Reviewerly/exHarmony huggingface dataset:
Design of Urban Rail Transit Service Network Platform Based on Genetic Algorithm

In view of the problems existing in the current research and development of online course platform, such as the lack of humanization and personalization, the lack of high relevance of course related modules, and the lack of integration of related theories, this paper takes the urban rail transit service English as an example, studies the design of online course platform based on genetic algorithm, and deeply discusses the integration framework of knowledge management and online course. The prototype system well reflects the knowledge management of system level and individual level.
Risk Faktörlerinin Dağıtık Yazılım Ekip Yapısına Etkisi

The distributed software team structure represents a development type ensuring that team members are located in different remote sites through the life cycle of the software. Hence, it forms a network of distant sub-teams. Nowadays, the development of software projects with geographically distant groups forces software development environments to be flexible. It is of great importance for these software managers to organize and manage their distributed team management well. A higher number of stakeholders are brought to the scene of software projects by the trend of the distributed teams throughout countries and zones. Whereas risk management is the most significant assurance to obtain the project’s expected outcome, the said processes must be considered as the shared responsibilities and commitment of the stakeholders taking part in the project from each site. The development of software ‘in-house’ leads to a number of risks. At the same time, its development partially in another geography causes the exponential multiplication of the risks in question. The management of risks must start at the early project stages and proceed in the form of a monitoring and follow-up effort during the projects’ life cycle. In scope of this paper, the process steps developed to be carried out in accordance with Capability Maturity Model Integration (CMMI), instead of the traditional project method approach of the main risk factors in the distributed team structure are explained in detail. Also, the current study aimed to reveal the components taking part in the process of risk management regarding distributed software development.
Bare Bones Particle Swarms With Linear Variable Weight

In order to improve the ability of BBPSO to jump out of the local optimal solution and avoid it falling into the local optimal solution, this paper proposes the BBPSO with linear variable weights. First, a linearly changing adjustment factor β is introduced into the iterative formula to balance the exploration and exploitation. Secondly, the scale factor D is introduced for the fast convergence of BBPSO, so that the adjustment factor will decrease to its minimum in the first half of the iterative process. The experimental results show that BBPSO with linear variable weight has better global optimality and can effectively balance the exploration and exploitation.
A Fast Map Construction Method for Indoor Mobile Robot

In order to make the indoor robot can reach each area of the room quickly, a new map which is made up of main channels is proposed for robot indoor. Firstly, the barrier-free area is marked as a regular passable area by scanning the indoor environment. Secondly, a multi-objective optimization model is constructed according to the standard of the main channel. Thirdly, a satisfactory solution is obtained. Finally, an example is given to illustrate the method. Compared with the previous method of map construction, the new channel map enables the mobile robot to reach the target area quickly, efficiently and satisfactorily to a certain extent in the indoor environment.
On Forging SPHINCS$$^{+}$$-Haraka Signatures on a Fault-Tolerant Quantum Computer

SPHINCS\(^{+}\) is a state-of-the-art hash based signature scheme, the security of which is either based on SHA-256, SHAKE-256 or on the Haraka hash function. In this work, we perform an in-depth analysis of how the hash functions are embedded into SPHINCS\(^{+}\) and how the quantum pre-image resistance impacts the security of the signature scheme. Subsequently, we evaluate the cost of implementing Grover's quantum search algorithm to find a pre-image that admits a universal forgery.
A study on LG content lock and data acquisition from apps based on content lock function

During digital forensics investigations, smartphone application data are an important target, because they store personal user data, such as memos, images, and videos. Some applications use data hiding or encryption to protect application data, including personal user information. While these methods are excellent for data protection, they act as anti-forensics in digital forensic investigations. The LG smartphone provides Content Lock as a system application to protect the privacy of the user's memo and multimedia files. Content locked by Content Lock can only be accessed by entering the password specified by the user. In this paper, we identified the password verification process of Content Lock using reverse engineering, and recovery of the password input by the user. The original data in the locked file were acquired by analyzing two applications, QuickMemo+ and Gallery, that use Content Lock. No special data were required to obtain the original data. Our research enabled us to obtain original data hidden or encrypted by system apps on LG smartphones. Our research suggests that it is possible to obtain original data hidden or encrypted by system apps on LG smartphones.
Topological Anderson Localization Transition in Time-Multiplexed Quantum Walks

