Reviewerly/exHarmony huggingface dataset:
A parallel double scrambling encryption scheme for MQIR image based on random combination

With the rapid expansion of quantum information and quantum networks, quantum image security research has gradually become a research hotspot in the field of information security. However, in the field of quantum image processing, most encryption algorithms are independent of color components or position space, and the encryption effect is not satisfactory. Therefore, we propose a parallel double scrambling encryption scheme for a multimode quantum image representation (MQIR) image based on random combination, which uses the principle of random combination to encrypt the position and pixel value of the image at the same time, so as to realize the parallel double scrambling of the pixel value and position of the MQIR image. The algorithm is based on specific keys when encrypting, so that the generation of the keys are related to the seeds. At the same time, a certain number of bits are selected on the qubits representing the three-dimensional positions, and NOT gates are added. This is so that the generated pseudo-random sequence is also related to the number of qubits chosen for the three-dimensional position of the quantum image, making it more difficult to decrypt the encrypted image. We give the quantum circuits with parallel double scrambling and inverse scrambling for MQIR quantum images. In the circuit, we divide the image into four sub-blocks and locate them respectively at the end of each sub-block. The encryption key for different sub-blocks depends on the selected seed. This circuit only needs to encrypt four times without iterating. The algorithm is simple and has a large key space. Finally, we perform the correlation coefficient (CC) and the number of pixels change rate (NPCR) analysis between the plaintext image and the encrypted image. The experimental results show that the CC exhibits low correlation between the plaintext image and the encrypted image, and the NPCR between them is extremely high, which verifies the effectiveness and security of the proposed quantum image encryption scheme.
Sign Language versus Spoken English Language—A Study with Supervised Learning System Using RNN for Sign Language Interpretation

Language is a method of communication. There are different types of spoken language which are having standard rules and grammar and are easy to understand and learn. But, if we talk about understanding, interpretation and learning of sign language, then it is difficult because it is not a regular language and is not taught to everyone as it is used by deaf and mute. Lack of understanding of this knowledge bridges a gap between disabled and normal people. Due to this gap, many talented disabled people cannot explore and showcase their knowledge in front of normal community. In this paper, we are coming up with comparison and grammatical difference between English and ISL. We have also proposed an interpretation system which is using recurrent neural network with LSTM to train the interpretation system.
Neural Network-Based Motion Control Algorithm for Perching Nano-Quadrotor on Outdoor Vertical Surface

In this paper, a nano-quadrotor (40 g, diameter less than 0.10 m) is used for the self-controlled perching task on vertical wall surfaces using an adaptive neural network model. Due to its very light weight, the effect of aerodynamics drag is more severe. The proposed neural control scheme is developed for unmodelled dynamics of the nano-quadrotor. The unmodelled dynamics are estimated using a Chebyshev neural network. This single-layer neural network is adopted for developing the control laws for perching on vertical structures. The Lyapunov theory-based analysis is utilized to derive the update law for weights of the neural network. The proposed control algorithm confirms robustness under unknown dynamics of nano-quadrotor. The validation of system is demonstrated by experiments which shows the effectiveness of proposed control approach in perching application of nano-quadrotor.
A Twig-Based Algorithm for Top-k Subgraph Matching in Large-Scale Graph Data

Subgraph matching aims to find similar substructures in a single graph according to a given query graph and is known as a basic query for graph data management. There exist many categories of subgraph matching solutions. Subgraph isomorphism, which is thought of an NP-complete problem, is an initial solution for the subgraph matching task. To speed up the procedure, graph simulation has been presented to match subgraphs with a polynomial complexity of time. Unfortunately, graph simulation usually loses topologies of matched subgraphs because of its loose restrictions. In this paper, we propose an approximation approach named kSGM (top-k Subraph Graph Matching) for subgraph matching based on twig patterns. First, we transform query graphs into twig patterns and match candidate substructures in graph data. Second, we present an optimized join strategy along with top-k mechanism, including join order selection based on cost evaluation and optimized pruning based on maximum/minimum possible score. Finally, we design experiments on real-life and synthetic graph data to evaluate the performance of our work. The results show that our proposed kSGM obviously reduces the time complexity and guarantee the correctness for answering the queries of subgraph matching compared to existing algorithms.
Vulnerability Detection in Smart Contracts Using Deep Learning

