Reviewerly/exHarmony huggingface dataset:
Security analysis of discretized polar modulation continuous-variable quantum key distribution

In experimental setups of Gaussian modulation continuous-variable quantum key distribution (CV-QKD), the amplitude and phase of coherent states will be modulated and discretized due to the finite resolution of voltages that drive electro-optical modulators, resulting in Gaussian modulation discretization. We demonstrate that the impact of discretization on CV-QKD can be described as a multiplicative coefficient experimentally and modeled as a preparation noise imposed on ideal Gaussian modulation theoretically. To obtain an accurate estimation of quadrature fluctuation induced by discretized polar modulation, a data-filtering process named preselection can be applied before state transmission. Numerical results show that when amplitude resolution is 0.25 and phase resolution is 0.02, discretized polar modulation enables a transmission distance of 69 km under homodyne detection, reaching 89% of ideal Gaussian modulation. As for heterodyne detection, 55 km and 80% are achieved.
Image-Based Number Sign Recognition for Ethiopian Sign Language Using Support Vector Machine

In recent times, several technologies have been utilized to bridge the communication gap between persons who have hearing or speaking difficulties and those who dont. Human use speech as a means of communication but hearing impaired people are not fortunate to use their voice for communication, but instead they use sign language as a primary means of communication. However, only a few number of people understand sign language. This results in miscommunication in terms of expression and communication between hearing impaired persons and normal people. To reduce this gap in communication, sign language recognition technologies are used as a means to translate expressions from one form of language to another. Therefore, this paper presents a local number sign recognition approach for Ethiopian sign language using Support Vector Machine (SVM) with sign number images as input and local sign numbers as the desired output. The proposed system comprises of three major stages: Image processing, feature extraction, and SVM modeling. Image processing is performed to make the Amharic sign language number feature extraction process simpler. The features are extracted from the shape of the signers hand to represent local number signs in Ethiopian Sign Language. Finally, a SVM classification model was built using a machine training dataset. The SVM multi-class model achieves 99.33% with a test loss accuracy of 9.7% for local number sign recognition.
A model for generating a user dynamic profile on social media

A user profile can be considered as an interface of one’s characteristics, interests, thoughts, and preferences. This profile is dynamic as it is changed over time. Designing a user profile model that can track and accommodate these changes is important for providing personalized services on the Internet. This paper introduces a model for building a dynamic user profile based on the daily activities of the user and other users’ activities who are followed by this user. The proposed model tracks the user, learns from his activities, and finally reformulates the user profile. Using two different supervised learning classification algorithms, the user’s activities (tweets, retweets, etc.) are classified into a predefined list of topic categories of interest that are used to form and model the user’s dynamic profile. The primary results show that the proposed model can be used to accurately describe the user, and thus, could be the default user interface for others.
Efficient Approximation of Action Potentials with High-Order Shape Preservation in Unsupervised Spike Sorting

This paper presents a novel approximation unit added to the conventional spike processing chain which provides an appreciable reduction of complexity of the high-hardware cost feature extractors. The use of the Taylor polynomial is proposed and modelled employing its cascaded derivatives to non-uniformly capture the essential samples in each spike for reliable feature extraction and sorting. Inclusion of the approximation unit can provide 3X compression (i.e. from 66 to 22 samples) to the spike waveforms while preserving their shapes. Detailed spike waveform sequences based on in-vivo measurements have been generated using a customized neural simulator for performance assessment of the approximation unit tested on six published feature extractors. For noise levels σN between 0.05 and 0.3 and groups of 3 spikes in each channel, all the feature extractors provide almost same sorting performance before and after approximation. The overall implementation cost when including the approximation unit and feature extraction shows a large reduction (i.e. up to 8.7X) in the hardware costly and more accurate feature extractors, offering a substantial improvement in feature extraction design.
Breast Cancer Subtypes Classification with Hybrid Machine Learning Model

Abstract Background Breast cancer is the most prevailing heterogeneous disease among females characterized with distinct molecular subtypes and varied clinicopathological features. With the emergence of various artificial intelligence techniques especially machine learning, the breast cancer research has attained new heights in cancer detection and prognosis. Objective Recent development in computer driven diagnostic system has enabled the clinicians to improve the accuracy in detecting various types of breast tumors. Our study is to develop a computer driven diagnostic system which will enable the clinicians to improve the accuracy in detecting various types of breast tumors. Methods In this article, we proposed a breast cancer classification model based on the hybridization of machine learning approaches for classifying triple-negative breast cancer and non-triple negative breast cancer patients with clinicopathological features collected from multiple tertiary care hospitals/centers. Results The results of genetic algorithm and support vector machine (GA-SVM) hybrid model was compared with classics feature selection SVM hybrid models like support vector machine-recursive feature elimination (SVM-RFE), LASSO-SVM, Grid-SVM, and linear SVM. The classification results obtained from GA-SVM hybrid model outperformed the other compared models when applied on two distinct hospital-based datasets of patients investigated with breast cancer in North West of African subcontinent. To validate the predictive model accuracy, 10-fold cross-validation method was applied on all models with the same multicentered datasets. The model performance was evaluated with well-known metrics like mean squared error, logarithmic loss, F1-score, area under the ROC curve, and the precision–recall curve. Conclusion The hybrid machine learning model can be employed for breast cancer subtypes classification that could help the medical practitioners in better treatment planning and disease outcome.
Collaborative Recommender System based on Improved Firefly Algorithm

