Reviewerly/exHarmony huggingface dataset:
Handwritten Multi-Scale Chinese Character Detector with Blended Region Attention Features and Light-Weighted Learning

Character-level detection in historical manuscripts is one of the challenging and valuable tasks in the computer vision field, related directly and effectively to the recognition task. Most of the existing techniques, though promising, seem not powerful and insufficiently accurate to locate characters precisely. In this paper, we present a novel algorithm called free-candidate multiscale Chinese character detection FC-MSCCD, which is based on lateral and fusion connections between multiple feature layers, to successfully predict Chinese characters of different sizes more accurately in old documents. Moreover, cheap training is exploited using cheaper parameters by incorporating a free-candidate detection technique. A bottom-up architecture with connections and concatenations between various dimension feature maps is employed to attain high-quality information that satisfies the positioning criteria of characters, and the implementation of a proposal-free algorithm presents a computation-friendly model. Owing to a lack of handwritten Chinese character datasets from old documents, experiments on newly collected benchmark train and validate FC-MSCCD to show that the proposed detection approach outperforms roughly all other SOTA detection algorithms.
Prior-Driven NeRF: Prior Guided Rendering

Neural radiation field (NeRF)-based novel view synthesis methods are gaining popularity. NeRF can generate more detailed and realistic images than traditional methods. Conventional NeRF reconstruction of a room scene requires at least several hundred images as input data and generates several spatial sampling points, placing a tremendous burden on the training and prediction process with respect to memory and computational time. To address these problems, we propose a prior-driven NeRF model that only accepts sparse views as input data and reduces a significant number of non-functional sampling points to improve training and prediction efficiency and achieve fast high-quality rendering. First, this study uses depth priors to guide sampling, and only a few sampling points near the controllable range of the depth prior are used as input data, which reduces the memory occupation and improves the efficiency of training and prediction. Second, this study encodes depth priors as distance weights into the model and guides the model to quickly fit the object surface. Finally, a novel approach combining the traditional mesh rendering method (TMRM) and the NeRF volume rendering method was used to further improve the rendering efficiency. Experimental results demonstrated that our method had significant advantages in the case of sparse input views (11 per room) and few sampling points (8 points per ray).
What makes a readable code? A causal analysis method

Abstract Context Code readability is one of the most important quality attributes for software source code. To investigate which features affect code readability, most existing studies rely on correlation‐based methods. However, spurious correlations (a mathematical relationship wherein two variables appear to be causal but are not) involved in correlation‐based methods may affect research conclusions. Objective In order to remove spurious correlations and obtain conclusions from the perspective of causation as to what makes a readable code, we propose a causal theory‐based approach to analyze the relationship between code features and code readability scores. Method First, we adopt the PC algorithm and additive noise models to construct the causal graph on the basis of the selected code features. Then, we use the linear regression algorithm based on the back‐door criterion to obtain the causal effect of different features on code readability. Result We conduct a set of experiments on readability data labeled by human annotators. The experimental results show that the average number of comments positively impacts code readability, with each additional unit increasing the code readability score by 0.799 points. Whereas the average number of assignments, identifiers, and periods have a negative impact, with each additional unit decreasing the code readability score by 0.528, 0.281, and 0.170 points respectively. Conclusion We believe that our findings will provide developers with a better understanding of the patterns behind code readability, and guide developers to optimize their code as the ultimate goal.
Multi‐feature subspace representation network for person re‐identification via bird's‐eye view image

Abstract Person re‐identification (Re‐ID) is one of the most remarkable research topics that widely applied in our daily lives. For person Re‐ID in bird's eye scenes, traditional computer vision‐based methods used multiple features, for example, texture and color, of a pedestrian's head and shoulders. Those methods are difficult to cope with environments of variety and the change the appearance of different people due to the instability of feature detection. On the other hand, although recent advanced deep learning‐based methods are powerful to extract discriminative features, the requirement of a large amount of annotated training data restricts the appliable tasks. To overcome this problem, in this article, we propose a novel method fusing multiple heterogeneous features through a multi‐feature subspace representation network (MFSRN) to maximize the classification performance while keeping the disparity among features as small as possible, that is, common‐subspace constraints. We conducted comparative experiments with state‐of‐the‐art models on the bird's‐eye view person dataset, and extensive experimental results demonstrated that our proposed MFSRN could achieve better recognition performance. Furthermore, the validity and stability of the method are confirmed.
Time and Accuracy Trade-Off of LPI Radar Classification Technology Based on Time-Frequency Analysis and Deep Learning

