Reviewerly/exHarmony huggingface dataset:
Security Data Sharing of Shipbuilding Information Based on Blockchain

The shipbuilding industry has problems such as long transaction cycles, many participating suppliers, and data sensitivity.A time-dimensional shipbuilding information security sharing scheme based on an alliance chain is proposed to solve this problem.The blockchain can better deliver value and protect user privacy, the blockchain can directly complete the instant transfer of value through smart contracts and tokens on the blockchain.We divide the program into three parts: data preprocessing, data storage, and data sharing.For data sensitivity, data confidentiality and reliability are handled separately.Aiming at the problem of user privacy leakage in data storage, a privacy-protecting blockchain shared storage solution is proposed; for deduplication, two deduplication algorithms between storage nodes and within storage alliances are proposed to ensure Data information is not disclosed.Storage nodes are allowed to perform data deduplication, data sharing and down-loading, and use off-chain transactions to solve the blockchain performance problems caused by frequent transactions.The scheme in this paper carries out theoretical analysis from three aspects of correctness, security and performance.Finally, this paper has carried out a comparative experimental analysis on the scheme, and the experimental results show that the proposed scheme is feasible and efficient.
Flexible Light Field Angular Superresolution via a Deep Coarse-to-Fine Framework

Acquisition of densely-sampled light fields (LFs) is challenging. In this paper, we develop a coarse-to-fine light field angular superresolution that reconstructs densely-sampled LFs from sparsely-sampled ones. Unlike most of other methods, which are limited by the regularity of sampling patterns, our method can flexibly deal with different scale factors with one model. Specifically, a coarse restoration on epipolar plane images (EPIs) with arbitrary angular resolution is performed and then a refinement with 3D convolutional neural networks (CNNs) on stacked EPIs. The subaperture images in LFs are synthesized first horizontally, then vertically, forming a pseudo 4DCNN. In addition, our method can handle large baseline light field without using geometry information, which means it is not constrained by Lambertian assumption. Experimental results over various light field datasets including large baseline LFs demonstrate the significant superiority of our method when compared with state-of-the-art ones.
Importance-Practicability Analysis of NCS-based Vocational Skills among Students in Beauty Specialized High School

Purpose: This study aims to strengthen job competency that is effectively used in the beauty industry. By analyzing the importance and performance level of the required basic job skills as per the national competency standards (NCS) of high school students specializing in beauty, job competency can be improved.Methods: A survey was conducted on 158 students enrolled in a specialized beauty high school in Seoul from March 22 to April 20, 2021. Frequency, factor, reliability, correlation, and simple regression analyses were performed. This study also used the importance–practicability analysis (IPA) matrix of basic vocational competency.Result: The IPA analysis results reveal that organizational understanding needs improvement. Moreover, as sub-areas, human resource capability, conflict management, negotiation, and technology application ability were identified, further confirming the urgent need for improvement. In addition, intense investment in this area is required. The higher the perceived social self-efficacy, the better information is provided with the help of others. Moreover, students’ ability to prepare for job search is greater, and the effect on organizational understanding is positive.Conclusion: Previous studies posited that the self-efficacy of university students has a positive effect on organizational understanding ability and confirmed that using the flipped learning teaching method improves the self-efficacy. The use of flipped learning in beauty as a subject, where theory and practical classes are mixed, and self-efficacy is used as a medium to strengthen organizational understanding, it is expected that it will help high school students specializing in beauty to adapt to the industry.
S–V

A method used in →cryptography that is intended to provide additional security for the →hash values. Since random characters are added to the hash calculation it is known as “salting the hash”.
Short-time coherence of a qubit and measurement apparatus

The effects of the measurement apparatus on quantum coherence are studied by considering a purely dephasing model of a qubit. The initial state is prepared from a thermal state of the whole system by performing a nonselective measurement on the qubit. The magnitude of the initial postmeasurement coherence is bounded by the value [Formula: see text], which is realized with special measurement schemes and in the low-temperature limit. The coherence magnitude identically vanishes, increases or decreases with an approximately constant rate over a determined short-time scale, according to the choice of the preparation measurement. The maximization of the short-time increasing or decreasing rate is favored by the choice of other special measurement schemes and the high-temperature limit. The measurement apparatus allows to manipulate the quantum coherence of the qubit over short times via nonselective preparation measurements.
Privacy Protection of Task in Crowdsourcing: Policy-Hiding and Attribute Updating Attribute-Based Access Control Based on Blockchain

