Reviewerly/exHarmony huggingface dataset:
Real Time Object Detection using CNN based Single Shot Detector Model

Object Detection has been one of the areas of interest of research community for over years and has made significant advances in its journey so far. There is a tremendous scope in the applications that would benefit with more innovations in the domain of object detection. Rapid growth in the field of machine learning has complemented the efforts in this area and in the recent times, research community has contributed a lot in real time object detection. In the current work, authors have implemented real time object detection and have made efforts to improve the accuracy of the detection mechanism. In the current research, we have used ssd_v2_inception_coco model as Single Shot Detection models deliver significantly better results. A dataset of more than 100 raw images is used for training and then xml files are generated using labellimg. Tensor flow records generated are passed through training pipelines using the proposed model. OpenCV captures real-time images and CNN performs convolution operations on images. The real time object detection delivers an accuracy of 92.7%, which is an improvement over some of the existing models already proposed earlier. Model detects hundreds of objects simultaneously. In the proposed model, accuracy of object detection significantly improvises over existing methodologies in practice. There is a substantial dataset to evaluate the accuracy of proposed model. The model may be readily useful for object detection applications including parking lots, human identification, and inventory management.
Scaling Local Self-Attention for Parameter Efficient Visual Backbones

Self-attention has the promise of improving computer vision systems due to parameter-independent scaling of receptive fields and content-dependent interactions, in contrast to parameter-dependent scaling and content-independent interactions of convolutions. Self-attention models have recently been shown to have encouraging improvements on accuracy-parameter trade-offs compared to baseline convolutional models such as ResNet-50. In this work, we aim to develop self-attention models that can outperform not just the canonical baseline models, but even the high-performing convolutional models. We propose two extensions to self-attention that, in conjunction with a more efficient implementation of self-attention, improve the speed, memory usage, and accuracy of these models. We leverage these improvements to develop a new self-attention model family, HaloNets, which reach state-of-the-art accuracies on the parameter-limited setting of the ImageNet classification benchmark. In preliminary transfer learning experiments, we find that HaloNet models outperform much larger models and have better inference performance. On harder tasks such as object detection and instance segmentation, our simple local self-attention and convolutional hybrids show improvements over very strong baselines. These results mark another step in demonstrating the efficacy of self-attention models on settings traditionally dominated by convolutional models.
ABSAC: Attribute-Based Access Control Model Supporting Anonymous Access for Smart Cities

Smart cities require new access control models for Internet of Things (IoT) devices that preserve user privacy while guaranteeing scalability and efficiency. Researchers believe that anonymous access can protect the private information even if the private information is not stored in authorization organization. Many attribute-based access control (ABAC) models that support anonymous access expose the attributes of the subject to the authorization organization during the authorization process, which allows the authorization organization to obtain the attributes of the subject and infer the identity of the subject. The ABAC with anonymous access proposed in this paper called ABSAC strengthens the identity-less of ABAC by combining homomorphic attribute-based signatures (HABSs) which does not send the subject attributes to the authorization organization, reducing the risk of subject identity re-identification. It is a secure anonymous access framework. Tests show that the performance of ABSAC implementation is similar to ABAC’s performance.
THE UTILIZATION OF LOOSE PARTS MEDIA IN STEAM LEARNING FOR EARLY CHILDHOOD

STEAM-based learning (Science, Technology, Engineering, Arts, and Mathematics) has begun to be developed a lot recently. This learning is considered able to answer the challenges of the times to be able to develop learning in the 21st century. STEAM is not only developed for secondary to tertiary education but is also developed for learning in early childhood. The challenges for developing STEAM-based learning are not small. The teacher considers that to provide learning about technology, for example, one must use sophisticated computer-based learning media, so it requires expensive costs. Also, science and engineering learning has not been developed much in kindergarten (TK) learning because it is considered a difficult concept to understand and requires high costs for the learning media. Learning media for early childhood is diverse and does not have to all buy new materials at a high cost. Likewise with learning media that can be used to develop STEAM-based learning. Teachers can make use of items or materials that are easily available, such as rocks, tree branches, seeds, used bottle caps, dry leaves, clothespins, etc. These removable materials are called loose parts. Loose parts are loose objects that can be moved, manipulated, and how to use them is determined by the child. These removable materials as learning media can make children think creatively and imaginatively. Loose parts can also be used as media in STEAM learning.
Towards Incorporating Human Knowledge in Fuzzy Pattern Tree Evolution

