Reviewerly/exHarmony huggingface dataset:
Research Practice and Progress of Models and Algorithms Applied in Topic Identification and Prediction Based on the Analysis of CNKI

As a hot topic in the field of library and information, the research on topic recognition and trend prediction has been paid close attention by academic circles. This paper uses a systematic literature review, bibliometric analyses and classification methods. Through a systematic literature review, 96 studies about topic identification and evolution prediction models are selected from the CNKI database. By using VOSviewer to conduct bibliometric analyses, the key research content and themes are revealed. Through the classification method, EXCEL is used to summarize models and algorithms used in the literature comprehensively. It is found that topic identification models and algorithms can be divided into four categories: ① Topic model based on LDA and related derivative models. ② Machine learning and deep learning methods. ③ Methods based on reference relation. ④ Text mining methods. Trend prediction models and algorithms mainly cover two categories: ① deep learning or machine learning models and algorithms based on time sequence; ② link prediction algorithms based on complex network. At the same time, we have also summarized the common index system involved in each study and the way to evaluate the effectiveness of the method, thus this paper comprehensively reveals the application progress in academic circles of topic identification and prediction models and algorithms from the last 10 years and beyond, based on the CNKI database. The purpose is to determine the most popular models and algorithms applied in research, generalize the corresponding indicator systems and validation methods, and finally provide references for model choice or evaluation when identifying and predicting topics in the future. Thus, this paper can help us to understand the overall progress made in text analysis research, and provides a useful reference for selecting and applying the appropriate models, algorithms and indicators.
Mining Discriminative Itemsets Over Data Streams Using Efficient Sliding Window

Abstract In this paper, we present an efficient novel method for mining discriminative itemsets over data streams using the sliding window model. Discriminative itemsets are the itemsets that are frequent in the target data stream, and their frequency in the target stream is much higher in comparison to their frequency in the rest of the streams. The problem of mining discriminative itemsets has more challenges than mining frequent itemsets, especially in the sliding window model, as during the window frame sliding, the algorithms have to deal with the combinatorial explosion of itemsets in more than one data stream, for the transactions coming in and going out of the sliding window. We propose a single scan algorithm using two novel in-memory data structures for mining discriminative itemsets in a combination of offline and online sliding windows. Offline processing is used for controlling the generation of many unpromising itemsets. Online processing is used for getting more up-to-date and accurate online answers between two offline slidings. The discovered discriminative itemsets are accurately updated in the offline sliding window periodically, and the mining process is continued in the online sliding between two periodic offline slidings. The extensive empirical analysis shows that the proposed algorithm provides efficient time and space complexities with full accuracy. The algorithm can handle large, fast-speed, and complex data streams.
UTTSR: A Novel Non-Structured Text Table Recognition Model Powered by Deep Learning Technology

To prevent the compilation of documents, many table documents are formatted with non-editable and non-structured texts such as PDFs or images. Quickly recognizing the contents of tables is still a challenge due to factors such as irregular formats, uneven text quality, and complex and diverse table content. This article proposes the UTTSR table recognition model, which consists of four parts: text region detection, text line detection and recognition, and table sequence recognition. For table detection, the Cascade Faster RCNN with the ResNeXt105 network is implemented, using TPS (Thin Plate Spline) transformation and affine transformation to correct the image and to improve accuracy. For text line detection, DBNET is used with Do-Conv in FPN (Feature Pyramid Networks) to speed up training. Text lines are recognized using CRNN without the CTC module, enhancing recognition performance. Table sequence recognition is based on the transformer combined with post-processing algorithms that fuse table structure sequences and unit grid content. Experimental results show that the UTTSR model outperforms the compared methods. This upgraded model significantly improves the accuracy of the previous state-of-the-art F1 score on complex tables, reaching 97.8%.
A Case Study of Introducing Security Risk Assessment in Requirements Engineering in a Large Organization

Abstract Software products are increasingly used in critical infrastructures, and verifying the security of these products has become a necessary part of every software development project. Effective and practical methods and processes are needed by software vendors and infrastructure operators to meet the existing extensive demand for security. This article describes a lightweight security risk assessment method that flags security issues as early as possible in the software project, namely during requirements analysis. The method requires minimal training effort, adds low overhead, and makes it possible to show immediate results to affected stakeholders. We present a longitudinal case study of how a large enterprise developing complex telecom products adopted this method all the way from pilot studies to full-scale regular use. Lessons learned from the case study provide knowledge about the impact that upskilling and training of requirements engineers have on reducing the risk of malfunctions or security vulnerabilities in situations where it is not possible to have security experts go through all requirements. The case study highlights the challenges of process changes in large organizations as well as the pros and cons of having centralized, distributed, or semi-distributed workforce for security assurance in requirements engineering.
The needs analysis and program development of teacher training for secondary school English teachers in connection with a school of education: The case of Gangwon province

