition. No simulator sickness was found from using either headset, and both headsets scored similarly for system usability and user acceptance of the technology. Virtual Reality, however, did produce a higher sense of presence than Mixed Reality. Overall, the findings suggest that some benefits can be gained from using Virtual and Mixed Realities for education.
How well do hate speech, toxicity, abusive and offensive language classification models generalize across datasets?

A considerable body of research deals with the automatic identification of hate speech and related phenomena. However, cross-dataset model generalization remains a challenge. In this context, we address two still open central questions: (i) to what extent does the generalization depend on the model and the composition and annotation of the training data in terms of different categories?, and (ii) do specific features of the datasets or models influence the generalization potential? To answer (i), we experiment with BERT, ALBERT, fastText, and SVM models trained on nine common public English datasets, whose class (or category) labels are standardized (and thus made comparable), in intra- and cross-dataset setups. The experiments show that indeed the generalization varies from model to model and that some of the categories (e.g., ‘toxic’, ‘abusive’, or ‘offensive’) serve better as cross-dataset training categories than others (e.g., ‘hate speech’). To answer (ii), we use a Random Forest model for assessing the relevance of different model and dataset features during the prediction of the performance of 450 BERT, 450 ALBERT, 450 fastText, and 348 SVM binary abusive language classifiers (1698 in total). We find that in order to generalize well, a model already needs to perform well in an intra-dataset scenario. Furthermore, we find that some other parameters are equally decisive for the success of the generalization, including, e.g., the training and target categories and the percentage of the out-of-domain vocabulary.
An assessment of the interplay between literacy and digital Technology in Higher Education

Abstract Digital technologies fundamentally transform teaching and learning in higher education environments, with the pace of technological change exacerbating the challenge. Due to the current pandemic situation, higher education environments are all now forced to move away from traditional teaching and learning structures that are simply no longer adaptable to the challenges of rapidly changing educational environments. This research develops a conceptual model and employs Structural Equation Modelling (SEM) using Partial least Squares (PLS) to examine the impact of information and digital literacy on 249 Finnish university staff and students’ intention to use digital technologies. The findings show the complex interrelationship between literacy skills and digital technologies among university staff and students. The results illustrate that information literacy has a direct and significant impact on intention to use; while, unlike our expectation, digital literacy does not have a direct impact on the intention to use. However, its effect is mediated through performance expectancy and effort expectancy. The authors suggest that to understand the changes that are taking place in higher education environment, more attention needs to be paid to redefining policies and strategies in order to enhance individuals’ willingness to use digital technologies within higher education environments.
LiDAR Panoptic Segmentation for Autonomous Driving

Truly autonomous driving without the need for human intervention can only be attained when self-driving cars fully understand their surroundings. Most of these vehicles rely on a suite of active and passive sensors. LiDAR sensors are a cornerstone in most of these hardware stacks, and leveraging them as a complement to other passive sensors such as RGB cameras is an enticing goal. Understanding the semantic class of each point in a LiDAR sweep is important, as well as knowing to which instance of that class it belongs to. To this end, we present a novel, single-stage, and real-time capable panoptic segmentation approach using a shared encoder with a semantic and instance decoder. We leverage the geometric information of the LiDAR scan to perform a novel, distance- aware tri-linear upsampling, which allows our approach to use larger output strides than using transpose convolutions leading to substantial savings in computation time. Our experimental evaluation and ablation studies for each module show that combining our geometric and semantic embeddings with our learned, variable instance thresholds, a category-specific loss, and the novel trilinear upsampling module leads to higher panoptic quality. We will release the code of our approach in our LiDAR processing library LiDAR-Bonnetal [27].
Faster Domain Adaptation Networks