Crowdsourcing is a new way to solve complex problems by using human intelligence. However, the tasks and user information privacy concerned in crowdsourcing have not been adequately addressed. It is necessary to design a privacy protection mechanism for tasks that need to be restricted to specific user groups. Ciphertext-policy attribute-based encryption (CP-ABE) is an efficient and feasible cryptographic tool, particularly for crowdsourcing systems. The encryptor can choose the access policy independently, which limits the scope of decryption users. At present, most CP-ABE schemes adopt a centralized management platform, which poses problems such as high trust-building costs, DDoS attacks, and single point of failure. In this paper, we propose a new access control scheme based on CP-ABE and blockchain, which has the properties of policy hiding and attribute updating. To protect the privacy of worker’s attributes, we adopt a test algorithm based on a fully homomorphic cryptosystem to confidentially judge whether the worker’s attribute lists match the hidden attributes policy in ciphertext or not before the decryption. Experiment results and comprehensive comparisons show that our mechanism is more flexible, private, and scalable than existing schemes.
Application of Computer Multimedia Technology in English Curriculum Design

With the continuous development of social informatization, how to make good use of computer multimedia for English curriculum education is a topic of concern and discussion. In actual teaching, it is a very important and effective method for teachers to share and communicate information resources through the use of modern networks. Therefore, this article studies the application of multimedia technology in English curriculum design, which is intended to deepen the understanding and mastery of multimedia teaching and improve the ability of English teaching. This article mainly uses case analysis method, comparative method and investigation method to discuss and analyze the application of multimedia technology in English teaching. The survey results show that 62% of students like to learn short videos in oral English teaching, imitating the authentic pronunciation of foreigners. Therefore, multimedia teaching is very popular.
LSS target tracking algorithm based on optical flow detection and polynomial fitting relocation

It’s difficult to detect LSS(Low-Small-Slow) target because of its overturning, distortion during movement. Aiming at this problem, this paper proposes a LSS target tracking algorithm based on optical flow detection and polynomial fitting relocation. Firstly, target is detected by optical flow method, and then the SVM classifier trained with hog feature and color histogram feature is used to eliminate the false target. Then use TBD strategy to process the target information in the first three frames to confirm the initial location of the target. In follow-up tracking process, the obtained target information is compared with polynomial fitting results to determine whether to trigger the relocation mechanism. In relocation mechanism, the hot spot region will be generated according to the polynomial fitting results, and the salient features of the hot spot region will be extracted to determine the target location. Through the performance on the public data set LaSOT and the image sequence collected by the author, the algorithm in this paper is insensitive to the maneuver and distortion of LSS targets, and the tracking effect is stable. Under certain constraints, the tracking accuracy of the algorithm can reach more than 96%, which has strong application value.
Adversarial attack method against image classification based on haze perturbation

The adversarial attack is a cutting-edge technique used to study the vulnerability of deep neural networks (DNNs). However, most existing studies focus on the additive perturbation-based attack, which cannot represent real-world corruption and limit their applications. In particular, haze is a common natural phenomenon that significantly corrupts an image, which inevitably poses a potential threat to deep models. In this work, for the first attempt, we study the effects of haze on DNNs from the perspective of adversarial attacks and propose two adversarial haze attack methods. We first propose the optimization-based adversarial haze attack (OAdvHaze) that optimizes the parameters of the atmospheric scattering model with the guidance of a DNN to synthesize a hazy image, which leads to a high attack success rate. To achieve a more efficient attack, we further propose a predictive adversarial haze attack (PAdvHaze) employing a DNN to predict the hazing parameters through a one-step way. To validate the effectiveness of both methods, we conducted extensive experiments on two publicly available datasets, i.e., ILSVRC 2012 and NIPS 2017. OAdvHaze and PAdvHaze achieve comparable attack success rates and transferability to state-of-the-art attacks. This work would contribute to the evaluation and enhancement of the robustness of DNNs against haze perturbation that may happen in the real world.
Transfer Learning on Knowledge Graph Construction: A Case Study of Investigating Gas-Mining Risk Report

This study addressed the problem of automated Knowledge Graph (KG) construction from unstructured documents, with the assistance of transfer learning. Despite a large amount of effort made to discover KG, how to explore unknown KGs from existing knowledge remains a challenge. In this paper, we firstly formulate the KG detection process as a transfer-learning problem, which consists of two main steps. At first, we pretrain a backbone model using the source domain. Due to sufficient samples from the source domain, this backbone model can be trained better. Second, we migrate this model (from the known domain) to the target domain by fine-tuning key parameters. The fine-tuning operation only requires less computation, which is very efficient. As such, the backbone model can be successfully transferred into the target domain, even with limit training samples. Experimental evaluations using one real-world dataset of gas-mining reports demonstrate the advantages of utilizing the proposed algorithm to construct KG using transferable information.
Image Inpainting Based on Edge Features and Attention Mechanism

