Reviewerly/exHarmony huggingface dataset:
Human Motion Recognition by Three-view Kinect Sensors in Virtual Basketball Training

In recent years, human action recognition has received a considerable amount of research attention because of it's potential in a variety of applications, such as video surveillance, human-computer interaction, and virtual reality (VR). However, many researches on human action recognition performed in single-camera or double-camera system, which achieve reduced performance due to vulnerability to partial occlusion and miss-recognition of back. Some works on human action recognition use multiple cameras but are too complex for practical application. In this paper, we propose a new human action recognition system using triple Kinect sensors for VR application. Particularly, we design a mark detection method to determine the front of user and fusion skeleton data in real time. Features are extracted from three-dimensional (3D) skeleton data sequences, and divided into five parts according to body parts. A classification model based on the part-aware long short-term memory networks is proposed to recognize human motion. Finally, we demonstrate the system with a virtual reality basketball application and the results of experiment validate the feasibility of the proposed system.
Eavesdropping and countermeasures for backflash side channel in fiber polarization-coded quantum key distribution

Nowadays, the practical security of quantum key distribution (QKD) is the biggest challenge. In practical implementation, the security of a practical system strongly depends on its device implementation, and device defects will create security holes. The information leakage from a receiving unit due to secondary photon emission (backflash) is caused by a single-photon detector in the avalanche process. Now studies have shown that the backflash will leak the information about time and polarization and the eavesdropping behavior will not generate additional error rate in the communication process. An eavesdropping scheme obtaining time information by using backflash is proposed. Targeting this security hole for backflash leaking polarization information, an eavesdropping scheme for obtaining polarization information by using backflash is proposed in free-space QKD; however, it has not been reported in fiber QKD. In this study, the eavesdropping scheme and countermeasures for obtaining information by using backflash in fiber polarization-coded QKD is proposed. Since the polarization state of the fiber polarization-coded QKD system is easy to change, the scheme is proposed based on the time-division multiplexing polarization compensation fiber polarization-coded QKD system. In theory, the eavesdropper in this scheme obtaining the key information by using the backflash is theoretically deduced, and corrects the polarization change of the backflash by time-division multiplexing polarization compensation method, thus obtaining the accurate polarization information. The probability of backflash in the fiber polarization-coded QKD is measured to be 0.05, and the information leakage in the proposed eavesdropping scheme is quantified. The lower limit of the information obtained by the eavesdropper is 2.5 × 10&lt;sup&gt;–4&lt;/sup&gt;. Due to the fact that the polarization compensation process increases invalid information in actual operation, the information obtained by the eavesdropper will be further reduced, thus obtaining the lower limit of information leakage. The results show that the backflash leaks a small amount of key information in a time-multiplexed polarization-compensated fiber polarization-coded QKD system. The wavelength characteristics of the backflash can be utilized to take corresponding defense methods. Backflash has a wide spectral range, and the count of backflash has a peak wavelength. So, tunable filters and isolators can be used to reduce backflash leakage, thereby reducing the information leakage.
An Ensemble of Bacterial Foraging, Genetic, Ant Colony and Particle Swarm Approach EB-GAP: A Load Balancing Approach in Cloud Computing

Background: In the cloud environment, satisfaction of service level agreement (SLA) is the prime objective. It can be achieved by providing services in minimum time in an efficient manner at the lowest cost by efficiently utilizing the resources. This will create a win-win situation for both consumer and service provider. Through literature analysis, it has been found that the procedure of resource optimization is quite costly and time-consuming. Objective: The research aim is to design and develop an efficient load-balancing technique for the satisfaction of service level agreement and the utilization of resources in an efficient manner. Methods: To achieve this, authors have proposed a new load-balancing algorithm named eB-GAP by picking the best features from Bacterial Foraging, Genetic, Particle-Swarm, and Ant-Colony algorithm. Based on the availability of resources and load on a virtual machine, a fitness value is assigned to all virtual machines. Results: A newly arrived task is mapped with the fittest virtual machine. Whenever a new task is mapped or left the system, the fitness value of the virtual machine is updated. In this manner, the system achieves the satisfaction of service level agreement, the balance of the load, and efficient utilization of resources. To test the proposed approach, the authors have used the real-time cloud environment of amazon web service. In this, waiting time, completion time, execution time, throughput, and cost have been computed in a real-time environment.
NUT-RC: Noisy User-generated Text-oriented Reading Comprehension