A recommendation system aims to capture the taste of the customer and predict relevant items which he/she may be interested in buying. There are many algorithms for generating recommendations in literature, however, most of them are non-optimal and do not have the capability to handle big data. In this paper, a collaborative recommendation system is proposed based on improved firefly algorithm. The firefly algorithm is used to generate optimal clusters which provide effective recommendations. The proposed algorithm works in two phases: Phase I which generates the clusters with firefly algorithm and Phase II gives real time recommendations. The firefly algorithm has been implemented in Apache Spark to give it the capability of handling big data. The combination of improved firefly-based clustering and Apache Spark makes it much faster and optimal than the state-of-the-art recommendation models. For experiments, movie-lens dataset has been utilized and different evaluation metrics have been used for performance analysis. The results show that the proposed method gives better results compared to existing methods.
Comparative analysis of optical transformation for image encryption

This paper first introduces the characteristics of optical transformation, and then according to the relationship between optical transformation and cryptography, it is suitable for image encryption, followed by a detailed description of various common optical transformation, and comparative analysis.
Blockchain Applications - Transforming Industries, Enhancing Security, and Addressing Ethical Considerations

Unlock the transformative power of blockchain technology with Blockchain Applications - Transforming Industries, Enhancing Security, and Addressing Ethical Considerations. This edited volume brings together leading experts and their thought-provoking chapters on blockchain’s diverse applications. From healthcare to finance and from energy to supply chain, delve into the latest advancements in cybersecurity, smart contracts, and audit digitalization. Discover how blockchain is revolutionizing the tourism industry and enabling decentralized autonomous organizations. Explore the potential of deep learning for disease detection and gain insights into the legal and market challenges of non-fungible tokens (NFTs). With real-world examples and case studies, this book showcases blockchain’s tangible benefits, which include increased transparency, enhanced security, and improved efficiency. It also improves understanding of the ethical considerations and regulatory implications surrounding blockchain adoption for responsible implementation. This invaluable resource is for professionals, researchers, and technology enthusiasts alike, offering unique perspectives and cutting-edge research. Join us on a captivating journey through the world of blockchain applications. Experience its potential to reshape industries, enhance security, and pave the way for a transparent and decentralized future. Discover the keywords defining this volume: blockchain technology, transformative potential, cybersecurity, smart contracts, decentralized autonomous organizations, deep learning techniques, non-fungible tokens, transparency, security, efficiency, ethical considerations, regulatory implications, real-world examples, and cutting-edge research. Embrace the revolution and unlock the limitless possibilities of blockchain technology.
Real time dangerous action warning system based on graph convolution neural network

In order to solve a series of disadvantages of slow efficiency and high cost brought by the traditional human action recognition method based on manual feature extraction, in order to prove the feasibility of using graph convolution neural network and action recognition, a real-time dangerous action alarm system based on graph convolution neural network is designed and implemented.The system includes data collection, human posture estimation, action recognition, dangerous action alarm and other functions.The feasibility of using graph convolution neural network to realize real-time dangerous action alarm system is proved by the system.Even if the graph convolution neural network does not pass the training, it can extract good features only by using the original random initialization parameters.If certain annotation information is given, its effect can be greatly improved.After the system adopts the graph convolution neural network, the action recognition can be more accurate, more efficient, faster and more reliable than the traditional method.
Camera Auto-Calibration using a Sequence of 2D Images with Small Rotations and Translations

3D model generation needs depth information of the object in the input images. This information can be found using stereo imaging but it needs camera parameters. Camera calibration is not possible without some knowledge about the objects in the scene or assuming fixed or known values for the camera parameters. When using fixed camera parameters, however, small rotation angles or small translation in camera position can degenerate the results. The degeneracy can be omitted by adding new restrictions to the a-priori knowledge about the camera parameters. The calibrated data may be used to reconstruct 3D model of the scene.
Improved Mesh Reconstruction With an Edge Quality Enhancement Using Multiple Inward Depth Streams