Image inpainting as a kind important application in our life and entertainment, it also is a popular task of computer vision. The latest deep learning-based approaches have shown promising results for the challenging task of inpainting damaged regions of an image. However, there are still structural differences between the restored images and the ground truth images. Aiming at this problem, we propose a model of image inpainting called ECF-Net, ECF-Net incorporates edge information into the process of image inpainting to help damaged images to obtain more structures similar to the ground truth images, which to guide the generation of the feature of the damaged area. At the same time, we introduce the knowledge consistency attention mechanism in ECF-Net, which can obtain more reasonable semantics to eliminate blurs for image inpainting. Extensive experiments on various datasets such as CelebA-HQ, Places2 and the Paris StreetView clearly demonstrate that our method gets a better performance in vision.
Hybrid Training Strategies: Improving Performance of Temporal Difference Learning in Board Games

Temporal difference (TD) learning is a well-known approach for training automated players in board games with a limited number of potential states through autonomous play. Because of its directness, TD learning has become widespread, but certain critical difficulties must be solved in order for it to be effective. It is impractical to train an artificial intelligence (AI) agent against a random player since it takes millions of games for the agent to learn to play intelligently. Training the agent against a methodical player, on the other hand, is not an option owing to a lack of exploration. This article describes and examines a variety of hybrid training procedures for a TD-based automated player that combines randomness with specified plays in a predetermined ratio. We provide simulation results for the famous tic-tac-toe and Connect-4 board games, in which one of the studied training strategies significantly surpasses the other options. On average, it takes fewer than 100,000 games of training for an agent taught using this approach to act as a flawless player in tic-tac-toe.
Classification of Daily Lifestyle Based on Body Activities Obtained from Wearable Devices

This paper presented a lifestyle analytic of daily routine data from wearable devices. Individual lifestyle is classified into light active, moderate active and active group. The data collected from three wearable devices which were Xiaomi band, Huawei wear and Fitbit for three to nine months. The data are then been processed and classified into daily lifestyle group using three classification technique i.e., k-Nearest Neighbor (k-NN), Naïve Bayes and support vector machine (SVM). Through the experiments, it is found that k-NN achieved 98.37%, the highest accuracy among the three classifiers. Nevertheless, the result of the classification can be improved by adding more data into training in the machine learning algorithms.
Efficient table border segmentation with asymmetric convolutions

Automatic table understanding in document images is one of the most challenging topics in the research community. This is owing to the fact that tables may appear in various structures and designs. However, a big majority of tables are designed with ruling lines. Recognizing these lines in images is mandatory in numerous table understanding processes. Previous works have utilized hand-crafted features, merely applicable to distortion-free images. We present a compact CNN as an alternative solution. This method is capable of segmenting the ruling lines in challenging environments. In addition to the proposed architecture, a new dataset is generated for this task that contains 35K labeled samples. The reported results on this dataset show the effectiveness of this method. Our implementation and dataset are available online.
Using Conceptual Recurrence and Consistency Metrics for Topic Segmentation in Debate

We propose a topic segmentation model, CSseg (Conceptual Similarity-segmenter), for debates based on conceptual recurrence and debate consistency metrics. We research whether the conceptual similarity of conceptual recurrence and debate consistency metrics relate to topic segmentation. Conceptual similarity is a similarity between utterances in conceptual recurrence analysis, and debate consistency metrics represent the internal coherence properties that maintain the debate topic in interactions between participants. Based on the research question, CSseg segments transcripts by applying similarity cohesion methods based on conceptual similarities; the topic segmentation is affected by applying weights to conceptual similarities having debate internal consistency properties, including other-continuity, self-continuity, chains of arguments and counterarguments, and the topic guide of moderator. CSseg provides a user-driven topic segmentation by allowing the user to adjust the weights of the similarity cohesion methods and debate consistency metrics. It takes an approach that alleviates the problem whereby each person judges the topic segments differently in debates and multi-party discourse. We implemented the prototype of CSseg by utilizing the Korean TV debate program MBC 100-Minute Debate and analyzed the results by use cases. We compared CSseg and a previous model LCseg (Lexical Cohesion-segmenter) with the evaluation metrics Pk and WD. CSseg had greater performance than LCseg in debates.
Digital Literacy Curriculum Work Program for Primary General Education

