Reviewerly/exHarmony huggingface dataset:
Meaningful Engagement in E-Learning Through the Integration of Mobile Learning Tools

Despite the ubiquitous and potential of mobile technology in education, there is limited consideration for mobile-supported learning environments (M-learning) within the learning management systems (LMS) in many institutions of higher education in developing countries. Beyond the limitations of accessibility and flexibility, it is crucial to reconsider how to sustain learners' active engagement through integration of e-portfolios and M-learning. This chapter explores how such an integrative approach can be operationalized to facilitate effective e-learning. Further, the implementation of e-portfolios within M-learning contexts from situated and authentic learning theoretical perspectives is examined with suggestions to promote active engagement and meaningful learning.
Learning and the typology of word order: a model of the Final-over-Final Condition

This article investigates whether cross-linguistic generalizations may arise from asymmetries in learnability of competing syntactic patterns. The model presented here uses a domain- general statistical learner for parameter systems in order to probe whether languages violating the Final-over-Final Condition (FOFC; Sheehan et al. 2017) might be difficult to learn, rather than syntactically impossible. In this model, no parameters ruled out *FOFC languages, and no penalties targeting them were built into the learner. Regardless, the results of two learning tasks demonstrate a correlation between the learnability of a word order pattern and its frequency in the typology. *FOFC languages were harder to learn, providing a possible explanation for their relative rarity.
Space-Time Processes

This chapter deals with stationary Gaussian processes in $${\mathbb R}^d$$ specified by their covariance function. In the case of a real Gaussian process, K(x, x′) is a real-symmetric function of the separation vector x − x′; in the case of a complex-valued process, K(x, x′) is a Hermitian function of x − x′. The real Matérn process has a covariance function that is the Fourier transform of the rotationally-symmetric Student t distribution on $${\mathbb R}^d$$ ; the Gaussian process is real stationary and isotropic. The complex Matérn process has a covariance function that is the Fourier transform of the re-centered Student t distribution on $${\mathbb R}^d$$ ; the process is stationary but not isotropic. Hermitian covariance products are considered as models for a complex-valued space-time process. The real part of a Hermitian product is the sum of the product of the real parts and the product of the imaginary parts. It is real-symmetric and positive definite, so it may be used as a space-time covariance function. The product of the real parts is doubly symmetric, while the product of the imaginary parts is doubly skew-symmetric, so their contributions are very distinct. Both products are symmetric. The product of the real parts is positive definite, and the sum is also positive definite.
Increasing Communication Rates Using Photonic Hyperentangled States

We propose a mechanism for increasing transmission rate of quantum communication channels, by multiplexing spin and multiple orbital angular momentum states on a single photon, transmitting the photon, and demultiplexing them to different photons.
Estimation Method of Light Source Position for Linear-type Sensor

A proposed method that can be applied to light sources illuminating toward the center of the sensor with limited divergence is estimating the light source position using a single line image from the linear-type sensor.
Scrutinizing Micro-Macro and Macro-Macro Entanglement with NOON Initial State

In this article, we produce micro-macro and macro-macro states with the NOON initial state; finally, we scrutinize the effective squeezing parameter, mean photon numbers, number of input photons and number of macroscopic modes on concurrence.
Machine learning aided classification and grading of biopsy sample using discrete wavelet transform and gray level co-occurrence matrix

Machine learning based classification and grading of the pathological specimen plays an important role in the biomedical field. In the current study, we discuss the importance of feature extraction for machine learning.
A Reversible Optical-coherence Converter

We demonstrate here that entropy, or random fluctuations, in an optical degree of freedom (DoF) can be reversibly swapped between different DoFs, such that coherence is converted back and forth between them without loss of energy.
Effect of Pulse-shape Mismatch on the Security of Measurement-device-independent QKD Protocols

Pulse-width mismatch, primarily caused by channel asymmetry, affects the visibility of Hong-Ou-Mandel (HOM) interference. We show that a channel asymmetry of 176.5 km reduces HOM visibility to 0.37, rendering a polarization-based MDI-QKD protocol completely insecure.
Random Number Generation with Quantum Computers

Random numbers feature in most cryptographic protocols, including those securing internet traffic. Most random number generators deployed on internet appliances are subject to cryptanalytic attack. We explore generating random numbers on compact quantum computers.
Collaborative error-immune information transfer with OAM modes of light

OAM modes of light are already being used for a number of quantum communication tasks. As an application, we present a collaborative information transfer scheme that allows to share error-free information even through noisy channels.
Secure Cross-User Fuzzy Deduplication for Images in Cloud Storage