It is widely acknowledged that the success of deep learning is built upon large-scale training data and tremendous computing power. However, the data and computing power are not always available for many real-world applications. In this paper, we address the machine learning problem where it lacks training data and limits computing power. Specifically, we investigate domain adaptation which is able to transfer knowledge from one labeled source domain to an unlabeled target domain, so that we do not need much training data from the target domain. At the same time, we consider the situation that the running environment is confined, e.g., in edge computing the end device has very limited running resources. Technically, we present the Faster Domain Adaptation (FDA) protocol and further report two paradigms of FDA: early stopping and amid skipping. The former accelerates domain adaptation by multiple early exit points. The latter speeds up the adaptation by wisely skip several amid neural network blocks. Extensive experiments on standard benchmarks verify that our method is able to achieve the comparable and even better accuracy but employ much less computing resources. To the best of our knowledge, there are very few works which investigated accelerating knowledge adaptation in the community. This work is expected to inspire the topic for more discussion.
Faster Digital Quantum Simulation by Symmetry Protection

Simulating the dynamics of quantum systems is an important application of quantum computers and has seen a variety of implementations on current hardware. We show that by introducing quantum gates implementing unitary transformations generated by the symmetries of the system, one can induce destructive interference between the errors from different steps of the simulation, effectively giving faster quantum simulation by symmetry protection. We derive rigorous bounds on the error of a symmetry-protected simulation algorithm and identify conditions for optimal symmetry protection. In particular, when the symmetry transformations are chosen as powers of a unitary, the error of the algorithm is approximately projected to the so-called quantum Zeno subspaces. We prove a bound on this approximation error, exponentially improving a recent result of Burgarth, Facchi, Gramegna, and Pascazio. We apply the symmetry protection technique to the simulations of the XXZ Heisenberg interactions with local disorder and the Schwinger model in quantum field theory. For both systems, the technique can reduce the simulation error by several orders of magnitude over the unprotected simulation. Finally, we provide numerical evidence suggesting that the technique can also protect simulation against other types of coherent, temporally correlated errors, such as the $1/f$ noise commonly found in solid-state experiments.
Hiding Data Using Efficient Combination of RSA Cryptography, and Compression Steganography Techniques

Data compression is an important part of information security because compressed data is more secure and easy to handle. Effective data compression technology creates efficient, secure, and easy-to-connect data. There are two types of compression algorithm techniques, lossy and lossless. These technologies can be used in any data format such as text, audio, video, or image file. The main objective of this study was to reduce the physical space on the various storage media and reduce the time of sending data over the Internet with a complete guarantee of encrypting this data and hiding it from intruders. Two techniques are implemented, with data loss (Lossy) and without data loss (Lossless). In the proposed paper a hybrid data compression algorithm increases the input data to be encrypted by RSA (Rivest-Shamir-Adleman) cryptography method to enhance the security level and it can be used in executing lossy and lossless compacting Steganography methods. This technique can be used to decrease the amount of every transmitted data aiding fast transmission while using slow internet or take a small space on different storage media. The plain text is compressed by the Huffman coding algorithm, and also the cover image is compressed by Discrete wavelet transform DWT based that compacts the cover image through lossy compression in order to reduce the cover image's dimensions. The least significant bit LSB will then be used to implant the encrypted data in the compacted cover image. We evaluated that system on criteria such as percentage Savings percentage, Compression Time, Compression Ratio, Bits per pixel, Mean Squared Error, Peak Signal to Noise Ratio, Structural Similarity Index, and Compression Speed. This system shows a high-level performance and system methodology compared to other systems that use the same methodology.
UniFuse: Unidirectional Fusion for 360° Panorama Depth Estimation