Various decentralized applications have deployed millions of smart contracts (SCs) on the Blockchain networks. SCs enable programmable transactions involving the transfer of monetary assets between peers on a Blockchain network without any need to a central authority. However, similar to any software program, SCs may contain security issues. Software se-curity engineers and researchers have already uncovered several Ethereum BlockChain and SC vulnerabilities. Still, researchers continuously discover many more security flaws in deployed SCs. Indeed, the popularity of SCs attracts adversaries to launch new attack vectors. Thus, efficient vulnerability detection is necessary. This paper lists broad known vulnerabilities in SCs and classifies them based on the multi-class categories such as Suicidal, Prodigal, Greedy, and Normal SCs. The paper adopts artificial recurrent neural network architecture such as Long Short-Term Memory (LSTM) and Temporal Convolutional Network (TCN) used in deep learning to identify and then classify vulnerable Scs.
Human-in-the-loop online multi-agent approach to increase trustworthiness in ML models through trust scores and data augmentation

Increasing a ML model accuracy is not enough, we must also increase its trustworthiness. This is an important step for building resilient AI systems for safety-critical applications such as automotive, finance, and healthcare. For that purpose, we propose a multi-agent system that combines both machine and human agents. In this system, a checker agent calculates a trust score of each instance (which penalizes overconfidence in predictions) using an agreement-based method and ranks it; then an improver agent filters the anomalous instances based on a human rule-based procedure (which is considered safe), gets the human labels, applies geometric data augmentation, and retrains with the augmented data using transfer learning. We evaluate the system on corrupted versions of the MNIST and FashionMNIST datasets. We get an improvement in accuracy and trust score with just few additional labels compared to a baseline approach.
An adaptive optimization method for estimating the number of components in a Gaussian mixture model

Regarding the determination of the number of components ( M ) in a Gaussian mixture model (GMM), this study proposes a novel method for adaptively locating an optimal value of M when using a GMM to fit a given dataset; this method avoids underfitting and overfitting due to an unreasonable manually specified interval. The major contributions of this study are highlighted: (1) An adaptive interval for M (denoted as M ∈ [ M Min A d a , M Max A d a ] ) based on two procedures of a novel method, the modified incremental Gaussian mixture model (MIGMM), is determined via an adjustable parameter β . (2) Considering some typical criteria, the optimal number M within the obtained adaptive interval [ M Min A d a , M Max A d a ] , M O p t A d a , is ultimately determined. Regarding the adaptive interval, extensive experiments with typical synthetic datasets show that [ M M i n A d a , M M a x A d a ] , corresponding to the parameter [ β M i n = 10 − 11 , β M a x = 10 − 2 ] , is determined. The performance of the M O p t A d a determination based on several typical criteria is evaluated on both synthetic and real-world datasets. • The modified incremental Gaussian mixture model (MIGMM) is proposed. • An adaptive interval for M (denoted as M ∈ [ M Min A d a , M Max A d a ] ), based on two procedures of MIGMM, is determined. • Considering some typical criteria, the optimal number M O p t A d a ∈ [ M Min A d a , M Max A d a ] , is ultimately determined. • The performance of the M O p t A d a determination is evaluated on both synthetic and real-world datasets.
Selective Mean Filtering for Reducing Impulse Noise in Digital Color Images

The interest of this paper is in reduction of impulse noise in digital color images. The two main methods used for noise reduction in images are the mean and median filters. These techniques operate by replacing the test pixel in a chosen window by a new filtered pixel value. The window is made to iteratively slide across the entire image to reconstruct a new noise reduced image. The mean filters suffer from the effect of smoothing out color contrast and edges due to leveraging the unrepresentative pixels in the filtering process. The vector median filter and its variants overcome this problem by considering only the most representative pixel in the chosen window. The most representative pixel, i.e. the pixel that is of highest conformity to take the place of the test pixel, is determined by minimizing the aggregate distance from one pixel to every other pixel in the window. The problem in these median filtering approaches is that only one pixel is treated as representative of all the pixels in the chosen window. This conjecture could lead to information loss due to marginalizing other pixels that also are representative of the center pixel. In this paper, we propose a selective mean filtering process to overcome the said problem. The key idea here is to determine the most representative pixels in the window using the method of aggregate distances and then compute the mean of these pixels. This approach will perform better than the vector median filters as now a set of representative pixels are leveraged into the filtering process. Simulation results show that the proposed method performs better than the conventional vector median filtering methods in terms of noise reduction and structural similarity and thus validates the proposed approach. Moreover, the method is tested on real MRI scan images in successfully reducing impulse noise for improved medical diagnosis.
Secret sharing a key in a distributed way, Lagrange vs Newton