The program reveals the goals of mastering primary digital literacy by students in primary school, characterizes digital skills and determines the place of the digital literacy course in the curriculum, reveals the main approaches to the selection of course content and thematic planning, taking into account the hours chosen by the educational organization.
Comparison of Different Processing Methods of Joint Coordinates Features for Gesture Recognition with a RNN in the MSRC-12

In this paper we present an application of a recurrent neural network (RNN) to recognize gestures of Microsoft Research Cambridge 12 (MSRC-12) dataset. Three different processing methods of joint coordinates are used in the artificial neural network, our objective is to specify which method results in a more accurate network. The MSRC-12 dataset is captured by the Kinect sensor, it consists of a sequence of human body articulation movements. In addition, the FastDTW algorithm is employed to normalize the data frames number. The three different methods proposed in this paper are: the 3D coordinates method, the subtraction method, and the normalization method. These three methods are used in a RNN model, and we obtained with 3D coordinates method an accuracy rate of 87,30%, using subtraction method the ac-curacy rate is 87,11% and with the normalization method the accuracy rate obtained is 89,14%.
The role and importance of information from open sources in increasing vulnerability to security threats in cyberspace, with particular reference to cyberterrorism

The aim of the article is to present how the role and importance of open source information has increased in recent years as a significant part of the functioning of societies has moved to the world of Internet. Unfortunately, along with this trend, new threats have emerged, including those of a cyberterrorist nature, which require immediate action to limit their impact on information security.
Parts of Speech Tagging in NLP- an Investigation on Runtime Optimization with Quantum Formulation and ZX Calculus

This paper presents an optimized formulation of the parts of speech tagging in Natural Language Processing (NLP) with a quantum computing approach, and it further demonstrates the quantum gate-level runnable optimization with ZX-calculus, keeping the implementation target in the context of Noisy Intermediate Scale Quantum Systems (NISQ). The discussed quantum formulation exhibits quadratic speed up over the classical counterpart and further demonstrates the implementable optimization with the help of ZX calculus postulates.
Protecting Check-In Data Privacy in Blockchain Transactions with Preserving High Trajectory Pattern Utility

Because the blockchain is secure and untamperable, it has been widely used in many industries, such as the financial industry, digital tokens, and e-commerce logistics. The remarkable security feature of the blockchain is that the blockchain verifies the transaction initiated on each block through the node, and its process is broadcast throughout the whole network to let everyone know. On the one hand, this ensures the security of every transaction, but on the other hand, it is easy to cause privacy disclosure problems for transaction users. Therefore, under the premise of ensuring the security of the blockchain, it has become a hot issue to protect the sensitive information of transaction users. A check-in privacy protection (CPP) algorithm based on check-in location generalization is proposed in this paper, which can be applied to blockchain transactions to solve the privacy leakage problem of transaction users’ sensitive information. CPP algorithm not only protects the privacy of check-in data but also keeps the high utility of trajectory pattern data. Firstly, location types are recommended in the sensitive check-in location generalization based on the user’s trajectory pattern by using Markov chain technology. Secondly, to make sure that the generalized locations can be scattered as much as possible to prevent the attacker from deducing back, a heuristic rule is designed to select the generalized location based on the recommended location types, and at the same time, the similarity between the anonymous trajectory and the original trajectory is maintained. In addition, a generalized location search strategy is designed to improve the efficiency of the algorithm. Based on the real spatial-temporal check-in data, the results of the experiment indicate that our algorithm can effectively protect the privacy of sensitive check-in while ensuring the high utility of trajectory pattern data.
Multi-hop reading comprehension over Wikipedia

In open-domain question answering, especially in multi-hop question answering, it is important to extract clues related to the question from the huge knowledge base for the next-hop reasoning. Most of the current question answering methods do not make full use of the knowledge base to retrieve clues. To answer complex questions more accurately, in this article, we first extract the clue paragraphs related to the problem from the Wikipedia knowledge base to construct the representation of a fine-grained node. And then we perform multi-hop inference on the structure graph based on these nodes. An elaborate evaluation was conducted based on two public data sets. The proposed model can extract more answers and clues related to the question by importing an external knowledge base and achieves higher accuracy than the benchmark method on multi-hop reasoning tasks in joint answer predicting tasks.
Modelling of suspension transportation and sedimentation in backwater flow

Deposition of suspended solids in hydraulic plants like reservoirs, retarding structures or river basins produces often a considerable silting up that has to be controled from the navigational as well as the water quality point of view. The proper deals with the suspended load transportation in gradually varied flow by use of one and two-dimensional width averaged models.