This paper shows empirically that Fuzzy Pattern Trees (FPT) evolved using Grammatical Evolution (GE), a system we call FGE, meet the criteria to be considered a robust Explainable Artificial Intelligence (XAI) system. Experimental results show FGE achieves competitive results against state of the art black box methods on a set of real world benchmark problems. Various selection methods were investigated to see which was best for finding smaller, more interpretable models and a human expert was recruited to test the interpretability of the models found and to give a confidence score for each model. Models which were deemed interpretable but not trustworthy by the expert were seen to be outperformed in classification accuracy by interpretable models which were judge trustworthy, validating that FGE can be a powerful XAI technique.
The Future of Distance Learning as Perceived by Faculty Members

A questionnaire survey of 33,987 faculty members in April 2020 revealed predominantly negative evaluations of learning effectiveness in the context of distance learning, as compared to in-person instruction, and a rather pessimistic vision of the future of higher education in Russia. This article formulates hypotheses about the reasons of such attitudes among faculty members and provides arguments for possible opinion shifts and alleviation of resistance to technological change. The paramount problem consists in the contradiction between the acceptance of the idea and value of digital innovation and the rejection of such at the level of personal teaching practices. During expert interviews, faculty members were offered alternative scenarios of the future. The data obtained was then used to analyze the attitudes and beliefs behind their resistance and to find out which perceptions of the future correlated with negative and positive evaluations of distance learning.
Entrepreneurial Leadership and Its Impact on Strategic Ambidexterity

This study aims to know the impact of entrepreneurial leadership on strategic ambidexterity through the mediating role of knowledge sharing in Tala Abu-Ghazaleh International Group. It also aims to know the concepts of entrepreneurial leadership, strategic ambidexterity, and knowledge sharing. In order to achieve the study objectives, the analytical descriptive method is used. The survey method has also been used for sample selection by distributing the questionnaire electronically to all members of the study population around the world, composed of all managers of top and middle departments in Talal Abu-Ghazaleh International Group, with a total of 170 managers, where 142 questionnaires were returned, which are good enough for analysis since they constitute 83.5% of all distributed questionnaires. The study results show an increase in the relative significance of entrepreneurial leadership, strategic ambidexterity, knowledge sharing, and an impact of entrepreneurial leadership on strategic ambidexterity in the presence of knowledge sharing in Tala Abu-Ghazaleh International Group.
Trusted Node QKD at an Electrical Utility

Challenges facing the deployment of quantum key distribution (QKD) systems in critical infrastructure protection applications include the optical loss-key rate tradeoff, addition of network clients, and interoperability of vendor-specific QKD hardware. Here, we address these challenges and present results from a recent field demonstration of three QKD systems on a real-world electric utility optical fiber network.
Wearable Air-Writing Recognition System employing Dynamic Time Warping

Gesture recognition has been a popular research field under the trend of IoT and intelligent devices. Air-writing is the most challenging and crucial topic in the gesture recognition field. In this paper, we propose a wearable air-writing system that makes users can write the English alphabet in the three-dimensional space without any write rules. The proposed system is based on the Inertial Measurement Unit (IMU), and it uses dynamic time warping (DTW) as the main recognition algorithm. In addition, to improve the recognition accuracy and take a better advantage of the DTW algorithm, we present an adjustment system that gives some new optimization methods to the application of IMU and DTW. In the experiment, the accuracy of recognition is 84.6% for the uppercase alphabet (from `A' to `Z') in user-dependent case. And we also confirmed that the recognition method only based on the DTW algorithm is one kind of user-dependent methods, which means this method is heavily dependent on personalization.
Tree parity machine guided patients’ privileged based secure sharing of electronic medical record: cybersecurity for telehealth during COVID-19

In the COVID-19 pandemic, telehealth plays a significant role in the e-healthcare. E-health security risks have also risen significantly with the rise in the use of telehealth. This paper addresses one of e-health's key concerns, namely security. Secret sharing is a cryptographic method to ensure reliable and secure access to information. To eliminate the constraint that in the existing secret sharing schemes, this paper presents Tree Parity Machine (TPM) guided patients' privileged based secure sharing. This is a new secret sharing technique that generates the shares using a simple mask based operation. This work considers addressing the challenges presents in the original secret sharing scheme. This proposed technique enhances the security of the existing scheme. This research introduces a concept of privileged share in which among k number of shares one share should come from a specific recipient (patient) to whom a special privilege is given to recreate the original information. In the absence of this privileged share, the original information cannot be reconstructed. This technique also offers TPM based exchange of secret shares to prevent Man-In-The-Middle-Attack (MITM). Here, two neural networks receive common inputs and exchange their outputs. In some steps, it leads to full synchronization by setting the discrete weights according to the specific rule of learning. This synchronized weight is used as a common secret session key for transmitting the secret shares. The proposed method has been found to produce attractive results that show that the scheme achieves a great degree of protection, reliability, and efficiency and also comparable to the existing secret sharing scheme.
Stabilization of Multimode Schrödinger Cat States Via Normal-Mode Dissipation Engineering