In secret sharing, a dealer knows the secret it shares. In a distributed key generation (DKG) protocol, a shared secret is collectively generated in a group in a completely distributed way such that any subset of size greater than a threshold can reveal or use the shared secret, while smaller subsets do not have any knowledge about it. The most important aspect is that there is no dealer or trusted party. The core idea of secret sharing schemes is Shamir's secret sharing method, which uses Lagrange's interpolation to reconstruct the shared secret key. This paper investigates an alternative method, called Newton's interpolation and it cites the probability of implementing it on current DKG protocols.
The right tools for the job: Towards preference and performance considerations in the design of Virtual Reality interactions

Virtual reality (VR) users interact with virtual objects using motion tracked controllers. While many devices utilize abstract button pushes for interactions, some allow for limited finger tracking by estimating finger positions based on sensors. In this study, Vive Wands and Valve Index controllers were compared in three tasks: direct interaction with objects (throwing), tool usage (bow), and indirect control of a character (remote-control). Forty-four participants completed each task with both devices and rated the usability of the device after each task. Results showed differences in preference only for the remote-control task. Some participants noted that using the thumbstick of the Index felt more natural in this task. However, performance did not differ between devices in any task. Future research should not only compare designs of controllers, but also consider assets and interactions, as there may be preference and performance differences for certain combinations.
Empirical Study of RepPoints Representation for Object Detection in Aerial Images

Anchor-based detectors have dominated object detection for several years. These rely heavily on rectangular bounding boxes representation, which is convenient to use but reveals severe limitations, causing the inaccurate location of the objects with dense distribution or arbitrary direction. In this paper, we first utilize a new finer representation of objects, RepPoints (representative points), for improved feature extraction and object localization on aerial traffic images. Then, we experimentally fine-tuned the RPDet – an anchor-free object detector based on RepPoints – to prove that this approach can achieve the same effective performance as the state-of-the-art anchor-based detection methods. Our experimental modifications include the adoption of advanced models such as ResNet50, ResNeXt101 and Res2Net101 as backbone; Besides, we implement modules of DCN (Deformable Convolution Networks) for backbone architecture. To the best of our knowledge, the modified system is the current best performer on the task of object detection with 23.6% in AP and 42.8% in AP50 on the VISDRONE-DET detection benchmark.
Word prediction using closely and moderately related verbs in Down syndrome

People with Down syndrome (DS) have several difficulties in language learning, and one of the areas most affected is language production. Theoretical frameworks argue that prediction depends on the production system. Yet, people with DS can predict upcoming nouns using semantically related verbs. Possibly, prediction skills in people with DS are driven by their associative mechanism rather than by the prediction mechanism based on the production system. This study explores prediction mechanisms in people with DS and their relationship with production skills. Three groups were evaluated in a preferential-looking task: young adults, children with DS, and a typically developing control group paired by sex and mental age. Participants saw two images, a target and a distractor. They also heard a sentence in one of the three conditions: with a verb that was closely related to the object (e.g., "The woman read the book"), with a verb that was moderately related to the object (e.g., "My uncle waited for the bus"), or with a verb that was unrelated to the object (e.g., "My sister threw a broom"). Their productive vocabulary was then measured. In the young adult and typically developing groups, the results showed prediction in sentences with highly and moderately related verbs. Participants with DS, however, showed prediction skills only in the highly related context. There was no influence of chronological age, mental age, or production on prediction skills. These results indicate that people with DS base prediction mainly on associative mechanisms and they have difficulty in generating top-down predictions.
3D morphology reconstruction of rock joints from 2D profile measurement by a profilograph

Rock mass stability is closely related to the shear strength of rock joints. Measuring the joint surface morphology is of vital importance for evaluating the shear strength of rock joints. Although non-contact measurement methods such as laser scanning are advantageous in high precision, the characteristics of high purchase cost or rental fees and being susceptible to the operating environment have weakened their practicability for in-situ batch measurement of the three-dimensional (3D) morphology of joints. To facilitate the possibility of utilizing cost-effective contact-based devices to replace the non-contact ones, we proposed a methodology to reconstruct the 3D morphology of rock joints using two-dimensional (2D) joint profiles measured by contact measurement methods (e.g., a profilograph). First, through a comprehensive comparison of commonly used spatial interpolation methods, the V4 method from the Griddata function is determined as the optimal one for the proposed methodology. Then, based on the optimal spatial interpolation method, the appropriate profile interval is determined through a referential band derived in this study. Finally, the performance of the proposed methodology was validated from experimental and theoretical perspectives. Results show that the relative errors of the test cases are within the engineering permissible error range, and more than 92% of the test cases are associated with an error less than 5%. In addition, the influences of the joint mechanical parameters, joint roughness level, and joint size on the appropriate profile interval are investigated. This methodology offers a cost-effective alternative for in-situ batch measurement of joint 3D morphology in the field survey of rock joints.
Elliptic curve threshold signature scheme for blockchain