The interplay between disorder and topology triggers interesting effects for the propagation of quantum particles in low-dimensional network structures. For instance, the occurrence of Anderson localization, a commonly observed effect in a quantum network with static disorder, can be suppressed by introducing topology-a phenomenon dubbed as topological Anderson localization transition [1] . In one-dimensional systems, for example, disorder induces localization on a short length scale while topology counters that through long-range correlations, yielding divergent correlation length and extremely slow spreading at quantum criticality. However, its experimental demonstration still remains an outstanding challenge as that would require, in contrast to Anderson localization transition, addi¬tional control over internal degrees of freedom, which is hard to implement, for instance, in cold-atom systems [2] . Quantum walks (QWs), a quantum analog of random walks, comprising quantum coin toss and conditioned step operations are already established for studying quantum transport phenomena, such as, disorder and topology [5] . Based on our theoretical proposal [3] , here, we provide an experimental route using time-multiplexed quantum walk [4] for evidencing the targeted phenomenon.
Communication Design Playing a Role in Social Innovation

Design for social innovation aims at solving community problems by recombining available resources, creating new social relationships, and strengthening stakeholder networks. Social innovation presents itself as one of the most promising proposals for responding to systemic problems that manifest themselves both at a global and local level, and the use of design methodologies, tools, and skills enables social innovation to become more resilient and sustainable. Communication design is a particularly relevant area in this context, as it meets the needs for visibility, visual materialization, creation of future scenarios, dissemination, and replication. Through an in-depth case study of three social innovation initiatives, we collected evidence regarding different roles that design is called upon, although they are not exclusive to communication design. Some of the main roles relate to triggering the initiatives themselves as well as the social debate that sustains them, facilitating processes of ideation and co-creation, mediating actors and prototyping products, services or events.
Unsupervised Cross-modality Cardiac Image Segmentation via Disentangled Representation Learning and Consistency Regularization

Deep neural networks based approaches for medical image segmentation rely heavily on the availability of large amount of annotated data, which sometimes is difficult to obtain due to time, logistic effort and the requirement of expertise knowledge. Unpaired image translation enables a cross-modality segmentation network to be trained in an annotation-poor target domain by leveraging an annotation-rich source domain but most existing methods separate the image translation stage from the image segmentation stage and are not trained end-to-end. In this paper, we propose an end-to-end unsupervised cross-modality cardiac image segmentation method, taking advantage of diverse image translation via disentangled representation learning and consistency regularization in one network. Different from learning one-to-one mapping, our method characterizes the complex relationship between domains as many-to-many mapping. A novel diverse inter-domain semantic consistency loss is then proposed to regularize the cross-modality segmentation process. We additionally introduce an intra-domain semantic consistency loss to encourage the segmentation consistency between the original input and the image after cross-cycle reconstruction. We conduct comprehensive experiments on two publicly available datasets to evaluate the effectiveness of the proposed method. The experimental results demonstrate the efficacy of the present approach.
Topic Mining of Modern Poetry For Digital Humanities: Case Study of Gu Cheng Poetry

Using topic extraction and visualization method to explore the themes of Gu Cheng's complete poems, which provides a reference for the quantitative interpretation and analysis of modern poetry works. According to Gu's self-staging, we divide Gu's complete poems, and use Latent Dirichlet Allocation (LDA) model and word cloud visualization technology to conduct staged topic extraction and visual analysis of Gu's poems. Finally, we divide Gu's poems creation into four stages of nature stage, culture stage, counter-culture stage, and non-self stage. On the staged topic extraction experiment of Gu's poems, we obtain four stages of topic extraction results and word frequency cloud maps, and in-depth analyze the theme changes and reasons of Gu's poems at different stages. Results show that the used topic extraction and visualization method could objectively and comprehensively reveal the content and change process of Gu's poems.
A new method of image classification with photography composition

The Search Engines aim to provide the retrieval results that satisfy users, and the users will benefit from the search results that have been classified. This paper proposes a new image classification algorithm for organizing photos based on the rules of photography aesthetic. The main idea comes from the principles of photographic composition. The subjects in the photo are identified with the image sharpness evaluation algorithm. Then, the composition patterns frequently used by photographers are summarized and the images are classified according to their photographic composition. The experiments are designed to validate the the feasibility and effectiveness of the proposed algorithm. The experimental results show that the proposed scheme is consistent with the human vision system and subjective judgement.
영어 학습자의 자율성 연구에 대한 고찰