저피탐(LPI, low probability of intercept) 레이다 신호를 빠르고 정확하게 식별하는 기술은 인지통신 연구에 중요한 기술 이다.본 논문에서는 시간-주파수 분석(TFA, time-frequency analysis)과 딥러닝을 사용해 12가지 전형적인 레이다 신호를 식별하는 기술을 연구하였다.기존 CWD(Choi-Williams distribution)를 사용하는 방식은 spectrogram에 비해 500배 이상 긴 TFA 생성 시간이 필요하다.본 논문에서는 spectrogram, WVD(Wigner-Ville distribution), CWD를 딥러닝 학습 데이터로 사용 하여 저피탐 신호의 식별 정확도와 검출 시간의 trade-off를 제시한다.결과적으로, CWD 모델이 spectrogram 모델보다
Quality management: A model for optimizing information provision in Romanian special research libraries

A study regarding the relevance of special libraries and the quality of access to information in Romania was conducted. The general objective was to develop a contribution regarding the quality management in libraries using the grounded theory method. The model obtained for optimizing the quality of access to information implies the existence of material resources, but also innovations, the involvement of human resources through creativity and promotion, leading to increased academic visibility, increased satisfaction of long-term active users, attraction of new users both in the physical space and in the digital environment and, finally, increased relevance of the library in question within the tutelary research institute. The value of the study lies in the series of solutions for optimizing the access to scientific information in research libraries in the digital age, and the correlations between the results obtained based on the grounded theory and the three generic models of library quality management, LibQual+, Balanced Scorecard, and ISO 9001, by adapting them to the context of the special research libraries in Romania.
ADS-Classif: A Robust Tool for Classifying Documents from Diverse Sources Using Weighted Domain-Specific Vocabulary

AbstractExtracting domain-specific words from the domain corpus to build vocabulary and identifying classes represented by domain-specific vocabulary is a challenging task in classifying text. Several comprehensive domain-specific techniques have been developed to build specific domain vocabulary but they lack generality and also do not consider the degree of the word's relevance. This paper proposes a supervised generic algorithmic model to create a domain-specific vocabulary. The model identifies domain-indicative word combinations from labeled real-time domain corpus, calculates their weights and logged their inclusion time to understand their semantic behavior in the present era of development. Further, a framework ADS-Classif is also developed using vocabulary to classify real-time documents extracted from different sources and test the domain-specific vocabulary's significance. Experimental results show that the approach leads to the accurate identification of domain classes for test documents compared with classical classifiers Naïve Bayes and Support Vector Machine.KEYWORDS: ClassificationClassifiersDomain-specific vocabularyNaïve BayesSupport vector machine Disclosure statementNo potential conflict of interest was reported by the author(s).Additional informationNotes on contributorsVandana KalraVandana Kalra is an associate professor at Sri Guru Gobind Singh College of Commerce, University of Delhi, Delhi, India. She holds the post of head of department of computer science from 2006 to 2019. She has been teaching in this field for 23 years and has been active in the multidisciplinary field of research and innovation. She has a deep passion for innovation related to technology and advanced areas of computer science. She has been involved in many innovation projects and achieved the best societal impact award for one of the projects. Email: vandana.kalra@sggscc.du.ac.inIndu KashyapIndu Kashyap has 15 years of teaching, administration, and research experience. Currently, she is a professor with Manav Rachna International Institute of Research and Studies. She has to her credit more than 60 publications in reputed journals and conferences including Elsevier, Springer, and Taylor & Francis. Her research areas include wireless networks, machine learning, data analytics, and recommender systems. Email: indu.kashyap82@gmail.comHarmeet KaurHarmeet Kaur is an associate professor in the Department of Computer Science, Hansraj College, University of Delhi. Her teaching experience spans over a period of around 24 years. Her research interests lie in the field of recommender systems and crowdsourcing. She has published around 45 research papers in national and international journals and conferences of repute. Corresponding author. Email: hkaur@hrc.du.ac.in
Applying data-driven learning in self-translation of academic discourse: A case study of a Chinese medical student

This article reports on an experiment on the use of data-driven learning (DDL) in the revision of self-translation by a Chinese medical student. The think-aloud method is employed to investigate the difficulties the student encountered in self-translation and the effectiveness of DDL in improving the quality of self-translation. Results show that difficulties in the self-translation of medical abstracts are mostly associated with markers of rhetorical moves, terminologies, and conventional academic expressions and that they can be effectively solved by such corpus consultation strategies as checking possible options in bilingual dictionaries, using the most certain keywords to find collocations, and using the most possible accompanying words to find contexts. A comparison of translations before and after the application of DDL reveals that it could help improve translation quality in lexical choices, syntactic structures, and discourse practice. An immediate interview shows that the participant holds a positive attitude toward DDL.
Human-Computer Integration as an Extension of Interaction: Understanding Its State-of-the-Art and the Next Challenges

