Reviewerly/exHarmony huggingface dataset:
Chinese Character Relationship Extraction Method Based on BERT

With the continuous development of information technology and the advent of the era of big data, today's society has entered the era of artificial intelligence. A variety of artificial intelligence application products continue to appear, playing various important roles in many fields, such as AI + agriculture, AI + medical, AI + autonomous driving, AI + education, etc. The combination of artificial intelligence and traditional industries The emergence of artificial intelligence has made these traditional industries shine. In the field of artificial intelligence, there is a very important sub field--Natural Language Processing (NLP). In the development of various industries, the scale of data generated by various industries is increasing, and the problem of information overload is becoming more and more serious, so how to quickly and accurately Obtaining key information in the data is of great significance. However, the text data generated by all walks of life is often unstructured and diverse in types. Therefore, in recent years, information extraction in the field of natural language has become a research hotspot, and person relationship extraction is an important branch of information extraction. Person relationship extraction is the core task of text mining and information extraction, and its task is to identify sentences. The semantic relationship between two person name entities, in recent years, person relationship extraction has been widely promoted and applied in various aspects, such as intelligent semantic search, character knowledge graph construction, question and answer systems, etc. It performs well. Therefore, it is of great significance to design and develop a set of Chinese character relationship extraction system. In response to this problem, this paper proposes a BERT-based Chinese character relationship extraction architecture. Its fusion model architecture is BERT+BI-LSTM+Multi-head-Self-Attention+FC. The current popular deep learning for Chinese character relationship extraction the model of has a better effect on the relationship extraction of a single character relationship pair. However, when it is extended to single-sentence multi-person relationship pairs and document-level semantic complexity, the evaluation index data of this model is not high. Through comparison, it is found that BERT has a significant improvement in the pre-training effect of the word2vec model, and the BI-LSTM+Multi-head-Self-Attention model has a significant improvement in the effect of a single LSTM model. Therefore, the proposed BERT+BI-LSTM +Multi-head-Self-Attention+ FC fusion model has certain practical value in Chinese character relationship extraction.
Secure Cloud Data Deduplication with Efficient Re-Encryption

After the emergence of the cloud architecture, many companies migrate their data from conventional storage i.e., on bare metal to the cloud storage. Since then huge amount of data was stored on cloud servers, which later resulted in redundancy of huge amount of data. Hence in this cloud world, many data de-duplication techniques has been widely used. Not only the redundancy but also made data more secure and privacy of the existing data were also increased. Some techniques got limitations and some have their own advantages based on the requirements. Some of the attributes like data privacy, tag regularity and interruption to brute-force attacks. To make data deduplication technique more efficient based on the requirements. This paper will discuss schemes that brace user-defined access control, by allowing the service provider to get information of the information owners. Thus our scheme eliminates redundancy of the data without breaching the privacy and security of clients that depends on service providers. Our lastest deduplication scheme after performing various algorithms resulted in conclusion and producing more efficient data confidentiality and tag consistency. This paper has discussion on various techniques and their drawbacks for the effectiveness of the deduplication.
Shadow Removal using Multi-Channel Binarization, Color-Line Clustering and Illumination Estimation

Shadows are a typical aspect of pictures and once left undiscovered will slow down scene understanding and visual processing. Existing methods adopted by researchers based on segmentation and deep learning lead to high processing time. In this paper, we have proposed a technique for shadow detection and removal which involves performing clustering using color-line identified by performing offset correction, and then estimation for illumination is carried out. After that, an automatic shadow detection method is used. Then, the shadow scale is estimated by the ratio of shadow-free areas. At last, illumination optimization is used to improve the shadow scale. Detection of shadows involves color space conversion and binarization using a threshold for different channels of an image. The output image will be further processed to remove noisy regions. The method used for shadow detection is automatic which overcomes the manual work of the user to select the shadow region and based on this selection, segmentation was performed which consumed more time. Comparatively, our approach for shadow detection is expeditious and thus reduces manual work.
Cyberdefence of Offshore Deepwater Drilling Rigs

