Reviewerly/exHarmony huggingface dataset:
A Swin Transformer based Framework for Shape Recognition

Shape recognition is a fundamental problem in the field of computer vision, which aims to classify various shapes. The current mainstream network architecture is convolutional neural network (CNN), however, CNN offers limited ability to extract valuable information from simple shapes for shape classification. To address this problem, this paper proposes a deep learning model based on self-attention and Vision Transformers structure (ViT) to achieve shape recognition. Compared with the traditional CNN structure, ViT considers the long-distance relationship and reduces the loss of information between layers. The model utilizes a shifted-window hierarchical vision transformer (Swin Transformer) structure and an all-scale shape representation to improve the performance of the model. Experimental results show that the proposed model achieves superior accuracy compared to other methods, achieving an accuracy of 93.82% on the animal dataset, while the performance of state-of-the-art VGG-based method is only 90.02%.
A Survey of Using Unsupervised Learning Techniques in Building Masked Language Models for Low Resource Languages

A very common approach nowadays towards learning how to represent text in machines is to use transformers. These models are based on neural networks, and they show promising results when applied to problems in which both the input and output of the model are sequences. In this paper we give an overview on what transformers are with a focus on Bidirectional Encoder Representations from Transformers (BERT). Furthermore, we analyze different approaches on how these models are used for text representation in low resource languages. The final goal is to establish whether BERT can be applied to establish NLP capabilities for the Albanian language.
Construction and Data Integration of College Student Management Model Based on Human-Computer Interaction Data Acquisition and Monitoring System

The research on the management mode of students in colleges and universities is helpful in cultivating students’ autonomous learning, self-education, and self-service ability. However, there are still some deficiencies in the construction of college student management model. This paper aims to study and analyze the construction and data integration of college student management mode under the data acquisition and monitoring system based on human-computer interaction. Firstly, this paper analyzes the human-computer interaction technology. Robots participate in human activities and coexist with people, which is called human-computer interaction. Human-computer interaction technology mainly includes voice interaction, image interaction, data interaction, and action interaction. Then, the sensor data acquisition algorithm based on cooperative communication is analyzed, and finally, the management mode of students is experimentally explored. The composition of the management system of college students is realized by establishing the management subject, management object, and management environment of students. The experimental results of this paper show that, in the investigation of the student union management system, 43% of the participants chose “the system exists, but it needs to be improved,” and 31.5% of the participants chose “the system is perfect.” “The system is not perfect” accounted for 14.8%, and 89.2% of the participants believed that there was a system. Obviously, whether it is the system, the enforcement strength, or the standard level, there is still room for improvement. We can strengthen the connotation construction of the student union and improve the management function of the student union, deepen the understanding of the status and role of the student union, build a standardized evaluation system for the student union, and strengthen the internal organization of the student union.
NTP-VFL - A New Scheme for Non-3rd Party Vertical Federated Learning

Vertical Federated Learning (FL) handles decentralized and partitioned vertically data about common entities. While most existing privacy-preserving federated learning algorithms require a third party (TP) as an intermediary data accessor to coordinate model training, we propose a new private-preserving scheme named NTP-VFL (Non-3rd Party Vertical Federated Learning). Utilizing Paillier homomorphic encryption, our algorithm strategy allows for multi-party model training and guarantees clients' privacy against honest-but-curious adversaries. To the best of our knowledge, this is the first non- TP method that solves multi-party computation problems in Logistic Regression tasks. Our theoretical analysis and extensive experiments show outstanding performance with an average increase in efficiency of about 25% baselines with the traditional federated learning approach.
Differential Privacy-Based Double Auction for Data Market in Blockchain-Enhanced Internet of Things

With the rapid development of the Internet of Things (IoT), large amounts of data are collected, which constitute a valuable business resource. Hence, a suitable IoT data market needs to be established, and the provision of safe and effective trading services for multiple buyers and sellers is required. This paper introduces an IoT data market framework supported by blockchain. It focuses on a transaction realization scheme for multiple buyers and sellers. In the scheme, the mechanisms are designed to determine the corresponding data providers and recipients for the buyers and sellers, respectively, and the transaction prices of both parties. When the data market runs, an inference attack will raise bid information leakage issues. We study a transaction scheme that enables differential privacy protection of bids based on an exponential mechanism. This paper theoretically proves the individual rationality, weak budget balance, and truthfulness of the normal transaction scheme and differential privacy-based transaction scheme. This paper also theoretically proves the effectiveness of the differential privacy protection for bids of transaction participants. Furthermore, this paper verifies the performances of the two schemes through digital simulation experiments. From the experiments, we can also prove that these schemes occupy reasonable social welfare and computational overhead.
Hybrid Navigation Method for Multiple Robots Facing Dynamic Obstacles