Smart contract execution requires the triggering condition. Triggering condition of smart contract is the information outside the blockchain, and thus an oracle is required to provide the data services. For the security of user funds in on-chain aggregation model, each oracle must pay the gas fees to transmit off-chain data to the blockchain and trigger the smart contract, but this way easily causes the network congestion. In view of this, we propose an elliptic curve threshold signature scheme for blockchain (BC-ECTSS) to overcome the above issue. In the random oracle (RO) model, we detailedly show BC-ECTSS satisfies the existential unforgeability against the adaptive chosen-message attacks; in addition, it has the anonymity and its computation cost is relatively low.
ICU Patient Status Prediction Using Markov Chain Model

Intensive care medicine usually involves making quick decisions based on large amounts of information. In making medical decisions, ICU physicians generally rely on personal experience to make subjective evaluations. It is necessary to continuously monitor the parameters related to the admission and health of ICU patients, and it seems necessary to equip each intensive care unit with a special estimation system. This paper develops a Markov chain model to predict patient conditions in the ICU. The dataset we used for this model is pediatric congenital heart disease ICU patients. The state is determined by prior age, weight, CVP, blood pressure, and urine output. We propose a state-based transition probability matrix using these parameters. Experimental results show that deteriorated patients rarely go to an improvement state. This analysis will be helpful in significantly improving the quality of care in the ICU.
Building a Public Domain Voice Database for Odia

Projects like Mozilla Common Voice were born to address the challenges of unavailability of voice data or the high cost of available data for use in speech technology such as Automatic Speech Recognition (ASR) research and application development. The pilot detailed in this paper is about creating a large freely-licensed public repository of transcribed speech in the Odia language as such a repository was not known to be available. The strategy and methodology behind this process are based on the OpenSpeaks project. Licensed under a Public Domain Dedication (CC0 1.0), the repository currently includes audio recordings of pronunciations for more than 55,000 unique words in Odia, including more than 5,600 recordings of words in the northern Odia dialect Baleswari. No known public listing of words in this dialect was found by the author prior to this pilot. This repository is arguably the most extensive transcribed speech corpus in Odia that is also available publicly under any free and open license. This paper details the strategy, approach, and process behind building both the text and the speech corpus using many open source tools such as Lingua Libre, which can be helpful in building text and speech data for different low-medium-resource languages.
A Systematic Literature Review of Virtual and Augmented Reality Applications for Maintenance in Manufacturing

Virtual and augmented reality approaches, which support maintenance workers with up-to-date physical images and easy-to-understand data representations, are gaining importance. These approaches can increase the efficiency and quality of plant maintenance. In this review, based on the literature included in international peer-reviewed journals and conferences, we identify different areas and applications for virtual and augmented reality approaches that support maintenance in an in-dustrial environment. Furthermore, we motivate future research for technologies that are more manageable and better suited to the manufacturing environment.
Short-Term Photovoltaic Power Prediction Based on LGBM-XGBoost

Improving the accuracy of short-term power prediction of photovoltaic power generation systems is of great significance for the safe scheduling and stable operation of power systems. The algorithm in this paper uses the Light Gradient Boosting Machine (LGBM) algorithm in the baseline layer and the Extreme Gradient Boosting (XGBoost) algorithm in the boost layer, thus proposing a double-layer combined prediction algorithm based on LGBM-XGBoost to predict short-term photovoltaic power generation. This paper selects the power data of a photovoltaic power plant for one year as the training set, and predicts the actual power of photovoltaic power in the next week in the test set. Firstly, the importance of the features provided in the training set data is sorted, and the most important feature is discovered. However, the test set does not have this feature. Therefore, the strategy of divisional training is used to predict the most important feature first, and then predict the final actual power. At the same time, in order to avoid the defect of using a single prediction method for two trainings, a double-layer combined prediction method is adopted. The baseline layer uses the LGBM algorithm to predict the missing strong features of the test set. After adding the predicted strong features, establish multiple time zones for feature cross processing to build new features. Based on feature engineering, the boost layer uses the XGBoost algorithm to predict the photovoltaic power. Compared with using a single algorithm, the double-layer combined prediction algorithm effectively improves the accuracy of short-term prediction of photovoltaic power and meets the requirements of short-term prediction of photovoltaic power systems.