Reviewerly/exHarmony huggingface dataset:
A Brief Survey of Text Classification Methods

Text classification is a long-standing research spot in natural language processing (NLP), which greatly facilitates various NLP tasks. With the rapid development of Internet and social media, the need for effective data management also makes text classification become more and more important. Its research methodology has also developed from the early shallow classification based on handcrafted features to the deep language modeling with various neural networks. In this paper, we first give a brief introduction to its background and definition, and then describe its representative methods at different stages of development. Meanwhile, we also introduce the recently popular pre-trained language models on large-scale corpus, and discuss their applications to text classification. Overall, this paper gives a quick guide to understanding text classification research.
Multi-strategy Adaptive Whale Optimization Algorithm

The Whale optimization Algorithm (WOA) is a metaheuristic algorithm proposed based on the simulation of the hunting behavior of whales. It has advantages such as simplicity and few adjustable parameters. However, WOA suffers from insufficient exploration and exploitation abilities in the later stage of iteration, resulting in low solution accuracy and a tendency to get trapped in local optima. In response to the shortcomings of WOA, including slow convergence speed, low accuracy, inadequate global exploration ability, and a tendency to get trapped in local optima, an improved Whale optimization Algorithm (MSIWOA) is proposed. Firstly, a nonlinear decreasing mechanism for iteration number is designed to balance the global search and local exploitation abilities. Additionally, a Cauchy mutation mechanism is introduced to improve the problem of low convergence accuracy and avoid getting trapped in local optima. The effectiveness of the MSIWOA algorithm is tested on 19 widely used test functions, and the results demonstrate that the proposed algorithm has better performance than traditional WOA and other improved algorithms, proving the effectiveness of the MSIWOA algorithm.
Research On an Image Dehazing Algorithm Based On MATLAB

With the rapid development of technology, computer technology is gradually maturing. In the computer vision field, image processing technology in specific weather has become an important research field. For those photos taken in foggy weather, the mist will affect natural light leading to a certain impact on the visual effect making pictures seem unclear. Additionally, the useful information in the pictures will decrease. To solve the image blurring problem caused by natural weather such as haze, this paper adopts the histogram equalization method. By averaging the gray histogram of the image, the blurred areas are magnified and dispersed, and a clear state is visually presented. The retinex algorithm is used to process the histogram equalization result image to enhance the useful image information and further process the result details to defog the image and obtain the final defogged image.
Building A Deep Learning Model for Multi-Label Classification of Natural Disasters

Natural disasters, such as earthquakes, hurricanes/typhoons and wildfires, usually cause severe damage. Disaster response and management is a great challenge to the authority. Current studies usually focus on a single disaster identification using social media data. In reality, there are relationships among different types of disasters. And several disasters may happen simultaneously. In this study, we explore the role of the deep learning model in multi-label disaster classification. We build a deep CNN model for multi-label classification with the instruction of a high-order strategy. We train and validate our model using a professional low-altitude disaster dataset, LADI. We find our proposed deep learning model with the transfer learning method outperforms many other machine learning models in the previous study.
English Text Classification based on BiLSTM and Attention Mechanism

Different English text types directly affect the accuracy of the English translation. To give full play to the value of text information and improve the accuracy of the English translation, an English text classification model combining BiLSTM and the attention mechanism is proposed. Firstly, the text features are extracted using a BiLSTM network, and the attention mechanism is introduced, which assigns secondary weights to the selected features according to the importance of the words expressing the sentence meaning, and finally, the text representation is cascaded into the softmax function to classify the text. The experiments show that the method achieves 86% accuracy, 85% recall, and 84% F1-score index in the News Category dataset.
The communicative environment of a synchronous online Russian lesson in Chinese groups