Reading comprehension (RC) on social media such as Twitter is a critical and challenging task due to its noisy, informal, but informative nature. Most existing RC models are developed on formal datasets such as news articles and Wikipedia documents, which severely limit their performances when directly applied to the noisy and informal texts in social media. Moreover, these models only focus on a certain type of RC, extractive or generative, but ignore the integration of them. To well address these challenges, we come up with a noisy user-generated text-oriented RC model. In particular, we first introduce a set of text normalizers to transform the noisy and informal texts to the formal ones. Then, we integrate the extractive and the generative RC model by a multi-task learning mechanism and an answer selection module. Experimental results on TweetQA demonstrate that our NUT-RC model significantly outperforms the state-of-the-art social media-oriented RC models.
Fundamental assumptions as predictors of psychological hardiness of students with disabilities

&#x0D; The article describes the psychological study determining the factors that support fundamental assumptions of students with disabilities. We have determined correlations between psychological hardiness of students with disabilities and their system of fundamental assumptions; beliefs in the benevolence of the surrounding world and people, in the meaningfulness of some events, in a chance to build successfully one’s own life and in one’s own luck have a significant influence on psychological hardiness. The predictors of psychological hardiness of students with disabilities are: positive interactions with the outside world; self-acceptance as a conscious acceptance of various aspects of one’s own personality; personal autonomy, which allows an individual to independently determine their own behaviour, assess adequately their capabilities and resist external influences.&#x0D;
Densely Connecting Depth Maps for Monocular Depth Estimation

Predicting depth map from a single RGB image is beneficial for many three-dimensional applications. Although recent monocular depth estimation methods have achieved impressive accuracy, the preference on high-level features or low-level features prevents them from balancing sharpness and fidelity of depth maps. In this work, we propose a dense connection mechanism that connects diverse sub-depth maps produced by the sub-predictors to the final depth map to contribute information from features at different levels. Besides, two kinds of diversity enhancement devices are proposed to increase the number and diversity of the sub-depth maps collected by the dense connection mechanism. Experimental results on KITTI and NYU Depth V2 datasets shows that, by fusing the dense connection mechanism and diversity enhancement devices, our proposed method achieves state-of-the-art accuracy and predicts sharp depth maps that restore reliable object structures.
Exploring Half-Duplex Communication of NFC Read/Write Mode for Secure Multi-Factor Authentication

Near Field Communication (NFC) is a prominent short-range, contact-less communication technology, which is rapidly getting popular in modern smart devices. For communication between two active devices via NFC, applications generally choose the peer-to-peer operation mode. In this paper, we exploit the possibility of using the NFC read/write mode, designed primarily for unidirectional data transfer from an active NFC reader to a passive NFC tag, for bi-directional half-duplex communication between two active NFC devices. The advantages of using the NFC read/write mode include low protocol overhead and permitting different data formats. However, the challenges are avoiding the reader collision problem, maintaining a secure session, and completing all transactions in an acceptable time frame. In this paper, we address the above challenges and propose a methodology for efficient communication between active NFC devices using NFC read/write mode. To evaluate the scheme, we design a secure Multi-Factor Authentication (MFA) system that requires bi-directional communication for mutually authenticating two NFC devices. The proposed methodology is experimentally verified using NFC-enabled Android smartphones and a Kerberos server as the third-party authenticator.
Numeric estimation of resource requirements for a practical polarization-frame alignment scheme for quantum key distribution (QKD)

Due to physical orientations and birefringence effects, practical quantum information protocols utilizing optical polarization need to handle misalignment between preparation and measurement reference frames. For any such capable system, an important question is how many resources -- e.g., measured single photons -- are needed to reliably achieve alignment precision sufficient for the desired quantum protocol. Here we study the performance of a polarization-frame alignment scheme used in prior laboratory and field quantum key distribution (QKD) experiments by performing Monte Carlo numerical simulations. The scheme utilizes, to the extent possible, the same single-photon-level signals and measurements as for the QKD protocol being supported. Even with detector noise and imperfect sources, our analysis shows that only a small fraction of resources from the overall signal -- a few hundred photon detections, in total -- are required for good performance, restoring the state to better than 99% of its original quality.
Non-locality and entanglement in multi-qubit systems from a unified framework

Non-classical probability is the underlying feature of quantum mechanics. The emergence of Bell-CHSH non-locality [We stress that CHSH inequality (Clauser et al. in Phys. Rev. Lett. 23(15):880, 1969) is more general and reduces to Bell inequality (Bell in Physics 1:195, 1964) under the assumption of perfect correlations between the observables] for bipartite systems and linear entanglement inequalities for two-qubit systems has been shown in Adhikary et al. (Eur Phys J D 74:68, 2020), purely as violations of classical probability rules. In this paper, we improve upon that work by showing that violation of any nonlocality inequality implies violation of classical probability rules, manifested through negative probabilities, without recourse to any underlying theory. Moving on to entanglement, we employ parent pseudoprojections to show how any number of linear and nonlinear entanglement witnesses for multiqubit systems can be obtained as violations of classical probability rules. They include the ones that have been derived earlier by employing different methods. It provides a perspective complementary to the current understanding in terms of the algebraic approaches.
Energy-Based Dropout in Restricted Boltzmann Machines: Why Not Go Random