Non-Gaussian quantum states have been deterministically prepared and autonomously stabilized in single- and two-mode circuit quantum electrodynamics architectures via engineered dissipation. However, it is currently unknown how to scale up this technique to multi-mode non-Gaussian systems. Here, we upgrade dissipation engineering to collective (normal) modes of nonlinear resonator arrays and show how to stabilize multi-mode Schrodinger cat states. These states are multi-photon and multi-mode quantum superpositions of coherent states in a single normal mode delocalized over an arbitrary number of cavities. We consider tailored dissipative coupling between resonators that are parametrically driven and feature an on-site nonlinearity, which is either a Kerr-type nonlinearity or an engineered two-photon loss. For both types of nonlinearity, we find the same exact closed-form solutions for the two-dimensional steady-state manifold spanned by superpositions of multi-mode Schrodinger cat states. We further show that, in the Zeno limit of strong dissipative coupling, the even parity multi-mode cat state can be deterministically prepared from the vacuum. Remarkably, engineered two-photon loss gives rise to a fast relaxation towards the steady state, protecting the state preparation against decoherence due to intrinsic single-photon losses and imperfections in tailored dissipative coupling, which sets in at longer times. The relaxation time is independent of system size making the state preparation scalable. Multi-mode cat states are naturally endowed with a noise bias that increases exponentially with system size and can thus be exploited for enhanced robust encoding of quantum information.
Automatic modulation classification using different neural network and PCA combinations

This paper highlights one of the most promising research directions for automatic modulation recognition algorithms, although it does not provide a final solution. We study the design of a high-precision classifier for recognizing PSK, QAM and DVB-S2 APSK modulation signals. First, an efficient pattern recognition model that includes three main modules for feature extraction, feature optimization and classification is presented. The feature extraction module extracts the most useful combinations of up to six high-order cumulants that embed sixth-order moments and uses logarithmic function properties to improve the distribution curve of the six-order cumulants. To the best of our knowledge, this is the first time that these combinations and the improved feature criteria have been applied in this area. The optimizer module selects optimal features via principal component analysis (PCA). Then, in the classifier module, we study two important supervised neural network classifiers (i.e., multilayer perceptron (MLP)- and radial basis function (RBF)-based classifiers). Through an experiment, we determine the best classifier for recognizing the considered modulations. Then, we propose an RBF-PCA combined recognition system in which an optimization module is added to enhance the overall classifier performance. This module optimizes the classifier performance by searching for the best subset of features to use as the classifier input. The simulation results illustrate that the RBF-PCA classifier combination achieves high recognition accuracy even at a low signal-to-noise ratio (SNR) and with limited training samples.
Local Path Planning Method Based on Virtual Manipulators and Dynamic Window Approach for a Wheeled Mobile Robot

This paper proposes a local path planning method based on virtual manipulators and dynamic window approach (VMDWA) for a wheeled mobile robot. In the conventional researches such as the dynamic window approach (DWA), the mobile robot follows the desired path and avoids obstacles in the dynamic and static environment. However, DWA calculates the predicted path assuming the constant velocities. From these predicted paths, the best path is selected using the cost function. Since the DWA assumes the constant value of velocities, multiple candidates of paths cannot generate flexible movements. Therefore, it may not be possible to generate the path that does not collide with obstacles. To solve these problems, this paper proposes the local path planning method based on virtual manipulators and dynamic window approach (VMDWA). In the proposed method, virtual manipulators are taken into account at the calculation of the path planning based on DWA. Therefore, it is possible to use the variable velocities values for the predictive paths. VMDWA selects the optimal path from the several calculations of predictive paths. The simulation results confirmed the effectiveness of the VMDWA.
Global Syntactic Variation in Seven Languages: Toward a Computational Dialectology

The goal of this paper is to provide a complete representation of regional linguistic variation on a global scale. To this end, the paper focuses on removing three constraints that have previously limited work within dialectology/dialectometry. First, rather than assuming a fixed and incomplete set of variants, we use Computational Construction Grammar to provide a replicable and falsifiable set of syntactic features. Second, rather than assuming a specific area of interest, we use global language mapping based on web-crawled and social media datasets to determine the selection of national varieties. Third, rather than looking at a single language in isolation, we model seven major languages together using the same methods: Arabic, English, French, German, Portuguese, Russian, and Spanish. Results show that models for each language are able to robustly predict the region-of-origin of held-out samples better using Construction Grammars than using simpler syntactic features. These global-scale experiments are used to argue that new methods in computational sociolinguistics are able to provide more generalized models of regional variation that are essential for understanding language variation and change at scale.
Blockchain Vulnerabilities in Practice