In cloud storage, existing image fuzzy deduplication technology often adopts a cloud-based deduplication method, which, although it improves the image’s deduplication efficiency, ignores the client’s communication overhead. Therefore, to further reduce the bandwidth consumption caused by redundant image uploading, researchers use similar images to extract the same features and employ image features as encryption keys to achieve cross-user deduplication. Although this approach reduces the communication overhead, it increases the risk of side-channel attacks and threatens the image’s privacy. Thus, this paper proposes a cross-user deduplication scheme based on image content decomposition to solve the privacy concern. Specifically, by acquiring the image’s frequency characteristics, the base data representing the image’s main contents and the deviation data representing the image’s details are decomposed from the image. Then, we use the cross-user deduplication method for the base data deduplication and the cloud side deduplication method for the deviation data deduplication. The implementation demonstrates that the developed scheme improves the deduplication efficiency under the premise of effectively resisting side-channel attacks.
Research on Multi-channel Retrieve Mechanism Based on Heuristic

Search system is a crucial component in information systems, that is to retrieve the relevant documents by the specific query. There are many search retrieve algorithms, almost all focus on a specific domain of retrieve algorithms, such as text matching, tag matching, semantic matching. However, in a real search system, the purpose of the retrieve module is to find the documents related to the specified query as fully as possible, so it requires that the search system should contain multiple forms of retrieve capability. In this paper, we propose a multi-channel retrieve model, including text channel, semantic channel and intention channel. In text channels, we propose heuristic two-stage query rewrite model, which can generate more semantically rich queries. In semantic channel, we present a novel sample dynamic construction method, which saves the manual annotation cost, and can train the model more fully. Finally, a brief general implementation of the retrieve methods based on intention channel is presented. The results show that the retrieve method based on multi-channel can significantly improve the retrieve number and accuracy of documents, and business indicators such as click-through rate are also significantly improved.
Text-Independent Speaker Identification Using a Single-Scale SincNet-DCGAN Model

The state-of-the-art x-vector technique has been successful in text-independent speaker recognition tasks. However, neural networks are susceptible to overfitting issues in small sample settings, which impairs network performance. Recent studies have attempted to improve the regularization of speaker recognition networks using generative adversarial networks and have shown competitive results. In this paper, we propose a novel deep convolutional generative adversarial network-based speaker identification technique, which adds single-scale SincNet to the DCGAN network and performs text-independent speaker recognition directly using discriminators in the DCGAN network. Also, the loss function in the original model is replaced by the Wasserstein distance. Additionally, by jointly optimizing the “true/false” and classification objective functions, the discriminator enhances the speaker recognition system's capacity for generalization. On the LibriSpeech corpus, our technique outperformed the baseline model x-vector utilizing the dropout method and L2 regularization by a margin of 55.11% and 67.08%, respectively.
Knowledge Learning-Based Brain Storm Optimization Algorithm for Multimodal Optimization

Using swarm intelligence to obtain multiple optima in a single run simultaneously proved efficient for solving multimodal optimization problems (MOPs). However, the existing studies fail to resolve the contradiction between the required solution accuracy and the number of solutions. In this paper, an improved brain storm optimization (BSO) algorithm based on knowledge learning (KLBSO) is proposed as a solution to the problem. The properties of the improved algorithm and the domain knowledge of the problem are combined during the search process. Two factors need to be taken into account to solve a MOP: the accuracy and the diversity of the solution set. In the proposed algorithm, there are two learning approaches. Firstly, improving the learning method by replacing the perturbation operator of the random solution with the inter-solution learning of the worst solutions, improves the optimization ability of the algorithm. Secondly, by analyzing the MOPs, adding an archive set guarantees the solution’s diversity. To assess the efficiency of KLBSO, eight benchmark functions with various sizes and complexities were used. Comparing the results of KLBSO with those of state-of-the-art methods which are brain storm optimization algorithm (BSO), brain storm optimization algorithm in objective space (BSOOS), two kinds of pigeon-inspired optimization algorithms (PIO, PIOr), the comparison results show that the KLBSO is able to solve the contradiction between required solution accuracy and the number of solutions, and improves the outcomes where BSO is ranked first followed by the test algorithms.
Multi-Agent Path Finding Algorithm of Merged Node Rule and Bounded Focal Search

The complexity of the problem of Conflict-Based Search (CBS) in the Multi-Agent Path Finding(MAPF) is a major issue that affects the scalability of the system. In order to address this issue, we proposed Rule and Bounded Conflict-Based Search (RBCBS), which uses rules of child node selection and bounded focus search. First, to reduce the number of nodes to be searched, the selection rules of RBCBS are formulated to ensure that the number of nodes is limited. Second, the lower bound of the path planning cost is determined using the bounded focused search method by combining the suboptimal factors to generate the interval range of the focused search, ensuring that the cost of the new node selection priority on the final path planning is close to the optimal value. The simulation studies reveal that, when compared to standard algorithms like CBS, ICBS, the proposed RBCBS decreases the number of nodes examined and the runtime, enhances the algorithm's scalability and allows the multi-agent to find collision-free pathways rapidly.
Evaluation of Motion Planning Algorithms for Underground Mobile Robots