Deep learning architectures have been widely fostered throughout the last years, being used in a wide range of applications, such as object recognition, image reconstruction, and signal processing.Nevertheless, such models suffer from a common problem known as overfitting, which limits the network from predicting unseen data effectively.Regularization approaches arise in an attempt to address such a shortcoming.Among them, one can refer to the well-known Dropout, which tackles the problem by randomly shutting down a set of neurons and their connections according to a certain probability.Therefore, this approach does not consider any additional knowledge to decide which units should be disconnected.In this paper, we propose an energy-based Dropout (E-Dropout) that makes conscious decisions whether a neuron should be dropped or not.Specifically, we design this regularization method by correlating neurons and the model's energy as an importance level for further applying it to energy-based models, such as Restricted Boltzmann Machines (RBMs).The experimental results over several benchmark datasets revealed the proposed approach's suitability compared to the traditional Dropout and the standard RBMs.
SPEECH RECOGNITION BY IMPROVING THE PERFORMANCE OF ALGORITHMS USED IN DISCRIMINATION

Speech recognition techniques are one of the most important modern technologies. Many different systems have been developed in terms of methods used in the extraction of features and methods of classification. Voice recognition includes two areas: speech recognition and speaker recognition, where the research is confined to the field of speech recognition. The research presents a proposal to improve the performance of single word recognition systems by an algorithm that combines more than one of the techniques used in character extraction and modulation of the neural network to study the effects of recognition science and study the effect of noise on the proposed system. In this research four systems of speech recognition were studied, the first system adopted the MFCC algorithm to extract the features. The second system adopted the PLP algorithm, while the third system was based on combining the two previous algorithms in addition to the zero-passing rate. In the fourth system, the neural network used in the differentiation process was modified and the error ratio was determined. The impact of noise on these previous systems. The outcomes were looked at regarding the rate of recognizable proof and the season of preparing the neural network for every system independently, to get a rate of distinguishing proof and quiet up to 98% utilizing the proposed framework.
Circuit design for clique problem and its implementation on quantum computer

Finding cliques in a graph has several applications for its pattern matching ability. $k$-clique problem, a special case of clique problem, determines whether an arbitrary graph contains a clique of size $k$, has already been addressed in quantum domain. A variant of $k$-clique problem that lists all cliques of size $k$, has also popular modern-day applications. Albeit, the implementation of such variant of $k$-clique problem in quantum setting still remains untouched. In this paper, apart from theoretical solution of such $k$-clique problem, practical quantum gate-based implementation has been addressed using Grover's algorithm. This approach is further extended to design circuit for the maximum clique problem in classical-quantum hybrid architecture. The algorithm automatically generates the circuit for any given undirected and unweighted graph and any given $k$, which makes our approach generalized in nature. The proposed approach of solving $k$-clique problem has exhibited a reduction of qubit cost and circuit depth as compared to the state-of-the-art approach, for a small $k$ with respect to a large graph. A framework that can map the automated generated circuit for clique problem to quantum devices is also proposed. An analysis of the experimental results is demonstrated using IBM's Qiskit.
PREGAN: Pose Randomization and Estimation for Weakly Paired Image Style Translation

Utilizing the trained model under different conditions without data annotation is attractive for robot applications. Towards this goal, one class of methods is to translate the image style from another environment to the one on which models are trained. In this letter, we propose a weakly-paired setting for the style translation, where the content in the two images is aligned with errors in poses. These images could be acquired by different sensors in different conditions that share an overlapping region, e.g., with LiDAR or stereo cameras, from sunny days or foggy nights. We consider this setting to be more practical with: (i) easier labeling than the paired data; (ii) better interpretability and detail retrieval than the unpaired data. To translate across such images, we propose PREGAN to train a style translator by intentionally transforming the two images with a random pose, and to estimate the given random pose by differentiable non-trainable pose estimator given that the more aligned in style, the better the estimated result is. Such adversarial training enforces the network to learn the style translation, avoiding being entangled with other variations. Finally, PREGAN is validated on both simulated and real-world collected data to show the effectiveness. Results on down-stream tasks, classification, road segmentation, object detection, and feature matching show its potential for real applications. https://github.com/wrld/PRoGAN.
A2R2: Robust Unsupervised Neural Machine Translation With Adversarial Attack and Regularization on Representations