With the continuous development of robotics and artificial intelligence, robots are being increasingly used in various applications. For traditional navigation algorithms, such as Dijkstra and A*, many dynamic scenarios in life are difficult to cope with. To solve the navigation problem of complex dynamic scenes, we present an improved reinforcement-learning-based algorithm for local path planning that allows it to perform well even when more dynamic obstacles are present. The method applies the gmapping algorithm as the upper layer input and uses reinforcement learning methods as the output. The algorithm enhances the robots' ability to actively avoid obstacles while retaining the adaptability of traditional methods.
HSIE: Improving Named Entity Disambiguation with Hidden Semantic Information Extractor

Named Entity Disambiguation (NED) is a fundamental task in natural language processing. However, existing methods pay more attention to the acquisition of global features, but ignore hidden semantic information in local features. In this paper, we propose a Hidden Semantic Information Extractor (HSIE) to capture hidden semantic features. Specifically, the HSIE is composed of multi-layer neural networks with multi-head attention and a transition layer. Vectors of candidate entities are iteratively updated in the HSIE to capture more elaborated semantic features, which are beneficial for Neural Attention Module (NAM) to compute precise feature scores of candidate entities. An extensible vector space module aims to connect the HSIE and the NAM effectively, which enhances the performance of the HSIE. Such an HSIE can be embedded into local model effectively and extended to global and global (neighbor) models. Moreover, we develop a disambiguation system by orchestrating local, global and global (neighbor) models, each of which is equipped with their own HSIE, the extensible vector space module and the NAM respectively. Experimental results show that our disambiguation system achieves the best performance on AIDA-B, MSNBC and ACE2004 datasets.
MGRank: A keyword extraction system based on multigraph GoW model and novel edge weighting procedure

Keyword extraction is the process of extracting the most descriptive words from a textual document. State-of-the-art graph-based keyword extraction systems generally represent text documents using a simple graph called a graph-of-words (GoW), based on the sliding window concept. This representation of a text document requires determining a proper window size, models the document on a local scale, and allows the establishment of a single relation between two candidate keywords. In this study, we address these problems and propose a keyword extraction system called MGRank which uses a complete multigraph structure to build a GoW model to represent a text document. The completeness property of the proposed GoW model provides a means to represent a document globally and eliminates the need to determine the window-size parameter. Parallel edges allow the establishment of multiple relations between candidate keywords. In this study, we also propose a new edge-weighting procedure based on the positional distance of candidate keywords. To evaluate the performance of MGRank, we performed experiments on seven benchmark datasets and compared the results with those of six baseline methods. The experimental results show that MGRank outperforms the baseline methods statistically in precision, recall, and F1-score in almost all cases. In terms of mean average precision and mean reciprocal rank, MGRank performs statistically better than node ranking-based and statistical baseline methods and achieves on-par results with topic-based baseline methods. Furthermore, the experimental results showed that MGRank extracted the most relevant keywords.
Self-Supervised Monocular Depth Estimation Based on Channel Attention

Scene structure and local details are important factors in producing high-quality depth estimations so as to solve fuzzy artifacts in depth prediction results. We propose a new network structure that combines two channel attention modules in a deep prediction network. The structure perception module (spm) uses a frequency channel attention network. We use frequencies from different perspectives to analyze the channel representation as a compression process. This enhances the perception of the scene structure and obtains more feature information. The detail emphasis module (dem) adopts the global attention mechanism. It improves the performance of deep neural networks by reducing irrelevant information and magnifying global interactive representations. Emphasizing important details effectively fuses features at different scales to achieve more accurate and clearer depth predictions. Experiments show that our network produces clearer depth estimations, and our accuracy rate on the KITTI benchmark has improved from 98.1% to 98.3% in the δ &lt; 1.253 metric.
Information and Educational Environment in the Inclusive Education Practice

The study is devoted to inclusive education. The role and importance of the information and educational environment in the practice of inclusive education are discussed. The characteristics of the information and educational environment of the university are given. This paper provides data on the analysis of the main components of inclusive education. A functioning model of the information and educational environment of the university for people with disabilities is presented.
Lightweight real-time stereo matching algorithm for AI chips