Blockchains are not invulnerable. There are known vulnerabilities in various blockchain ecosystem components. This field note describes some vulnerabilities observed in smart contracts and node software, their exploitation, and how to avoid them, with a focus on the Ethereum ecosystem.
Applying TS-DBN model into sports behavior recognition with deep learning approach

The purposes are to automatically collect information about human sports behavior from massive video data and provide an explicit recognition and analysis of body movements. The analysis of multi-scale input data, the improvement of spatiotemporal Deep Belief Network (DBN), and the different pooling strategies are regarded as the focuses to improve the belief networks in deep learning (DL). Moreover, a human sports behavior recognition model is proposed based on particular spatio-temporal features. Also, video frame data are collected from the Royal Institute of Technology (KTH) and University of Central Florida (UCF) datasets for training. The TensorFlow platform is employed to simulate the built algorithm. Finally, the constructed algorithm model is compared with the DBN proposed by Yang et al. the Convolutional Neural Network (CNN) proposed by Ullah et al. and the DBN-Hidden Markov Model (HMM) algorithm proposed by Xu et al. to analyse its performance. The recognition effects of each algorithm in the two datasets are analyzed. Results demonstrate that CNN developed by Ullah et al. has the worst accuracy on the KTH and UCF datasets, followed by DBN developed by Yang et al. and DBN-HMM developed by Xu et al. The constructed algorithm model can provide the highest accuracy, reaching about 90%, and the recognition accuracy of human sports behaviors of each algorithm on the KTH dataset is lower than that on the UCF dataset. On the KTH dataset, the recognition accuracy for boxing is the highest and running the lowest. Analyzing the model's accuracy in the four scenes (S1, S2, S3, and S4) on the KTH dataset suggests that the recognition accuracy for the indoor scene (S4) is higher than that of the outdoor scenes (S1, S2, and S3). On the UCF dataset, the recognition accuracy for lifting is the highest, reaching 99%, and that for walking is the lowest, reaching 51%. Therefore, the proposed human sports recognition model can provide a higher accuracy than other classic DL algorithms, providing an experimental basis for subsequent human sports recognition research.
Resistance of SNOW-V against Fast Correlation Attacks

SNOW-V is a new member in the SNOW family of stream ciphers, hoping to be competitive in the 5G mobile communication system. In this paper, we study the resistance of SNOW-V against bitwise fast correlation attacks by constructing bitwise linear approximations. First, we propose and summarize some efficient algorithms using the slice-like techniques to compute the bitwise linear approximations of certain types of composition functions composed of basic operations like ⊞, ⊕, Permutation, and S-box, which have been widely used in word-oriented stream ciphers such as SNOW-like ciphers. Then, using these algorithms, we find a number of stronger linear approximations for the FSM of the two variants of SNOW-V given in the design document, i.e., SNOW-V σ0 and SNOW-V⊞8, ⊞8. For SNOW-V σ0, where there is no byte-wise permutation, we find some bitwise linear approximations of the FSM with the SEI (Squared Euclidean Imbalance) around 2−37.34 and mount a bitwise fast correlation attack with the time complexity 2251.93 and memory complexity 2244, given 2103.83 keystream outputs, which improves greatly the results in the design document. For SNOW-V⊞8, ⊞8, where both of the two 32-bit adders in the FSM are replaced by 8-bit adders, we find our best bitwise linear approximations of the FSM with the SEI 2−174.14, while the best byte-wise linear approximation in the design document of SNOW-V has the SEI 2−214.80. Finally, we study the security of a closer variant of SNOW-V, denoted by SNOW-V⊞32, ⊞8, where only the 32-bit adder used for updating the first register is replaced by the 8-bit adder, while everything else remains identical. For SNOW-V⊞32, ⊞8, we derive many mask tuples yielding the bitwise linear approximations of the FSM with the SEI larger than 2−184. Using these linear approximations, we mount a fast correlation attack with the time complexity 2377.01 and a memory complexity 2363, given 2253.73 keystream outputs. Note that neither of our attack threatens the security of SNOW-V. We hope our research could further help in understanding bitwise linear approximation attacks and also the structure of SNOW-like stream ciphers.
Nested Session Types

Abstract Session types statically describe communication protocols between concurrent message-passing processes. Unfortunately, parametric polymorphism even in its restricted prenex form is not fully understood in the context of session types. In this paper, we present the metatheory of session types extended with prenex polymorphism and, as a result, nested recursive datatypes. Remarkably, we prove that type equality is decidable by exhibiting a reduction to trace equivalence of deterministic first-order grammars. Recognizing the high theoretical complexity of the latter, we also propose a novel type equality algorithm and prove its soundness. We observe that the algorithm is surprisingly efficient and, despite its incompleteness, sufficient for all our examples. We have implemented our ideas by extending the Rast programming language with nested session types. We conclude with several examples illustrating the expressivity of our enhanced type system.