This study aims to review and analyze research articles on English language learner autonomy in academic journals on English language teaching published in South Korea in recent 20 years. A total of 90 articles were analyzed according to research periods, participants, methods, and themes. The results showed that the number of learner autonomy research has increased since 1998. The favored research participants were college students followed by elementary, middle school and high school students. Recently more studies were conducted with mixed level students, teachers, and experts. Mixed and quantitative methods were evenly dominant research methods, but mixed methods have been more popular in recent years. In terms of research themes, teaching and learning methods appeared most often, while there emerged a variety of themes such as correlations among variables related to self-directed learning ability, textbook analysis and development of instructional models as well as the concept of learner autonomy, learners’, parents’, and teachers’ perceptions of learner autonomy. Based on the findings, important pedagogical implications and future directions for research regarding English learner autonomy were suggested.
2 photons interference in twin images

Among the whole experiments using entangled pairs of photons, the famous experiment of two-photon interference known now as Hong-Ou-Mandel (HOM) interference, is probably one of the most fascinating. This ground-breaking experiment paved the way for a multitude of experiments showing the richness of the quantum properties of Spontaneous Parametric Down conversion (SPDC) and their applications to original communication protocols. Most of them use the coherence time property of a bi-photon state and measurement are mainly performed with bucket detectors and coincidence counters. While some studies report the spatial coherence of two-photon state, measurements are always performed in the temporal domain.
Constructing Financial Information Security Model Based on PCA and Optimized BP Neural Network

In recent years, with the rapid development of 5G technology, computer technology, communication technology, and the financial industry, people have paid more and more attention to financial information security issues. This article aims to use PCA technology and optimized BP neural network, and merge the two together to build a financial information security model and conduct in-depth research. This paper proposes the PCA technology, the principal component analysis technology, which uses the concept of dimensionality reduction to convert multiple indexes into partially integrated indexes. It is a data set simplification technology and the reverse propagation algorithm training according to a certain error. The BP neural network with many levels and multiple feedbacks carried out a financial information security model simulation experiment, and finally obtained experimental data to support the model. The experimental results of this article show that in 2020, my country has become the country with the highest network coverage in the world, reaching 64.3%, and the number of denizens has reached 632 million. In this experiment, there are a total of 13,896 pieces of useful data, including 4 types. Therefore, current research attaches great importance to the field of financial information security.
Discrete Two-Step Cross-Modal Hashing through the Exploitation of Pairwise Relations

The cross-modal hashing method can map heterogeneous multimodal data into a compact binary code that preserves semantic similarity, which can significantly enhance the convenience of cross-modal retrieval. However, the currently available supervised cross-modal hashing methods generally only factorize the label matrix and do not fully exploit the supervised information. Furthermore, these methods often only use one-directional mapping, which results in an unstable hash learning process. To address these problems, we propose a new supervised cross-modal hash learning method called Discrete Two-step Cross-modal Hashing (DTCH) through the exploitation of pairwise relations. Specifically, this method fully exploits the pairwise similarity relations contained in the supervision information: for the label matrix, the hash learning process is stabilized by combining matrix factorization and label regression; for the pairwise similarity matrix, a semirelaxed and semidiscrete strategy is adopted to potentially reduce the cumulative quantization errors while improving the retrieval efficiency and accuracy. The approach further combines an exploration of fine-grained features in the objective function with a novel out-of-sample extension strategy to enable the implicit preservation of consistency between the different modal distributions of samples and the pairwise similarity relations. The superiority of our method was verified through extensive experiments using two widely used datasets.
Efficient Parallel Algorithms for String Comparison

The longest common subsequence (LCS) problem on a pair of strings is a classical problem in string algorithms. Its extension, the semi-local LCS problem, provides a more detailed comparison of the input strings, without any increase in asymptotic running time. Several semi-local LCS algorithms have been proposed previously; however, to the best of our knowledge, none have yet been implemented. In this paper, we explore a new hybrid approach to the semi-local LCS problem. We also propose a novel bit-parallel LCS algorithm. In the experimental part of the paper, we present an implementation of several existing and new parallel LCS algorithms and evaluate their performance.
Multi-view Human Motion Analysis based on Sequential Multi-objective Particle Swarm Optimization

Human motion analysis from multi-view camera is a challenging problem in computer vision. In this paper, multi-view human motion analysis is formulated as a high dimensional constrained multi-objective optimization problem. A novel generative method called Sequential Multi-objective Particle Swarm Optimization algorithm (SMOPSO) is proposed for motion analysis. Firstly, nonlinear dimensionality reduction method is used to learn the low-dimensional latent space of pose state. Pose optimizing is performed in this latent space. Secondly, multi-objective optimization framework of human motion analysis is built, bi-directional silhouette based likelihood function for each camera view is designed, and MOPSO is used for pose optimization. Thirdly, in order to make MOPSO suitable for motion tracking, Sequential MOPSO is proposed by incorporating the temporal continuity information into the traditional MOPSO. Experimental results show that this method achieves accurate and stable tracking of 3D human motion.
Cbow Training Time and Accuracy Optimization Using SkipGram