The Interaction Era is evolving and extending to the Human-Computer Integration Era, in which the relationship between humans and technologies is characterized as a partnership. Motivated by this evolution, we conducted a Systematic Literature Review to present the current state of knowledge about the emerging paradigm of Human-Computer Integration (HInt), as well as its opportunities and challenges for the Human-Computer Interaction (HCI) area. As a result, we organize the existing knowledge and research on HInt which describes it as a new interaction paradigm. Our findings indicate that HInt is a topic of growing interest in the HCI Community, and provide an overview of existing initiatives and key research gaps on HInt within the HCI scope. As contributions, our results are useful to (1) expand the HCI community’s knowledge regarding this emerging paradigm and (2) stimulate new initiatives and contributions to the consolidation and evolution of the HCI with a lens on HInt.
TTECCDU: a blockchain-based approach for expressive authorization management

Authorization uses the access control policies to allow or limit a user the access to a resource. Blockchain-based access control models are used to manage authorization in a decentralized way. Many approaches exist that have provided the distributed access control frameworks which are user driven, transparent and provide fairness with its distributed architecture. Some approaches have used authorization tokens as access control mechanisms and mostly have used smart contracts for the authorization process. The problem is that most of the approaches rely on a single authorization factor like either trust or temporal; however, none has considered other important factors like cost, cardinality, or usage constraints of a resource making the existing approaches less expressive and coarse-grained. Also, the approaches using smart contracts are either complex in design or have high gas cost. To the best of our knowledge, there is no approach that uses all the important authorization factors in a unified framework. In this article, we present an authorization framework: TTECCDU that consists of multi-access control models i.e., trust-based, cost-based, temporal-based, cardinality-based, and usage-based to provide strong and expressive authorization mechanism. TTECCDU also handles the delegation context for authorization decisions. The proposed framework is implemented using smart contracts which are written in a modular form so that they are easily manageable and can be re-deployed when needed. Performance evaluation results show that our smart contracts are written in an optimized manner which consume 60.4% less gas cost when the trust-based access is compared and 59.2% less gas cost when other proposed smart contracts from our approach are compared to the existing approaches.
Identifying Historic Buildings over Time through Image Matching

The buildings in a city are of great importance. Certain historic buildings are landmarks and indicate the city’s architecture and culture. The buildings over time undergo changes because of various factors, such as structural changes, natural disaster damages, and aesthetic interventions. The form of buildings in each period is perceived and understood by people of each generation, through photography. Nevertheless, each photograph has its own characteristics depending on the camera (analog or digital) used for capturing it. Any photo, even depicting the same object, is impossible to capture in the same way in terms of illumination, viewing angle, and scale. Hence, to study two or more photographs depicting the same object, first they should be identified and then properly matched. Nowadays, computer vision contributes to this process by providing useful tools. In particular, for this purpose, several feature detection and description algorithms of homologous points have been developed. In this study, the identification of historic buildings over time through feature correspondence techniques and methods is investigated. Especially, photographs from landmarks of Drama city, in Greece, on different dates and conditions (weather, light, rotation, scale, etc.), were gathered and experiments on 2D pairs of images, implementing traditional feature detectors and descriptors algorithms, such as SIFT, ORB, and BRISK, were carried out. This study aims to evaluate the feature matching procedure focusing on both the algorithms’ performance (accuracy, efficiency, and robustness) and the identification of the buildings. SIFT and BRISK are the most accurate algorithms while ORB and BRISK are the most efficient.
Fast Single-Parameter Energy Function Thresholding for Image Segmentation Based on Region Information

To solve the problems of image threshold segmentation based on weak continuous constraint theory, the running time is long, and the two parameters need to be selected manually, and therefore a fast single-parameter energy function thresholding for image segmentation based on region information (FSEFTISRI) is proposed in this paper. The proposed FSEFTISRI algorithm uses simple linear iterative clustering (SLIC) technology to pre-block the image, extract the image super-pixels, and then map the image super-pixels to the interval type-2 fuzzy set (IT2FS), so as to construct the single-parameter energy function to search the optimal threshold, and adaptively select the penalty parameters in the energy function through the class uncertainty theory. On a non-destructive testing (NDT) database and Berkeley segmentation datasets and benchmarks (BSDS), the proposed FSEFTISRI is compared with five related algorithms. The average misclassification error (ME) of the proposed FSEFTISRI algorithm on NDT and BSDS are 0.0466 and 0.0039, respectively. The results show that the proposed FSEFTISRI has acquired more satisfactory results in visual effect and evaluation index, and the running time of the proposed FSEFTISRI algorithm is shorter, which shows the effectiveness of the proposed FSEFTISRI.
Explainable clinical coding with in-domain adapted transformers