AI chips have developed rapidly and achieved remarkable acceleration effects in the corresponding algorithm field in recent years. However, deep learning algorithms are changing rapidly, including many operators that AI chips and inference frameworks cannot use in the short term. To solve the problem that it is challenging to deploy a stereo matching algorithm based on binocular vision on AI chips, this paper proposes a multi-stage unsupervised lightweight real-time depth estimation algorithm for AI chips called TradNet. TradNet combines the traditional matching algorithm with a convolutional neural network and uses convolution directly supported by AI chips to realize the structure of the traditional matching algorithm. TradNet is composed of operators directly supported by current AI chips, which reduces the computational complexity of the algorithm, and greatly improves the compatibility of the stereo matching algorithm with existing AI chips. Compared with the deep learning-based multi-stage binocular disparity algorithm AnyNet, the accuracy is improved by 5.12%, and the inference speed is only 12.7%. Compared with the matching-based binocular disparity algorithm BM, the accuracy is improved by 25.24%, and the inference speed is only 48.7%. Our final model can process 1280×720 resolution images within a range of 60–80 FPS on an NVIDIA TITAN Xp. It achieves 28FPS on a 1TOPS (Tera Operations Per Second) custom AI chip, and the power consumption is 0.88 W.
Transfer Kernel Learning for Multi-Source Transfer Gaussian Process Regression

Multi-source transfer regression is a practical and challenging problem where capturing the diverse relatedness of different domains is the key of adaptive knowledge transfer. In this article, we propose an effective way of explicitly modeling the domain relatedness of each domain pair through transfer kernel learning. Specifically, we first discuss the advantages and disadvantages of existing transfer kernels in handling the multi-source transfer regression problem. To cope with the limitations of the existing transfer kernels, we further propose a novel multi-source transfer kernel kms. The proposed kms assigns a learnable parametric coefficient to model the relatedness of each inter-domain pair, and simultaneously regulates the relatedness of the intra-domain pair to be 1. Moreover, to capture the heterogeneous data characteristics of multiple domains, kms exploits different standard kernels for different domain pairs. We further provide a theorem that not only guarantees the positive semi-definiteness of kms but also conveys a semantic interpretation to the learned domain relatedness. Moreover, the theorem can be easily used in the learning of the corresponding transfer Gaussian process model with kms. Extensive empirical studies show the effectiveness of our proposed method on domain relatedness modelling and transfer performance.
ChevroCrypto – Security &amp; Cryptography Broker

The Internet of Things technologies are increasingly incorporated into the various challenges that arise, and it is becoming increasingly important to ensure data protection from these systems. There are several tools available to assist developers in data protection, but understanding the international mandatory rules and a whole set of essential characteristics that should be considered when carrying out projects is required. This project was developed to assist developers who may have less expertise, are unfamiliar with the region, and want a high level of security in their system.
Research on Unmanned Ship Path Planning based on RRT Algorithm

Abstract The increase of water shipping density, coupled with the increasing complexity of the waterway, the ship's intelligent collision avoidance system plays a pivotal role in the transportation safety of shipping. Therefore, it has gradually become the focus of water traffic safety research. In recent years, China is still facing a period of great development in navigation technology, safety, green, digital, intelligent will become the development trend. Although the waterway environment has been improving, the increase of shipping vessels has led to frequent accidents in the waterway, and the traditional management methods and means lack real-time monitoring and scheduling of vessels, and the management lacks intelligence and information technology. In this paper, we solve the ship path problem from the point of path planning of intelligent ships. RRT* algorithm is a common method for path planning, but the traditional RRT* algorithm has problems in ship path planning, slow convergence speed, many path turns and large curvature fluctuations. For these problems, an improved fast extended random tree algorithm (Bi-RRT) is proposed. The improved RRT* algorithm is simulated and verified by MATLAB simulation platform. The simulation results show that the research algorithm can shorten the planning time, reduce the number of iterations, and has better feasibility and effectiveness.
Conceptualising language archives through legacy materials