Most word embedding techniques get their theoretical foundation from distributional semantics theory. They have been among the most popular trends of natural language processing for the last two decades. They have a large range of application. The present paper presents an overview of recent word embedding techniques. Furthermore, it proposes an optimized continuous bag of word (Cbow) model. The experiments we conducted show that the proposed approach outperforms the classic Cbow technique in terms of accuracy and training time.
Facial Mask Detection Using Deep Learning and Machine Learning Approaches

The COVID-19 pandemic is destructing the mankind. Until the discovery of the vaccine, precautionary measures have to be taken. The protection method that is effective is covering with a face mask in the areas where most of the people move around. The important utility is face masks in eliminating the COVID-19. In this paper, deep learning and machine learning are used for the detection of face masks. There are two components in the proposed work. The first one is the extraction of features and the second is the classification process of face masks by using Support Vector Machine (SVM). Real-world Masked Face Datasets (RMFD) have been selected for experimentation. The SVM classifier achieved an accuracy of 95.31% when tested.
Teaching Quality Evaluation of “Data Structure” Courses Based on Principal Component Analysis and Support Vector Machine

In order to obtain a higher-precision “data structure” teaching quality evaluation result, according to the characteristics of the “data structure” teaching quality evaluation. A “data structure” teaching quality evaluation model based on the combination of principal component analysis and support vector machine is designed and applied to specific examples. The experimental results confirm that the established model has obtained high-precision evaluation results in the “data structure” teaching quality evaluation level, which can provide valuable information for improving the teaching quality evaluation of “data structure” and related courses.
An efficient framework for classifying the Clothing items based on fashion and fabric of the images

In recent days classification of images over fashion domain has become fundamental research problem with lot of computer vision based applications. In most of the existing research image classification is done based on the labels on the objects, however in the real world scenarios the images needs to classified into the labels based on the domain specific with proper guidelines, therefore the existing research works fails in achieving the evaluation measures like classification accuracy, precision, recall and time take taken for classification, Hence there is need of an efficient frameworks that classifies the images based on fashion and fabric by addressing existing research problems. This paper presents an efficient framework for segmenting using grab cut techniques with the integrating the histograms of oriented gradients (HOG) and the speeded-up robust features (SURF) techniques. Whereas HOG technique is used to retrieve the global features and SURF technique is used for the local features and then segmentation is done based on Scale-Invariant Feature Transform(SIFT) method that segments the line, solve the incident scaling, changes in lighting condition and rotation between the two images, Later classification of the clothing images is done by using feature matching classification techniques, with proper text analysis the classification of images is done based on color and fabric of the image. The proposed technique is compared with existing multi-level classification technique to prove the proposed framework is more efficient than existing works. With the proposed technique, we could able to achieve the accuracy of 93% by varying the dataset of the images which is 10–15% more accurate than existing multi-level classification.
Moving Object Detection Based on Self-adaptive Contour Extraction

Object detection of moving targets requires both accuracy and real-time performance. In this paper, we propose a contour extraction prior to convolutional neural network to extract more salient features and use region proposal network to generate candidate regions. Afterwards, the feature maps and proposal regions are inputed to ROI pooling layer followed with some fully connected layers to classify objects and regress bounding box. Simulation experiments show that our method is effective in improving detection accuracy by testing on the dataset with 11 categories of moving targets.
Research on Fast Estimation Method of Fuzzy Parameters for Motion Blurred Images

Abstract Motion blur distortion is the most common type of image distortion in daily life. the research on motion-blurred image restoration technology has developed more mature. Classical algorithms such as Wiener filter and Kalman filter and various improved algorithms can achieve better results, but they take a long time and have great limitations in actual image restoration application scenarios. To solve this problem, this paper proposes an algorithm for fast restoration of image motion blur, an improved algorithm based on Randon transform to judge the image motion blur angle, and studies the algorithm for estimating the blur length under the condition that the blur angle is determined, and obtains two motion blur parameters of blurred image. Experimental results show that the fast image restoration method proposed in this paper has shorter time consumption, stronger anti-noise interference ability and better practicability.