Abstract An alternative methodology using new preventative technology to manage cybersecurity exposure on deepwater drilling rig assets is presented. For the past two years Shell's Deepwater Wells business has been evaluating typical cyber defence approaches and undertaken cybersecurity risk assessments and penetration tests. These activities have demonstrated the challenges attaining cybersecure drilling rig environments. Whilst cyberattacks increase in frequency, adaptability, and become cheaper to launch, regulatory and liability insurance requirements are also evolving. To achieve the goal of cyber-resilience, a major Operator has collaborated with a cybersecurity firm to trial technology for rapidly and reliably protecting deepwater rigs. The paper presents aspects of the numerous challenges faced and offers a different approach using new technology applied to both supplement and accelerate the attainment of a cyber-resilient environment onboard deepwater drilling rigs. It shares the deep dive lessons learnt leading to a more comprehensive understanding of how to protect drilling rigs and their safety critical control systems. Aside from addressing technical attributes using risk vs. maturity based methods, the approach also caters to business demands of short term rig contracts, managing multi-vendor legacy systems and satisfying increasing digitalisation/remote access needs with associated reductions in overall cybersecurity CAPEX spend.
Extended Belief Rule Base Parameter Optimization Based on Differential Evolution Algorithm

The extended belief rule base combines the characteristics of knowledge structure and evidence reasoning, which can quantitatively describe the uncertainty and incompleteness of data. However, the parameter selection of original model has a great impact on its performance. For example, the accuracy of classification is affected by interval division, and the parameter learning method needs to artificially set the number of rules. In response to the above problems, this paper proposes an extended belief rule base (EBRB) model optimized by differential evolution algorithm (DE), and applies this model to the health assessment of lithium-ion batteries. EBRB optimized based on differential evolution algorithm uses a data-driven method to transform the rule set into rules. It uses DE to train the initial parameters, and uses the test data set to test the effectiveness of the model. The comparison with traditional methods verifies that the effectiveness of the proposed method
Recent Developments in Practical QKD

Turning an abstract protocol into a practical device offers a variety of challenges. The main problem is that the devices used in the implementation such as optical fibres and single-photon detectors are never perfect. For instance, one has to take into account that photons get lost when sent though an optical fibre, especially over long distances. In this chapter, we discuss the challenges that arise when practically implementing QKD schemes. We furthermore present developments in QKD that aim at overcoming these practical challenges, such as measurement device-independent QKD and continuous-variable QKD, and give a brief overview of the state-of-the art experiments for several types of protocols.
Direction-Based Hybrid Strategy Combining Pushing and Hitting for Fast Object Singulation

This paper presents a hybrid singulation strategy for fast object singulation in a cluttered environment. Recent techniques related to object singulation in clutter have employed various kinds of pushing techniques and in some cases have also used hitting techniques. However, these techniques have not addressed the issue related to the direction of pushing and hitting which is vital for fast object singulation. Finding the appropriate direction of hitting and pushing helps in singulating objects quickly in a cluttered environment. This paper proposes the desired direction for pushing and hitting, combined with a hybrid strategy, that results in fast object singulation in a cluttered environment. The number of times of pushing and hitting in terms of time is chosen as the measure of performance. We employ multiple circular disks as the test example and carry out diverse experiments to corroborate the usefulness of the proposed object singulation algorithm. This approach is able to singulate objects quickly in complex formations. In this paper, we have combined both pushing and hitting and also proposed the direction of hitting and pushing in order to singulate objects in clutter quickly.
Generating Facial Character: A Systematic Method Accumulating Expressive Histories

Abstract The author presents a method to simulate facial character development by accumulating an expressive history onto a face. The model analytically combines facial features from Paul Ekman’s seven universal facial expressions using a simple Markov chain algorithm. The output is a series of 3D digital faces created in Blender with Python. The results show that systematically imprinting features from emotional expressions onto a neutral face transforms it into one with distinct character. This method could be applied to creative works that depend on character creation, ranging from figurative sculpture to game design, and allows the creator to incorporate chance into the creative process. The author demonstrates the method’s application to sculpture with ceramic casts of generated faces.
Toward Improving The Visual Characterization of Sport Activities With Abstracted Scene Graphs

We present techniques for abstracting relevant information from scene graph features to improve action recognition in sports videos. Feature representation with relevant information can dramatically increase machine learning’s utility across many tasks. Despite the advantages of incorporating objects and relations as building blocks of semantic information, we still encounter too many irrelevant objects and relations in sports videos, adding uncertainty to the classifiers. This paper describes four fundamentally different scene abstraction techniques, each searching for the relevant information within aggregated features from pixel-level to object-level. In each method, we formulate relevancy through co-occurrence statistics, semantic similarity, feature decomposition, and correlation-based mapping and evaluate each technique’s efficacy through performance gains in action recognition and decay rate of training loss. We demonstrate that by creating a relevant and more concise knowledge representation, we improve performance (mAP) of action recognition in sports by 26.6% and achieve faster converging models due to higher representation power.
Group LCD and group reversible LCD codes