Purpose The purpose of this paper is to discuss the role of linguistic legacy materials within archives and databases. These data of past documentation projects are currently playing a minor role in the design of modern language archives. This is due to various challenges that legacy materials pose – ethical considerations, difficulties with formats, unclear or deficient metadata. Tackling these challenges can highlight general issues in language documentation and the use of language data. These insights can be used to inform the design of tools and infrastructures for data in this field, both recent and legacy materials. Design/methodology/approach This paper is conceptual and theorises digital language archives through their oldest deposits. It is informed by the author’s experiences in working with linguistic legacy materials of the South Estonian Kraasna dialect. The discussion makes references to relevant discourses in linguistics, archiving and computer science, encouraging transdisciplinary efforts in the design of language archives. Findings A digital archive created around linguistic legacy materials has the potential to respond to challenges posed by current data. Originality/value This paper discusses digital language archives from the perspective of documentary linguistics. It introduces the challenges and necessary steps in curating legacy materials. Several suggestions for the design of digital archives arise from this discussion. These ideas can inspire creators of digital language archives and provide a view from researchers using legacy materials.
A robust, real-time camera-based eye gaze tracking system to analyze users’ visual attention using deep learning

Eye gaze tracking has recently become indispensable for domains like virtual reality, augmented reality, human–computer interaction and advertisement. The commercial eye gaze tracking equipment is too expensive to be used by the masses. In this manuscript, a non-invasive, low-resolution ordinary camera-based system has been proposed for tracking eye gaze. The proposed system attained an accuracy of 84% without requiring any costly equipment or infrared light like other commercial tools. Eye gaze fixations data of 37 participants were recorded on a webpage and gaze patterns were plotted using heat maps. Further, a case analysis was done to validate the performance of the proposed system, where the user interface has been controlled using eye movements.
Implicit neural refinement based multi-view stereo network with adaptive correlation

In this paper, we propose ACINR-MVSNet, an end-to-end trainable framework with adaptive group-wise correlation and implicit neural depth refinement for multi-view stereo (MVS). Previous learning-based MVS methods have demonstrated their outstanding performance, and most of them estimate depth maps in a coarse-to-fine manner. However, in a commonly used multi-stage cascaded framework, the previous wrong estimation might lead to error propagation. In contrast, we focus on another coarse-to-fine structure, i.e., one-stage MVS architecture followed by refinement modules. Inspired by implicit neural representation, we propose an implicit neural refinement module to refine the coarse depth map. Guided by the corresponding reference image, it can better recover finer details, especially those in boundary areas. To solve the visibility problem in complex scenarios while maintaining efficiency, we propose an adaptive group-wise correlation similarity measure for cost volume construction. Besides, we present a pyramid-based feature extraction network with a repeated top-down and bottom-up structure to gather more context-aware information, which can better meet the challenges in ill-posed regions. This novel feature extractor is also utilized to construct an enhanced Gauss-Newton refinement module for further upsampling and optimizing. Extensive experiments on the DTU, the Tanks & Temples and the BlendedMVS datasets demonstrate the effectiveness and generalization of our approach, which can achieve better or competitive results compared to state-of-the-art methods. The code will be available at https://github.com/BoyangSONG/ACINR-MVSNet.
Progress of quantum entanglement in a trapped-ion based quantum computer

In recent years, there have been significant progress toward building a practical quantum computer, demonstrating key ingredients such as single-qubit gates and a two-qubit entangling gate. Among various physical platforms for a potential quantum computing processor, a trapped-ion system has been one of the most promising platforms due to long coherence times, high-fidelity quantum gates, and qubit connectivity. However, scaling up the number of qubits for a practical quantum computing faces a core challenge in operating high-fidelity quantum gates under influence from neighboring qubits. In particular, for the trapped-ion system, unwanted quantum crosstalk between qubits and ions’ quantum motional states hinder performing high-fidelity entanglement as the number of ions increases. In this review, we introduce a trapped-ion system and explain how to perform single-qubit gates and a two-qubit entanglement. Moreover, we mainly address theoretical and experimental approaches to achieve high-fidelity and scalable entanglement toward a trapped-ion based quantum computer. • Trapped-ion system has been one of the most promising quantum computing platforms due to high-fidelity quantum gates and qubit connectivity. • Scalable quantum computing requires high-fidelity quantum entanglement. • Trapped-ion qubits can be entangled through their Coulomb-coupled motion by applying a qubit state-dependent laser field. • Large numbers of qubits in the system complicate couplings between their motions and qubit states, degrading the entangling fidelity. • Controlling the laser field can mitigate unwanted couplings and achieve high-fidelity entanglement for the universal quantum computer.