The underground coal mine is an extremely hard environment for workers due to geological hazard, poisonous gas, and harmful dusts. In such environment, coal mining robots are very helpful for coal mines to reduce the number of onsite workers, enhance safety, and improve efficiency. However, the autonomous movement of coal mine robots in the underground tunnel is still a big challenge. This paper gives a detailed evaluation of the commonly used motion planning algorithms for underground mobile robots. Firstly, the motion planning framework for mine mobile robots is proposed. It considers the global path planning, local path planning ,and the recovery behavior. Then, a motion testing framework in a simulated coal mine environment is constructed based on the ROS platform to test the commonly used motion planning algorithms, such as the the Dijistra, A*, rrt* and Hybrid A* algorithms. The test results shows that RRT* of DWA algorithm runs faster than other algorithm but Hybrid A* of DWA algorithm gets the shortest and smoothest path.
Complementary Convolutional Restricted Boltzmann Machine and Its Applications in Image Recognition

Restricted Boltzmann Machines (RBMs) are widely applied in Image Classification and Image Reconstruction. However, although highly expressive conditional distributions of RBMs commonly produce effective features for the two tasks, building these expressive conditional distributions and sampling from the distributions are difficult for conventional RBMs. In this paper, a Complementary Convolutional Restricted Boltzmann Machine (CCRBM) is proposed. The CCRBM designs complementary factors in its visible layer and uses the factors to produce highly expressive conditional Gaussian distributions under low sampling cost. To further extract hierarchical image features, a Complementary Convolutional Deep Belief Net (CCDBN) is proposed for Image Processing based on CCRBM. Experiments verify that the proposed CCRBM and CCDBN perform better than other commonly used RBMs and probabilistic graphic models with the help of the designed complementary factors.
Word Sense Disambiguation for Hindi Language Using Neural Network

One of the major hurdles in the development of Natural language Processing applications is ambiguity and the process to solve is known as Word Sense Disambiguation. It is useful to determine the appropriate meaning of polysemy words in each context using computational methods. In every language, ambiguity exists, and to resolve this Knowledge, Supervised, and Unsupervised based approaches are used. Over the past ten years, the Indian government has launched a large number of digital services for its citizens. To better serve Indian citizens, these services are primarily based on Hindi or other Indian languages. For it to be easily accessed by web portals or any electronic device, natural language processing is required. Since English and other languages such as Chinese, Japanese, and Korean have abundant resources for natural language processing application development. Due to the limited resources available for disambiguating polysemous words in Hindi and other Indian languages, building applications based on these languages becomes challenging. This paper provides a very gentle introduction to the simplest version of neural network techniques for solving ambiguity problems who are not familiar with integration with IndoWordNet. The outcomes demonstrate that the suggested technique performs consistently and significantly better than the alternatives.
Bounds for Sparse Solutions of K-SVCR Multi-class Classification Model

The support vector classification-regression machine for k-class classification (K-SVCR) is a novel multi-class classification approach based on the “1-versus-1-versus-rest” structure. In this work, we suggested an efficient model by proposing the p-norm $$(0<p< 1)$$ instead of the 2-norm for the regularization term in the objective function of K-SVCR that can be used for feature selection. We derived lower bounds for the absolute value of nonzero entries in every local optimal solution of the p-norm based model. Also, we provided upper bounds for the number of nonzero components of the optimal solutions. We explored the link between solution sparsity, regularization parameters, and the p-choice.
Synthetic Aperture Radar Image Based Navigation Using Siamese Neural Networks

Due to its ability to capture precise topological features in obstructive meteorological environments, synthetic aperture radar (SAR) technology offers a multitude of novel applications including the possibility of developing self-reliant navigational techniques for global positioning system denied settings. To this effect, the broader aim of this chapter is to utilize image data generated by SAR to determine the location of a given system that is navigating over a specified geographical area of interest. We propose an image retrieval technique that leverages on the concept of Siamese neural network, which is an artificial neural network (ANN) often used for signature verification and face recognition. As a backbone, the network architecture is constructed based on SqueezeNet, which is a compact deep neural network that offers greater scalability compared to other popular architectures. Numerical experiments are performed and demonstrate that the proposed method can be used effectively and holds promise for navigational tasks.
ieSTGCN:A Mining Model of Skeleton Spatio-temporal Graph