In this paper, we give a new method for constructing LCD codes. We employ group rings and a well known map that sends group ring elements to a subring of the n×n matrices to obtain LCD codes. Our construction method guarantees that our LCD codes are also group codes, namely, the codes are ideals in a group ring. We show that with a certain condition on the group ring element v, one can construct non-trivial group LCD codes. Moreover, we also show that by adding more constraints on the group ring element v, one can construct group LCD codes that are reversible. We present many examples of binary group LCD codes of which some are optimal and group reversible LCD codes with different parameters.
Context-aware Adaptive Bitrate Streaming System

As video traffic volume increases, video streaming providers are struggling to improve the quality of experience (QoE). On the other hand, video viewers often prefer a lower traffic volume over a QoE that is too high since they contract for data-capped or pay-per-use communication plans. Thus, traffic volume should be reduced as much as possible while achieving the required QoE, which is the minimum QoE that will satisfy users. However, the required QoE depends on the context (e.g., the type of content and the preference of the user), and it is unrealistic and costly for users to set the required quality separately for each possible context. In this paper, we propose a context-aware adaptive bitrate streaming system that reduces the traffic volume while achieving the required QoE. Instead of requiring the required QoE to be configured by users, the proposed system uses the viewing time as implicit feedback on the QoE. By using this feedback, the proposed system automatically controls the QoE with a two-stage approach: it estimates the required QoE and calculates the bitrate to reduce the traffic volume while maintaining a QoE above the required QoE. To determine the required QoE based on few views, the proposed system searches for the required QoE in the mean opinion score space and uses Bayesian optimization. The results of trace-based simulations show that the proposed system can control the QoE close to the context-dependent required QoE based on fewer views than baseline algorithms.
OFNBee Method Applied for Solution of Problems with Multiple Extremes

There are plenty of algorithms whose operation is based on the behavior of bees. Currently, the most intensively developed concept is the ABC algorithm. Its chief asset is the ease of customization and fast operation. One of the older solutions is the MBO concept. However, due to its high complexity, the algorithm had limited application. The subject of this article is the OFNbee concept - it is based on the application of the selected arithmetic of the fuzzy number in the new bee swarm optimization method. This approach can improve the efficiency of the optimization algorithm.
An IB-PSO algorithm for unconstrained nonlinear programming problems

Particle swarm optimizer (PSO) is an efficient algorithm to find the best solution in engineering. However, the classical particle swarm optimization algorithm and the existing variational algorithm still have the problems of prematurity and slow convergence speed in the late iteration in different application problems. To make up the above demerits, we proposed a hybrid algorithm PSO to combine an improved PSO (IPSO) with an improved Broyden-Fletcher-Goldfarb-Shanno (BFGS) method. The hybrid algorithm introduced the convergence direction of the RL-BFGS to correct the evolution direction and enhance global search capability. And the initial point of the RL-BFGS is decided by the IPSO. For the sake of the effectiveness evaluation of IB-PSO, The performance of eight improved PSO algorithms was compared by optimizing six benchmark functions with different characteristics, such as single mode, multi-mode and rotation. The results show that compared with the other eight algorithms, the proposed IB-PSO has good performance in solving multimodal problems. Furthermore, the proposed approach can be used in engineering problems to obtain high-quality solutions.
Digital Video Watermrking: Aspects, Relevance, Challenges

In the existent years, with the evolution of internet the exchange of data has been increased over web and the digital content is being widely used. This constant barrage of data however useful also requires meticulous safety and security so as to save it from piracy and/or use by third party. Digital video watermarking is one of the widely used tools in regard to safekeeping of digital media dissemination and the same is identified as a differential mark, entrenched in a noise-resistant instruction secretly in a video. In this paper, a brief description is presented on different aspects of digital video watermarking such as characteristics of digital watermarking, the digital watermarking model, robustness evaluation as the after- effects of various image as well as attacks related to video like frame dropping, frame averaging and statistical analysis etc in watermarking. Finally, this paper emphasizes on the other aspects of digital video watermarks like kinds, security, techniques to implement it and a detailed literature survey is discussed in a tabular format which can provide the directions to upcoming researchers to pursue their research in this field. In other words, the papers identified the most prominent problems of digital video watermarking and present several state-of-the-art explanations with a focus on evaluation of security, efficiency, toughness, perceptibility and security of video multimedia object.
RCN2: Residual Capsule Network V2