The communicative approach to teaching Russian as a foreign language (RFL) implies that the main goal - the acquisition of communication skills - can only be achieved in a stimulative communicative environment. Meanwhile, the online format, which is currently becoming popular, is characterized by limited interaction between the participants of the educational process. Thus, the problem of developing methodological foundations for a productive communicative environment in the process of online teaching is becoming acute for the methodology of teaching RFL. The aim of this study is to describe the features of educational communication, determined by Chinese cultural and educational traditions, on the material of monolingual groups from China studying Russian in Russian universities, and to develop a methodological framework for creating a productive communicative environment for synchronous RFL online lessons, considering the specifics of students' communicative behavior. The study material included 10 synchronous online RFL lessons with a total duration of 15 hours and 51 minutes. Monolingual groups of Chinese students (A2-B2) participated in the lessons. A content analysis method using Atlas.ti software was applied to process the results. Coding was based on a deductive approach with elements of inductive approach; the coding system was based on the category of social presence, operationalized and adapted to the specifics of RFL learning. The prevalence of several communication behavior patterns among Chinese students, which conflict with the current methodological principles, was shown. At the same time, deviations from conventional behavior of Chinese students at the analyzed RFL classes were observed. This suggested that despite the strong influence of cultural attitudes, the parameters of the communicative environment of an online lesson may vary depending on the teacher's efforts. It is argued that the formation of communicative environment of a synchronous online RFL lesson requires that the teacher possesses the ability to manage the interactivity of communication, to form an atmosphere of cooperation and cohesion in the online group, to maintain emotionality of online communication. The study contributes to the development of digital language teaching, forming a scientific basis for creating methodological technologies of a productive educational environment in RFL online teaching.
Joint Identification of Space-Time Block Code and Modulation Methods Based on Trajectory Image

A trajectory image-based identification algorithm is proposed for the joint identification of Space-Time Block Code (STBC) and modulation method used in multiple input multiple-output (MIMO) systems. First, the correspondence between the combination of STBC, modulation method, and trajectory image of the received signal was analyzed, and the feasibility of joint identification of STBC and modulation methods based on trajectory image was demonstrated. Two improvement schemes for trajectory images were given to increase the differentiation of different STBCs on trajectory images and the detailed information contained in trajectory images under BPSK. Finally, using the modified LeNet network to classify the trajectory image. The simulation verifies the ability of the algorithm to jointly discriminate between 24 combinations of 4 modulation methods and 6 STBCs, showing the algorithm’s feasibility to together to identify modulation methods and STBC jointly.
Federated Graph Learning with Cross-subgraph Missing Links Recovery

Federated Graph Learning (FGL) has been developed to enable multiple parties to collaboratively train a graph neural network while maintaining their local graph data. Despite its advantages, FGL is not immune to the issue of cross-subgraph link missing, which arises when there are edges between nodes in different subgraphs that are not observed by any of the parties. This can have a substantial negative impact on the performance of the Graph neural networks, particularly in cases where the missing edges are of crucial importance. This paper proposes a novel Federated Graph Learning Framework with Cross-subgraph Missing Links Recovery (FGL-CMLR) to address this issue by capturing the global graph structure and identifying the missing links. FGL-CMLR reduces the search space of nodes with potential link loss from the entire subgraph to a target set comprising clustered central and edge nodes. Next, a federated GCN-based link prediction model is trained to repair the lost links between central-edge nodes pairs belonging to different subgraphs. By aggregating representations of neighboring nodes in cross-subgraphs, the previous embedding of nodes with missing links can be corrected. Numerical experiments demonstrate that FGL-CMLR successfully alleviates the issue of cross-subgraph link missing and achieves improved accuracy.
Exploring the Role of Deep Learning in Liver Segmentation

Delineating a target organ is the procedure known as biomedical segmentation. Liver cancer is the second most common cancer to cause death. The automatic liver segmentation from CT scans is a necessary job for the diagnosis of liver cancer disease. However, because of its irregular form and proximity to other organs, the liver is one of the more challenging organs to segment. For liver segmentation in the current research, datasets with only liver labels are typically used, the model is not resistant to cases involving multiple phases and vendors. AbdomenCT1K is a large and various abdominal CT datasets from 12 medical institutions, including cases with multiple diseases, vendors, and phases. To segment the liver, we use the Dense-UNet trained with AbdomenCT-1K, and it achieved the dice of 0.9271 and NSD of 0.9685. We compare different backbone networks in this research, and the results show that our model is more reliable and performs better than many other deep learning models in the prior study.
Inverse projection of axisymmetric orientation distributions

We show that the projection of an axisymmetric three-dimensional orientation distribution to two dimensions can be cast into an Abel transform. Based on this correspondence, we derive an exact integral inverse, which allows for the quantification of three-dimensional uniaxial alignment of rodlike units from two-dimensional sliced images, thus providing an alternative to X-ray or tomographic analysis. A matrix representation of the projection and its inverse is derived, providing a direct relationship between two- and three-dimensional order parameters for both polar and non-polar systems.
Not ‘just consumers of content’: early insights into the policy enactment of the Digital Learning Framework