This study aims to develop a teacher training program to effectively enhance the instructional expertise of secondary school English teachers in Gangwon province by collaborating with a local school of education. In order to achieve this objective, the researcher proceeded with two Focused Group Interviews (FGI) with a total of three English teachers, consisting of one middle school teacher and two high school teachers. A questionnaire was provided ahead of each interview which was carried out via Zoom. Drawing upon the questionnaire responses and the information gathered from the interviews, the researcher analyzed the training experiences and specific needs of the three teachers. Then a 30-period teacher training program was designed, taking into account the identified needs as well as the current state of teacher training. To finalize the program selection criteria and determine its detailed contents, the second interview was conducted, focusing on the proposed 30-period program. It was determined to separate the training programs between middle and high schools, offering both online and offline sessions during regular semesters and vacations. The teachers’ opinions on effective ways to activate training were also provided. Finally, the limitations and implications arising from this research were presented.
Visual Analytics on Network Forgetting for Task‐Incremental Learning

Abstract Task‐incremental learning (Task‐IL) aims to enable an intelligent agent to continuously accumulate knowledge from new learning tasks without catastrophically forgetting what it has learned in the past. It has drawn increasing attention in recent years, with many algorithms being proposed to mitigate neural network forgetting. However, none of the existing strategies is able to completely eliminate the issues. Moreover, explaining and fully understanding what knowledge and how it is being forgotten during the incremental learning process still remains under‐explored. In this paper, we propose KnowledgeDrift, a visual analytics framework, to interpret the network forgetting with three objectives: (1) to identify when the network fails to memorize the past knowledge, (2) to visualize what information has been forgotten, and (3) to diagnose how knowledge attained in the new model interferes with the one learned in the past. Our analytical framework first identifies the occurrence of forgetting by tracking the task performance under the incremental learning process and then provides in‐depth inspections of drifted information via various levels of data granularity. KnowledgeDrift allows analysts and model developers to enhance their understanding of network forgetting and compare the performance of different incremental learning algorithms. Three case studies are conducted in the paper to further provide insights and guidance for users to effectively diagnose catastrophic forgetting over time.
DGFaceNet: Lightweight and efficient face recognition

Face recognition has achieved great success due to the development of deep convolutional neural networks (DCNNs). However, complex DCNNs bring a large number of parameters as well as computational effort, which poses a significant challenge to resource-constrained embedded devices. Meanwhile, the commonly popular loss functions and lightweight networks are not so effective for face recognition. In this paper, we first investigate the impact of the number of similar features generated by inexpensive operations on model performance. It is shown that DCNNs can tolerate more similar features generated by cheap operations in the early stage of the network. We construct Dynamic Ghost Bottleneck based on this idea, and DGFaceNet is composed of stacking Dynamic Ghost Bottleneck. In addition, we propose a new class-margin-linear softmax loss function (CML-softmax) for lightweight networks. CML-softmax designs a quadratic function to replace the cosine function as the target logit, which allows better performance and convergence in low-dimensional output for face recognition. Meanwhile, CML-softmax introduces two margin functions to alleviate class imbalance and softmax early saturation problems, respectively. Our method demonstrates competitive results in many validation datasets and large-scale popular benchmark tests. Speed tests on embedded devices show that the actual inference time of DGFaceNet is 11.08 times, 8.57 times, 2.75 times, and 2.82 times faster than ResNet-50, EfficientNet, MobileNetV2, and MobileFaceNet, respectively. DGFaceNet can significantly improve the running efficiency of the model in resource-constrained embedded devices while ensuring the model’s performance.
The 4IR-Health Service Delivery Nexus