Unlike Convolutional Neural Network (CNN), which works on the shift-invariance in image processing, Capsule Networks can understand hierarchical model relations in depth[1]. This aspect of Capsule Networks let them stand out even when models are enormous in size and have accuracy comparable to the CNNs, which are one-tenth of its size. The capsules in various capsule-based networks were cumbersome due to their intricate algorithm. Recent developments in the field of Capsule Networks have contributed to mitigating this problem. This paper focuses on bringing one of the Capsule Network, Residual Capsule Network (RCN) to a comparable size to modern CNNs and thus restating the importance of Capsule Networks. In this paper, Residual Capsule Network V2 (RCN2) is proposed as an efficient and finer version of RCN with a size of 1.95 M parameters and an accuracy of 85.12% for the CIFAR-10 dataset.
A Benchmark of Objective Quality Metrics for HLG-Based HDR/WCG Image Coding

In this work, we study the suitability of high dynamic range, wide color gamut (HDR/WCG) objective quality metrics to assess the perceived deterioration of compressed images encoded using the Hybrid Log-Gamma (HLG) method, which is the standard for HDR television. — Several image quality metrics have been developed to deal specifically with HDR content, although in previous work we showed that the best results (i.e., better matches to the opinion of human expert observers) are obtained by an HDR metric that consists simply in applying a given standard dynamic range metric, called visual information fidelity (VIF), directly to HLG-encoded images. — However, all these HDR metrics ignore the chroma components for their calculations, i.e., they just consider the luminance channel. For this reason, in the current work, we conduct subjective evaluation experiments in a professional setting using compressed HDR/WCG images encoded with HLG and analyze the ability of the best HDR metric to detect perceivable distortions in the chroma components, as well as the suitability of popular color metrics (including ΔITP <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">R</sub> , which supports parameters for HLG) to correlate with the opinion scores. Our first contribution is to show that there is a need to consider the chroma components in HDR metrics, as there are color distortions that subjects perceive but that the best HDR metric fails to detect. Our second contribution is the surprising result that VIF, which utilizes only the luminance channel, correlates much better with the subjective evaluation scores than the metrics that do consider the color components.
An aggregate approach to diachronic variation in modern Chinese writings and translations

Abstract Modern Chinese took the place of classical Chinese and has been the standard form of writing since the early 1920s. While several studies have been carried out on diachronic variation in modern written Chinese, these include few aggregate investigations. This study examines the diachronic variation in modern Chinese writings and translations from the 1900s to the 2000s. Frequencies of multiple linguistic features sensitive to historical change were drawn from a multi-genre comparable corpus, ‘DCMCWT’, containing five periods: 1900–1911, 1919–1930, 1931–1949, 1950–1966, and 1978–2012. Hierarchical agglomerative clustering was employed for periodisation, while multidimensional scaling supplemented the developmental path. The results suggest that Chinese writings and translations fall into three broad periods: 1900–1911, 1919–1966, and 1978–2012. Chinese translations follow a similar evolutionary path as the writings, and the gap between them, narrowed from 1900 to 2012. This developmental path corresponds to the socio-historical backgrounds in Chinese history and shares similarities and differences with the development of English. Diachronic variation in early modern Chinese mirrors that of English in that both languages developed to be more colloquial and interactive. However, early modern Chinese is different from English, as diglossia has played a crucial evolutionary role.
Cyberlaw and cyberspace <i>vis-a-vis</i> impact of internet during COVID-19 pandemic

Purpose We live in a techno savvy era that is called as virtual connection and communication. Design/methodology/approach During this pandemic crisis, we all are dependent upon technology, internet for connecting to our families and our friends; this all is monitored by cyberlaw. Findings Today, the computer crimes are increasing at a high rate, and it should be controlled and managed to maintain the platform .Cyberspace on the other hand can be discussed as the space where all internet-related activities are taking place and cyberlaw regulates. The paper will throw light on the impact of internet on the COVID-19 pandemic. Originality/value The paper will throw light on the impact of internet on the COVID-19 pandemic.
Multi-sensor image matching method based on phase congruency

In order to solve the current research situation of high difficulty and low accuracy in multi-sensor image (optical image and SAR image) registration, this paper proposes a multi-sensor image registration method based on phase congruency. In this paper, we analyze the key points and difficulties of multi-sensor image matching, and point out the limitations and instability of some current algorithms. An existing algorithm based on gradients and vectors has been improved to enhance its stability and accuracy. On the basis of rough matching, a matching algorithm based on phase congruency is proposed to achieve accurate matching between multi-sensor images. Finally, the effectiveness of the method proposed in this paper is validated through case study.