In Ireland, as with many international jurisdictions, digital technology is firmly positioned at the core of educational discourses. However, despite a rise in ‘techno-positivity’ among professionals in schools, these positive attitudes do not seem to permeate through to practice nor in effective learning outcomes. As a result, given the introduction of the Digital Learning Framework in the Irish schooling system, this study focuses on the initial experiences of those who lead the initiative within their respective schools and the contextual nuances they must navigate in the design of the enactment. This qualitative study comprised in-depth interviews with the Digital Learning Leaders of 10 different schools. Data evidenced the initial organisational considerations a leader must reckon with during the planning phase, and early indications of pedagogical impact, leading to a conclusion regarding contextual literacy. The research also highlights the subtle policy shift of embedding digital technologies within school evaluation mechanisms, heightening a sense of accountability associated with successful enactment.
Designing an Effective Organizational Culture to Guard Against the Cyber Risks of Emerging Technologies

The Internet of Medical Things (IoMT) and interoperable technologies have transformed how patient data affect medical care; such technological innovations revolutionize how healthcare organizations (HCOs) improve cost, quality, and access. New cyber risks, however, accompany developing cyber ecosystems. Although immediate data exchange is beneficial, risk arises from the IoMT's increased susceptibility to human influence. The success of quality care relies on protecting health information technology (HIT) against newly developing cyber vulnerabilities. Therefore, managers must be just as invested in their HCO's cybersecurity protocols as cybercriminals are in bypassing those protocols. This essay proposes a healthcare cyber resiliency model that leverages human and technical factors through a cycle of feedback and process improvement. It intends to equip healthcare administrators with the foundational philosophy necessary to secure their emerging technologies.
Visually semantic-preserving and people-oriented color image encryption based on cross-plane thumbnail preservation

Uploading images to the cloud has increasingly become a trend, and users have therefore enjoyed many benefits brought by the cloud. On the other hand, these images may carry a wealth of sensitive information, which threatens privacy. Some traditional encryption schemes, in which the original image with rich information is encrypted into the noise-like image, may be able to deal with privacy threats but give up visual usability. Recently, thumbnail-preserving encryption (TPE) was proposed to balance visual usability and privacy but exposes too much information in the encrypted image. Motivated by this, the existing TPE schemes are analyzed in-depth and the scheme to encrypt the image based on cross-plane thumbnail preservation is proposed. A novel architecture of permutation-substitution matching this scheme is constructed, which can preserve the visually semantic information in the encrypted image to balance usability and privacy, and the balance is people-oriented. Meanwhile, a prototype of the scheme is proposed by using a chaotic system, and experiments are conducted using the prototype to verify the excellent performance of this work, e.g., the visual effect is achieved to preserve the semantics, and it becomes the fastest scheme at present om terms of time cost.
Segmentation embedding method with modified interpolation for increasing the capacity of adaptable and reversible audio data hiding

The development of digital technology brings many advantages to human life. However, it also comes with the risk of making data protection required. This protection can come in many forms, one of which is the data hiding method, often called steganography. It works by embedding secret data into media data, such as images, audio, and video, to avoid suspicion caused by their existence. Nevertheless, the embedding capacity and stego-data quality problems have become challenging. This research proposed new audio steganography, which utilizes modified linear interpolation values to give flexibility to the embedding capacity. This proposed method also adds bit thresholds to distribute the embedding capacity as evenly as possible to maintain its similarity level (quality) as high as possible by shifting the embedded sample value towards the original value of stego-data produced. The experimental results show that this method can obtain an average similarity level of 100.55 dB on 0.75 bit per sample audio data and 87.68 dB on 2.267 bit per sample audio data. The stability of stego-data quality is also measured by considering the best and minimum quality measurements with a difference of less than 1 dB on embedding secret data below 1.52 bit per sample.
CSAL: Cost sensitive active learning for multi-source drifting stream

Multi-source stream classification is a prominent real-world problem challenged by the limited real labels and non-stationary environment. Despite growing research achievements in this field, most existing works solved this problem by requiring all real labels of source or target streams to conduct domain adaption or transfer learning mechanisms, which brings high labeling costs. However, in real-world applications, there are usually insufficient labeled data in both source and target streams, and the annotation cost of the source and target streams are generally unequal. Thus, we propose a Cost-Sensitive Active Learning (CSAL) method for multi-source drifting streams. Specifically, a multi-source ensemble framework with an asymmetry weighting mechanism is presented to ensure beneficial knowledge transfer and avoid the negative transfer. Then, a multi-perspective similarity estimation method is proposed to evaluate the similarity of source and target streams. On this basis, a novel cost-sensitive hybrid labeling strategy that combines volatility strategy and uncertainty strategy with a cost-sensitive budget control mechanism is proposed, which adaptively selects representative samples at the appropriate time. At last, a parallel multiple hypothesis drift detection method is proposed, which can efficiently utilize real labels to detect concept drift. Experimental results on real-world and synthetic data streams show that our CSAL outperforms the state-of-the-art methods with even fewer labels.
Improving adversarial robustness of deep neural networks via adaptive margin evolution