The delivery of quality healthcare services to citizens is not only a constitutional right, but also the task and obligation of every government. However, bribery, lack of financial accountability and counterfeit drugs, a shortage of human resources, poor hygiene and ineffective infection control measures, and poor medical record keeping have been identified as major challenges for the implementation of a successful public healthcare system. Fraudulent orders, tender irregularities, fiscal dumping, and over-pricing have also been identified as additional challenges. Due to the lack of quality healthcare services, people have lost trust and hope in the African National Congress (ANC). The paper found that to deliver adequate healthcare services, government needs to establish mechanisms that promote efficiency, quality, transparency, and safety. AI is a mechanism that can increase transparency, efficiency, and detect diseases before they become critical. The paper adopted a qualitative research methodology with an exploratory approach.
Variable Selection for Meaningful Clustering of Multitopic Territorial Data

This paper proposes a new methodology to improve territorial cohesion in clustering processes where many variables from different topics are considered. Clustering techniques provide added value to identify typologies, but there are still unsolved challenges when data contain an unbalanced number of variables from different topics. The territorial feature selection method (TFSM) is presented as a method to select the representative variable of each topic such that the interpretability of resulting clusters is preserved and the geographical cohesion is improved with respect to classical approaches. This paper also introduces the thermometer as a new knowledge acquisition tool that allows experts to transfer semantics to the data mining process. TFSM proposes the index of potential explainability (Ek) as the criteria to select the most promising variables for clustering. Ek is based on the combination of inferential testing and metrics such as support. The proposal is applied with the INSESS-COVID19 database, where territorial groups of vulnerable populations were found. A set of 195 variables with 21 unbalanced thematic blocks is used to compare the results with a traditional multiview clustering analysis with promising results from both the geographical and the thematic point of view and the capacity to support further decision making.
Promoting Document Relevance Using Query Term Proximity for Exploratory Search

In the information retrieval system, relevance manifestation is pivotal and regularly based on document-term statistics, i.e., term frequency (tf), inverse document frequency (idf), etc. Query term proximity (QTP) within matched documents is mostly under-explored. In this article, a novel information retrieval framework is proposed to promote the documents among all relevant retrieved ones. The relevance estimation is a weighted combination of document statistics and query term statistics, and term-term proximity is simply aggregates of diverse user preferences aspects in query formation, thus adapted into the framework with conventional relevance measures. Intuitively, QTP is exploited to promote the documents for balanced exploitation-exploration, and eventually navigate a search towards goals. The evaluation asserts the usability of QTP measures to balance several seeking tradeoffs, e.g., relevance, novelty, result diversification (coverage, topicality), and overall retrieval. The assessment of user search trails indicates significant growth in a learning outcome (due to novelty).
GBDTMO: as new option for early-stage breast cancer detection and classification using machine learning

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Exploring Interpersonal Relationships in Historical Voting Records

Abstract Historical records from democratic processes and negotiation of constitutional texts are a complex type of data to navigate due to the many different elements that are constantly interacting with one another: people, timelines, different proposed documents, changes to such documents, and voting to approve or reject those changes. In particular, voting records can offer various insights about relationships between people of note in that historical context, such as alliances that can form and dissolve over time and people with unusual behavior. In this paper, we present a toolset developed to aid users in exploring relationships in voting records from a particular domain of constitutional conventions. The toolset consists of two elements: a dataset visualizer, which shows the entire timeline of a convention and allows users to investigate relationships at different moments in time via dimensionality reduction, and a person visualizer, which shows details of a given person's activity in that convention to aid in understanding the behavior observed in the dataset visualizer. We discuss our design choices and how each tool in those elements works towards our goals, and how they were perceived in an evaluation conducted with domain experts.
Enhanced Multi-View Subspace Clustering via Twist Tensor Nuclear Norm and Constraint Propagation

Multi-view subspace clustering (MVSC) can effectively group multi-view data distributed around several low-dimensional subspaces. Although encouraging results, most existing methods suffer from two typical limitations, resulting in clustering performance degradation. They ignore high-order correlations underlying the multi-view data, leading to degeneration of complementary power; in addition, they rely on much prior knowledge (e.g., pairwise constraints) for clustering enhancement. In this paper, a novel algorithm called Enhanced Multi-view Subspace Clustering (EMVSC) is proposed to address both limitations. EMVSC can effectively exploit high-order correlations and optimally use limited prior knowledge for better clustering performance. Specifically, EMVSC imposes twist tensor nuclear norm on multi-view tensor representation constructed by stacking view-specific self-representations; in addition, EMVSC exploits prior knowledge of pairwise constraints from whole dataset by employing constraint propagation, which propagates limited constraint knowledge from constrained samples to unconstrained samples. To efficiently optimize EMVSC, an extended intact augmented Lagrangian method is derived with good convergence. Experimental results on seven standard multi-view databases demonstrate its efficacy.
Low-Light Image Enhancement Using a Simple Network Structure