Automatic clinical coding is a crucial task in the process of extracting relevant information from unstructured medical documents contained in Electronic Health Records (EHR). However, most of the existing computer-based methods for clinical coding act as "black boxes", without giving a detailed description of the reasons for the clinical-coding assignments, which greatly limits their applicability to real-world medical scenarios. The objective of this study is to use transformer-based models to effectively tackle explainable clinical-coding. In this way, we require the models to perform the assignments of clinical codes to medical cases, but also to provide the reference in the text that justifies each coding assignment.We examine the performance of 3 transformer-based architectures on 3 different explainable clinical-coding tasks. For each transformer, we compare the performance of the original general-domain version with an in-domain version of the model adapted to the specificities of the medical domain. We address the explainable clinical-coding problem as a dual medical named entity recognition (MER) and medical named entity normalization (MEN) task. For this purpose, we have developed two different approaches, namely a multi-task and a hierarchical-task strategy.For each analyzed transformer, the clinical-domain version significantly outperforms the corresponding general domain model across the 3 explainable clinical-coding tasks analyzed in this study. Furthermore, the hierarchical-task approach yields a significantly superior performance than the multi-task strategy. Specifically, the combination of the hierarchical-task strategy with an ensemble approach leveraging the predictive capabilities of the 3 distinct clinical-domain transformers, yields the best obtained results, with f1-score, precision and recall of 0.852, 0.847 and 0.849 on the Cantemist-Norm task and 0.718, 0.566 and 0.633 on the CodiEsp-X task, respectively.By separately addressing the MER and MEN tasks, as well as by following a context-aware text-classification approach to tackle the MEN task, the hierarchical-task approach effectively reduces the intrinsic complexity of explainable clinical-coding, leading the transformers to establish new SOTA performances for the predictive tasks considered in this study. In addition, the proposed methodology has the potential to be applied to other clinical tasks that require both the recognition and normalization of medical entities.
A general QoE assessment framework for applications and services

In this paper, we have designed a testbed to evaluate end-to-end Quality of Experience (QoE) performance for mobile applications and devices in a controllable and repeatable manner. Various network scenarios have been defined which allow replication of real-life conditions in a controlled environment. The general testbed framework is presented, which is comprised of a deconstruction process and an aggregation process. A convergence study of the testbed has been carried out to identify the required number of iterations for achieving stable results, and human panel experiments have been introduced to calibrate the performance of the testbed. A case study of two use cases, i.e. content streaming and high-speed internet, is presented with focus on the user experience domain. The measurement results have demonstrated the measurement capability of the proposed testing methodology, which is flexible to generalize the QoE computations for many of the use cases. It is shown that with the help of the testbed, we can better understand how radio resources and propagation channel models will influence the performance of the application/service under test.
Analysis and Design of Integrated Intelligent Anti-error System for Substation

Abstract A new comprehensive intelligent anti-error system that meets the requirements of smart substations is proposed. This new system can overcome the problem that the microcomputer anti-error system lacks operation error prevention technology for secondary equipment and also lacks operation and maintenance operations under the integrated control operation mode. Firstly, this paper analyzes the characteristics of the anti-error system, and the principle function of the judgment model. And then, an anti-misoperation process system is established for work scenarios such as information collection and monitoring, remote control operation error prevention, sequential control operation error prevention, local operation error prevention, secondary equipment, operation, and maintenance. This paper finally builds a realtime online anti-error system that can be intelligently analyzed for the whole business process to ensure the safety of intelligent substation equipment operation.
A Novel Ultra-short-term Photovoltaic Power Generation Forecasting Method Based on Seasonal Autoregressive Integrated Moving Average

Abstract This study suggests an ultra-short-term photovoltaic (PV) energy generation forecasting model based on Seasonal Autoregressive Integrated Moving Average (SARIMA) and Support Vector Machine (SVM). It can further increase the predictive performance of PV electricity generation electrical output and address the issue of significant fluctuation and instability of PV power generation output power. To achieve combined prediction, SVM is employed in this study to model the nonlinear SARIMA prediction residuals. In comparison to the traditional SVM method and the SARIMA method, the mixed forecasting method has a good forecasting consequence and can serve as a favorable basis for the safe operation and scheduling of the PV grid, according to the results of the simulation of actual data.