Learning depth from spherical panoramas is becoming a popular research topic because a panorama has a full field-of-view of the environment and provides a relatively complete description of a scene. However, applying well-studied CNNs for perspective images to the standard representation of spherical panoramas, i.e., the equirectangular projection, is suboptimal, as it becomes distorted towards the poles. Another representation is the cubemap projection, which is distortion-free but discontinued on edges and limited in the field-of-view. This paper introduces a new framework to fuse features from the two projections, unidirectionally feeding the cubemap features to the equirectangular features only at the decoding stage. Unlike the recent bidirectional fusion approach operating at both the encoding and decoding stages, our fusion scheme is much more efficient. Besides, we also designed a more effective fusion module for our fusion scheme. Experiments verify the effectiveness of our proposed fusion strategy and module, and our model achieves state-of-the-art performance on four popular datasets. Additional experiments show that our model also has the advantages of model complexity and generalization capability.The code is available at https://github.com/alibaba/UniFuse-Unidirectional-Fusion.
Diffractive Deep Neural Networks at Visible Wavelengths

Optical deep learning based on diffractive optical elements offers unique advantages for parallel processing, computational speed, and power efficiency. One landmark method is the diffractive deep neural network (D2NN) based on three-dimensional printing technology operated in the terahertz spectral range. Since the terahertz bandwidth involves limited interparticle coupling and material losses, this paper extends D2NN to visible wavelengths. A general theory including a revised formula is proposed to solve any contradictions between wavelength, neuron size, and fabrication limitations. A novel visible light D2NN classifier is used to recognize unchanged targets (handwritten digits ranging from 0 to 9) and targets that have been changed (i.e., targets that have been covered or altered) at a visible wavelength of 632.8 nm. The obtained experimental classification accuracy (84%) and numerical classification accuracy (91.57%) quantify the match between the theoretical design and fabricated system performance. The presented framework can be used to apply a D2NN to various practical applications and design other new applications.
Hand gesture recognition via enhanced densely connected convolutional neural network

Hand gesture recognition (HGR) serves as a fundamental way of communication and interaction for human being. While HGR can be applied in human computer interaction (HCI) to facilitate user interaction, it can also be utilized for bridging the language barrier. For instance, HGR can be utilized to recognize sign language, which is a visual language represented by hand gestures and used by the deaf and mute all over the world as a primary way of communication. Hand-crafted approach for vision-based HGR typically involves multiple stages of specialized processing, such as hand-crafted feature extraction methods, which are usually designed to deal with particular challenges specifically. Hence, the effectiveness of the system and its ability to deal with varied challenges across multiple datasets are heavily reliant on the methods being utilized. In contrast, deep learning approach such as convolutional neural network (CNN), adapts to varied challenges via supervised learning. However, attaining satisfactory generalization on unseen data is not only dependent on the architecture of the CNN, but also dependent on the quantity and variety of the training data. Therefore, a customized network architecture dubbed as enhanced densely connected convolutional neural network (EDenseNet) is proposed for vision-based hand gesture recognition. The modified transition layer in EDenseNet further strengthens feature propagation, by utilizing bottleneck layer to propagate the features being reused to all the feature maps in a bottleneck manner, and the following Conv layer smooths out the unwanted features. Differences between EDenseNet and DenseNet are discerned, and its performance gains are scrutinized in the ablation study. Furthermore, numerous data augmentation techniques are utilized to attenuate the effect of data scarcity, by increasing the quantity of training data, and enriching its variety to further improve generalization. Experiments have been carried out on multiple datasets, namely one NUS hand gesture dataset and two American Sign Language (ASL) datasets. The proposed EDenseNet obtains 98.50% average accuracy without augmented data, and 99.64% average accuracy with augmented data, outperforming other deep learning driven instances in both settings, with and without augmented data.
DBSCAN Clustering Algorithm Based on Density