Under low-light conditions, captured images can be affected by unsatisfactory lighting conditions. Low-light image enhancement called LLIE is a digital image processing to obtain natural normal-light images from the low-light image. LLIE includes three main tasks: reducing noise and artifacts, preserving edges and textures, and reproducing natural brightness and color. In recent years, many types of research have focused on deep-learning-based approaches that can achieve excellent performance. However, one primary problem with these methods is that inference time is long owing to complex network structures. To solve the trade-off between the performance and implementation time, we propose a simple network with effective modules. We utilize a U-Net structure and pre-processing is added to preserve edges and textures. Moreover, we embed Channel Attention to restore color and illumination, Res FFT-ReLU to reduce noise, and Pixel Shuffler to preserve the high-frequency components. According to our experimental results, the proposed method achieves better performance and faster inference time than conventional LLIE methods.
LCRM: Layer-Wise Complexity Reduction Method for CNN Model Optimization on End Devices

The increasing significance of state-of-the-art convolutional neural network (CNN) models in computer vision tasks has led to their widespread use in industry and academia.However, deploying these models in resource-limited environments, such as IoT devices or embedded GPUs, presents challenges due to increased complexities and resource consumption.This research paper proposes an optimization algorithm called Layer-wise Complexity Reduction Method (LCRM) to address these challenges by converting accuracy-focused CNNs into lightweight models.It evaluates the standard convolution layers and replaces them with the most efficient combination of substitutional convolutions based on the output channel size.The primary goal is to reduce the computational complexity of the parent models and the hardware requirements.We assess the effectiveness of our framework by evaluating its performance on various standard CNN models, including AlexNet, VGG-9, U-Net, and Retinex-Net, for different applications such as image classification, optical character recognition, image segmentation, and image enhancement.Our experimental results show up to a 95% reduction in inference latency and up to 93% reduction in energy consumption when deployed on GPU.Furthermore, we compare the LCRM-optimized CNN models with state-of-the-art CNN optimization methods, including pruning, quantization, clustering, and their four cascaded optimization methods, by deploying them on Raspberry Pi-4.The profiling experiments performed on each model demonstrate that the LCRM-optimized CNN models achieve comparable or better accuracy than the parent models while providing added benefits such as a 62.84% reduction in inference latency on end devices and significant memory compression and complexity reductions.
Toward Automatic Tutoring of Math Word Problems in Intelligent Tutoring Systems

Math Word Problem (MWP) solving, which involves solving math problems in natural language, is a prevalent approach employed by Intelligent Tutoring Systems (ITS) for teaching mathematics. However, one major drawback of ITS is the complexity of encoding all potential solutions for each problem supported, which is both time-consuming and labour-intensive. In this study, we propose a novel method for automatically converting the statement of a previously unseen MWP into the internal representation of an ITS, thereby simplifying the task of adding new MWPs by only requiring the problem statement. To accomplish this, we propose the use of large pre-trained language models to translate the problem into Python code, which can then be easily imported into an ITS. Experimental results indicate that this approach is effective and suitable for the task, and as language models continue to improve, the accuracy rates are expected to increase further.
Bi-Directional Adaptive Probabilistic Method With a Triangular Segmented Interpolation for Robot Path Planning in Complex Dynamic-Environments

Path planning is a fundamental aspect of mobile robots and autonomous systems. Methods of path planning are used in robotics to create a path for a robot or autonomous system to follow from a starting position to a goal one while avoiding obstacles and satisfying any additional conditions. There are many different methods to plan the path, including probabilistic methods, heuristics-based approaches, and optimization-based methods. In this paper, we propose a new path planning method called Dynamic Adaptive RRT-connect with a Triangular Segmented Interpolation. Our method improves the traditional RRT algorithms by using an Adaptive-RRT approach, where a random node is chosen as a new node to increase tree exploration. Then, we use a Bi-directional scheme to further enhance the convergence time and cost. Additionally, our method employs Triangular Segmented Interpolation (TSI) method to improve path length and smoothness. Finally, we operate this method within a dynamic environment depending on the Dynamic Window Approach (DWA). Experiments on a variety of environments have shown that our proposed method achieves better than the RRT and RRT-connect algorithms individually in terms of computation time (reduced by 90-80%), cost (reduced by 82-63%), and path length (shorten by 17-12%) besides the ability to avoid dynamic obstacles efficiently.