Adversarial training is the most popular and general strategy to improve Deep Neural Network (DNN) robustness against adversarial noises. Many adversarial training methods have been proposed in the past few years. However, most of these methods are highly susceptible to hyperparameters, especially the training noise upper bound. Tuning these hyperparameters is expensive and difficult for people not in the adversarial robustness research domain, which prevents adversarial training techniques from being used in many application fields. In this study, we propose a new adversarial training method, named Adaptive Margin Evolution (AME). Besides being hyperparameter-free for the user, our AME method places adversarial training samples into the optimal locations in the input space by gradually expanding the exploration range with self-adaptive and gradient-aware step sizes. We evaluate AME and the other seven well-known adversarial training methods on three common benchmark datasets (CIFAR10, SVHN, and Tiny ImageNet) under the most challenging adversarial attack: AutoAttack. The results show that: (1) On the three datasets, AME has the best overall performance; (2) On the Tiny ImageNet dataset, which is much more challenging, AME has the best performance at every noise level. Our work may pave the way for adopting adversarial training techniques in application domains where hyperparameter-free methods are preferred.
Secure delegated quantum algorithms for solving Mahalanobis distance

Distance metric is an important subroutine of some artificial intelligence algorithms such as classification and clustering algorithms. However, when a distance metric such as Mahalanobis distance is needed to calculate the distance between two points, the time required for classical algorithms to deal with Mahalanobis distance grows exponentially with the size of dataset and the dimensionality of the data. In this paper, a quantum algorithm for solving Mahalanobis distance (QAMD) is proposed. It can achieve an exponential speedup over the best classical counterpart if the covariance matrix in the sample distribution is a sparse Hermitian matrix. Especially, we propose a secure delegated QAMD protocol by combining the presented QAMD and quantum homomorphic encryption (QHE) which allows users with limited quantum capability to solve Mahalanobis distance with the help of a remote quantum server and still keep their data private. We also simulate a special case of the proposed delegated QAMD protocol on IBM's quantum cloud platform to show its feasibility.
Multi-hop quantum teleportation with GHZ-class states and Bell states in quantum networks

To transmit an arbitrary single-qubit state between two distant nodes, a protocol for multi-hop quantum teleportation is proposed based on various types of GHZ-class states and Bell states. Here, two effective methods are presented to determine the recovery Pauli operators. One is to derive a general expression for the final state, and the other is to construct a finite state machine. Additionally, this protocol can be implemented on the IBM quantum experience platform once the necessary quantum circuits have been designed. The experimental findings are loaded into quantum state tomography to check the accuracy of the transmitted quantum state. Furthermore, compared to multi-hop quantum teleportation using arbitrary Bell states or GHZ states, our protocol shows the lower classical communication costs.
A novel framework for the efficient evaluation of hybrid tree-pattern queries on large data graphs

Graph pattern matching is a fundamental operation for exploring and analyzing large graphs, which are widely used to represent entities and their relationships in a plethora of applications. However, existing algorithms do not perform satisfactorily as they generate a large number of intermediate results. In this paper, we introduce a novel framework to address the problem of efficiently finding homomorphic matches of hybrid tree-patterns over large graphs. These are patterns that can include two types of edges: child edges (which are mapped to edges in the data graph) and/or descendant edges (which are mapped to paths in the data graph), thus allowing for higher expressiveness and flexibility in query formulation. We introduce the concept of answer graph to compactly represent the query results and exploit computation sharing. Leveraging the answer graph, our approach is able to avoid the generation of intermediate results not participating in the query answer, and can produce the query answer in time linear on the size of the input data graph and the output. Moreover, the answer graph allows query counting without explicitly enumerating query results. We design a bottom-up algorithm (BUP) and a simulation-based algorithm (SIM) for building the answer graph, each having their own strengths in different cases. We also present two algorithms for enumerating the results using the answer graph. Our extensive experimental evaluation on real and synthetic datasets shows that our proposed techniques greatly outperform the state-of-the-art graph matching algorithms as well as a popular graph DBMS on evaluating various hybrid tree-pattern queries. Our source code, datasets and queries are publicly available at https://github.com/wuxyng/TwigMatchOnGraph.