Traditional methods of human action recognition focus on internal links between human joints, that is, local neighbor connections. However, some external links that cannot exist in the skeleton graph are also important for identifying human behaviors, such as hands and feet in harmonious movement. To capture richer spatial features in a skeleton graph, it is necessary to add external links and distinguish internal links from external links in a skeleton graph. Therefore, this paper designs two different adjacency matrices to characterize the internal links and external links of the human body respectively, and set different edge weights and feature weights to them for autonomous learning during the convolution process. Furthermore, a spatio-temporal graph convolution network called ieSTGCN is proposed. It consists of two modules: graph convolution network supporting internal and external links (ieGCN) and temporal convolution network in human joints (joTCN). Experiments on the Kinetics and the NTU-RGB+D datasets demonstrate that our model can obtain better recognition accuracy than some benchmark models.
Joint Feature Fusion Approach to Human Pose Estimation Using mmWave Radar

This paper mainly studies the use of millimeter wave (mmWave) radar for 3D human pose estimation. Although pioneering works have achieved remarkable success, the lack of stability of continuous data acquisition by mmWave radar is still an intractable problem in pose capturing. To mitigate this problem, a joint feature fusion method is proposed, which is based on the assumption that human pose in a motion sequence is domained by bone connection and specific motion cooperation mechanism. Specifically, we introduce a forget gate to suppress error features extracted due to the loss of information, and a select gate to select the most reasonable joint characteristics. Outputs of the two gates are fused to form an anti distortion mechanism of human pose estimation. Experiments show the effectiveness of our method.
TGNN: A GNN-Based Method with Multi-entity Node for Personal Banking Time Prediction

The offline business of banks is deeply involved in bank clients' financial lives and directly affects the satisfaction of clients. For offline banking scenarios, accurate prediction of clients' banking time is essential for the service system of the banks, which can be used for multiple downstream tasks, such as queuing time prediction, real-time queuing planning, etc. However, the problem of banking time prediction remains open due to many technical and domain challenges, such as Anonymous Client Problem (ACP) and Sparse Interaction Problem (SIP). The existing works mainly utilize the traditional machine learning method and are relatively plain in modeling this practical scenario thus leading to an unpromising performance. Different from existing works, we predict the personal banking time by modeling the clients and banking services in the banking process through mining a large number of banking records. In this paper, we propose a Tuple-node-based Graph Neural Network (TGNN) framework to learn representations of clients and services of banks and estimate the personal banking time based on the representations. Empirical studies on two real-world datasets collected from a fintech bank demonstrate the effectiveness of TGNN.
Modelling Trapped Ion Qubits: Optical Bloch Equations of Magnetic Dipole Transitions in a Four State System

An optical Bloch equation model is derived for a four state system interacting with electromagnetic radiation via magnetic dipole transitions. Applying the model to Rabi oscillation data of trapped ytterbium-171 ions contributes to experimental characterization.
Organizational and pedagogical conditions as factors of the quality of education of Russian language teaching in bilingual rural schools

In the Russian Federation, bilingualism and multilingualism are becoming an integral part of modern society. The problem of ensuring the availability of quality education, regardless of the place of residence and the language of instruction, is a priority task of the Russian education system. Organizational and pedagogical conditions can become one of the factors for obtaining high-quality education, regardless of the place of residence and language of education of students. The article presents the results of a study aimed at identifying organizational and pedagogical conditions that contribute to improving the quality of Russian language teaching in rural schools that conduct educational activities in their native language. A leading research method is questionnaire survey, which involved respondents from four regions - the Republic of Altai, Republic of Sakha (Yakutia), Tatarstan, Tuva - in which official languages are Russian and that of the republics. The research material was based on the responses to questionnaires of 368 respondents in three groups: heads of rural educational organizations, Russian language teachers, students in grades 5-9 and their parents. In addition, the following methods were used: observation, conversation, interviews, questionnaires, content analysis, and study of school documentation. Based on the theoretical analysis of sources on the research topic, the concept of “organizational and pedagogical conditions” was specified; the definitions of the types of conditions - motivational, administrative and managerial, personnel, material and technical, control and evaluation - were clarified and formulated. The results of the study allowed identifying these conditions as factors that ensure quality and presenting them in terms of motivation (for what purpose?), organization (what and how?), personnel (who?), logistics (where and by what?) and evaluation and control (how?). The results obtained helped to formulate methodological recommendations for creating organizational and pedagogical conditions as a set of certain pedagogical measures to influence participants in educational relations. These measures ensure the improvement of the quality of education and are aimed at the formation of balanced bilingualism in children in bilingual education. For our multilingual country, state support for Russian and the native languages of its peoples is necessary not only as a factor of sociopolitical development, but also as a guarantee of state security of a multinational state.