Clustering technology has important applications in data mining, pattern recognition, machine learning and other fields. However, with the explosive growth of data, traditional clustering algorithm is more and more difficult to meet the needs of big data analysis. How to improve the traditional clustering algorithm and ensure the quality and efficiency of clustering under the background of big data has become an important research topic of artificial intelligence and big data processing. The density-based clustering algorithm can cluster arbitrarily shaped data sets in the case of unknown data distribution. DBSCAN is a classical density-based clustering algorithm, which is widely used for data clustering analysis due to its simple and efficient characteristics. The purpose of this paper is to study DBSCAN clustering algorithm based on density. This paper first introduces the concept of DBSCAN algorithm, and then carries out performance tests on DBSCAN algorithm in three different data sets. By analyzing the experimental results, it can be concluded that DBSCAN algorithm has higher homogeneity and diversity when it performs personalized clustering on data sets of non-uniform density with broad values and gradually sparse forwards. When the DBSCAN algorithm's neighborhood distance eps is 1000, 26 classes are generated after clustering.
Scaling up face masks detection with YOLO on a novel dataset

Face mask detection is a challenging research problem of computer vision because of the small sized area of face mask. The unavailability of proper datasets makes this problem even harder to crack. To address this bottleneck, we propose a novel face masks detection dataset consisting of 52,635 images with more than 50,000 tight bounding boxes and annotations for four different class labels namely, with masks, without masks, masks incorrectly, and mask area, which makes it a novel contribution for variety of face masks classification and detection tasks. Further, this dataset is tested with eight variants of the YOLO algorithm to determine its effectiveness. On the proposed dataset, original YOLO v4 achieved a mAP value of 71.69 % which was highest among all the original YOLO variants, tiny YOLO v4 achieved a mAP value of 57.71 % which was highest among all tiny variants. To propose new face masks detection algorithms that can perform with high accuracy in a limited computational resources environment, we selected four tiny variants of the YOLO algorithm and proposed new architectures modifications in their feature extraction networks that increased the overall performance and specifically, improved mAP by 4.12 % for tiny YOLO v3 and 2.54 % for tiny YOLO v4.
A survey of phishing attack techniques, defence mechanisms and open research challenges

Phishing is an identity theft, which deceives Internet users into revealing their sensitive data, e.g., login information, credit/debit card details, and so on. Researchers have developed various anti-phishing methods in recent years. However, the problem still exists. Therefore, this paper presents a detailed analysis of phishing attack methods and defense techniques. This survey is presented in five folds. First, we discuss in detail the lifecycle of phishing attack, its history, and motivation behind this attack. Second, we present various distribution methods that are used to spread phishing attacks. Third, we provide taxonomy of various phishing-attacking techniques in desktop and mobile environments. Fourth, we provide numerous phishing protection mechanisms and their comparisons. Finally, the article presents various performance challenges faced by developers while dealing with this crucial attack. This paper also provides the consequences of phishing attacks in emerging domains like mobile and online social networks. This paper will help the different users in avoiding phishing attacks while using Internet for their day-to-day activities, and will guide business administrators in designing new effective solutions for their enterprise against various types of phishing threats.
Multiple-image encryption algorithm based on the 3D scrambling model and dynamic DNA coding

Lots of digital images from different fields distributed in Internet are exposed to various risks, such as the content leakage, illegal access and use. To solve the problems of weak security, insufficient encryption capacity and low encryption efficiency, this paper proposes a multiple-image encryption (MIE) algorithm based on the three-dimensional (3D) scrambling model and dynamic DNA coding. Firstly, this paper designs a generalized Zigzag transformation and establishes the 3D scrambling model based on the dimensionality reduction; secondly, multiple plain images are combined into 3D image cube, and the scrambled images can be obtained by the established 3D scrambling model; thirdly, the chaotic sequences are used to perform the dynamic DNA coding and DNA operations on the scrambled images; finally, dynamic DNA decoding operation is performed to obtain the final encryption image. Experimental results and algorithm analyses show that the proposed algorithm has the advantages of large encryption capacity, high encryption efficiency, large key space, high key sensitivity, strong ability to resist the statistical attack, the brute-force attack, the chosen-plaintext attack, etc. Therefore, the proposed algorithm can play a great role in the security of batch image data with high security requirements and large data volume.