Unsupervised neural machine translation (UNMT) has recently achieved significant progress without requirement on any parallel data. The models for UNMT are typically the sequence-to-sequence architecture with an encoder to map sentences in different languages to a shared latent space, and a decoder to generate their corresponding translation. Denoising autoencoding and back-translation are called in every iteration for the models to learn the relationship of sentence pairs in languages or between languages. However, sentences generated by the noise model of autoencoding or the reverse model of back-translation are normally different from those written by humans, which may cause inference bias. In this paper, we propose a regularization method for back-translation to explicitly draw representations of sentence pairs closer in the shared space. To enhance the robustness to sentences after autoencoding or back-translation, the adversarial attack on representations is applied. Experiments on unsupervised English↔French, English↔German and English↔Romanian benchmarks show that our approach outperforms the cross-lingual language model (XLM) baseline by 0.4~1.8 BLEU scores. Additionally, the boost on noisy test sets in most translation directions is over 5 BLEU scores.
Analysis of student’s mental model through representation chemistry textbooks based on augmented reality

Abstract The mental reconstruction model of students is essential to see the progress and difficulties of students in understanding chemical material, especially at the level of macroscopic, submicroscopic, and symbolic representations. This study aims 1) to see how the influence of Augmented Reality (AR) based learning media with the concept of representation, 2) to see the validity, practicality and effectiveness of the development of augmented reality-based representational chemistry textbooks, and 3) to analyze the mental models of students. The research method used is a literature study (literature study) with qualitative and quantitative approaches. The data collection technique used is the non-participant observation technique by reviewing and examining various research results related to AR-based learning media and the concept of chemical representation. The results of the study with the context of literature studies showed that AR-based learning media (teaching materials) with the concept of representation could increase the reasoning power and understanding of students in learning chemistry. The impact students can interpret more deeply so that their mental models can be reconstructed into scientific concepts. The conclusion that can be taken was that learning chemistry with the idea of representation through teaching materials based on Augmented Reality (AR) could be used as the primary reference in learning chemistry. Besides, the concept also changes the mindset and mental attitude of students as the generation of the Indonesian Nation, where improvements in mood and mentality from an early age have the potential to foster the readiness and resistance of students. That is, the application of this idea shows that natural science (chemistry) has a very vital role in the progress of the Nation, especially towards high-quality education according to the SDGs indicators.
Complementary Convolution Residual Networks for Semantic Segmentation in Street Scenes with Deep Gaussian CRF

To understand surrounding scenes accurately, the semantic segmentation of images is vital in autonomous driving tasks, such as navigation, and route planning. Currently, convolutional neural networks (CNN) are widely employed in semantic segmentation to perform precise prediction in the dense pixel level. A recent trend in network design is the stacking of small convolution kernels. In this work, small convolution kernels (3 × 3) are decomposed into complementary convolution kernels (1 × 3 + 3 × 1, 3 × 1 + 1 × 3), the complementary small convolution kernels perform better in the classification and location tasks of semantic segmentation. Subsequently, a complementary convolution residual network (CCRN) is proposed to improve the speed and accuracy of semantic segmentation. To further locate the edge of objects precisely, A coupled Gaussian conditional random field (G-CRF) is utilized for CCRN post-processing. Proposal approach achieved 81.8% and 73.1% mean Intersection-over-Union (mIoU) on PASCAL VOC-2012 test set and Cityscapes test set, respectively.
Diagrammatic approach to scattering of multiphoton states in waveguide QED

We give an exposure to diagrammatic techniques in waveguide quantum electrodynamics (QED) systems. A particular emphasis is placed on the systems with delayed coherent quantum feedback. Specifically, we show that the $N$-photon scattering matrices in single-qubit waveguide QED systems, within the rotating wave approximation, admit for a parametrization in terms of ($N\ensuremath{-}1$)-photon effective vertex functions and provide a detailed derivation of a closed hierarchy of generalized Bethe-Salpeter equations satisfied by these vertex functions. The advantage of this method is that the above-mentioned integral equations hold independently of the number of radiation channels, their bandwidth, the dispersion of the modes they are supporting, and the structure of the radiation-qubit coupling interaction, thus enabling one to study multiphoton scattering problems beyond the Born-Markov approximation. Further, we generalize the diagrammatic techniques to the systems containing more than a single emitter by presenting an exact set of equations governing the generic two- and three-photon scattering operators. The above-described theoretical machinery is then showcased on the example of a three-photon scattering on a giant acoustic atom, recently studied experimentally [G. Andersson et al., Nat. Phys. 15, 1123 (2019)].