This paper presents a complete 3D model reconstruction of an object with edge quality enhancements using multiple inward depth sensors to create closed 3D model. In the reconstruction pipeline, a pattern of incorrect depth information was consistently observed at the edges of the mesh generated by each sensor stream, which we refer to in this paper as a “drift-effect”. In order to mitigate this, we introduced a filtering approach with a localized threshold value that is used to remove drift faces from a mesh. We also present a mesh stitching technique incorporating Laplacian mesh smoothing to generate a closed 3D model from the smoothened multi-view meshes. The primary objective of this research was to implement a system that could capture a static physical object with a minimum scan time and at a low cost while retaining accurate details in the model. For the demonstration, we used four Intel RealSense D435 depth sensors to capture a clothing article that can be imported into a virtual dressing room application. We captured the entire object within three seconds, which is quicker than traditional techniques such as table rotation and sensor rotation. The final results indicate that the system is able to provide a satisfactory reconstruction of a clothing model which can be used in a live virtual dressing room application.
Research on Agricultural Named Entity Recognition Based on Pre Train BERT

Aiming at the problems of traditional named entity recognition methods relying on artificial dictionary and insufficient feature extraction in the process of agricultural pest information extraction, considering the complexity and fuzziness of agricultural text data, an agricultural named entity recognition method based on pre training BERT is proposed.firstly, the unlabeled pre training BERT was used to eliminate ambiguity, then BILSTM was used to capture long-distance dependence, and finally the best sequence annotation was selected through CRF.In addition, combined with the text particularity of agricultural entities, the word itself and partial radicals are selected to establish a joint multi feature PBERT-BILSTM-CRF for entity recognition.experiments show that the PBERT-BILSTM-CRF combined with the combined characteristics of the word itself and the radical has improved the precision, recall and F1 value compared with other models, and its optimal F1 value has reached 90.24%.the model has the characteristics of fast training speed and strong recognition ability.Named entity recognition is the premise of many tasks in the middle and downstream of natural language processing.The model provides a research basis for the construction of knowledge graph in agricultural field and agricultural Q&A.
Dynamic Monitoring and Evaluation of Regional EE Quality Based on Multi-source Remote Sensing Data

Regional EEal protection is an inevitable trend of environmental protection development.Based on multi-source remote sensing data, a typical land remediation project was selected.The rectification process of the project area.Monitoring and analysis.The results show that: 1) The humidity and greenness indicators have a positive impact on the ecological environment (EE) quality of the project area, and the dry heat index has a negative impact; 2) the average RSEI before repair, the middle and the back are 0.652, 0.572 and 0.605 respectively; RSEI fine grade level ratio of 78.73%, 39.55% and 63.29%; RSEI worse, better change, the ratio was 42.55%, 46.25% and 11.20%; 3) the EE quality in the project area showing the "first dropped and then rose to a total The trend of decline indicates that the "remediation period -the deterioration of the recovery period" has become betterthe whole process is even worse.Land remediation will cause continuous disturbance to the EE of the project area, and there will be a lag period for restoring and improving the regional EE.After five years of construction, the EE quality level of the project area is still lower than the level before the remediation.
Experience of Sophomore Syndrome Among Nursing College Students

This study was attempted to prepare the basic data necessary for developing a counseling program and career program suitable for the characteristics of the nursing department in the future by exploring the meaning of nursing students’ experiences of sophomore syndrome. The participants of this study were nine second-year students in the nursing department of one university in N city. The data collection period was from December 1, 2020 to January 15, 2021, and the data were collected through individual in-depth interviews and analyzed according to the qualitative content analysis method. As a result of analyzing the meaning of the experience, three thematic collections were composed: ‘symptoms of sophomore syndrome’, ‘causes of sophomore syndrome’, and ‘methods of overcoming sophomore syndrome’. Active support and support systems from universities and departments are needed so that nursing students can prepare for their careers and have psychological stability. We propose a qualitative study by applying the grounded theory methodology that intensively observes the relationship between society and the social environment from an interaction point of view.
Status Quo of Foreign Language Teaching Based on Multimedia Equipment-assisted Teaching

Multimedia-assisted foreign language teaching could combine the features of text, sound, image and animation into one, which helps create a good language learning situation, maximize the motivation and initiative of students' learning and enhance the teaching efficiency.Multimedia equipment-assisted teaching is a teaching method that many foreign language teachers are exploring and trying.However, some front-line teachers still have a vague understanding of it and lack theoretical guidance.The situation of foreign language teaching is quite different from other forms of foreign language teaching.Foreign language teaching in most schools meets the needs of entrance examination to a large extent.As a result, university teachers began to take the road of multimedia-assisted teaching in foreign language teaching in universities, that is, to take the advantages of different schools of teaching methods, to remove the shortcomings of each school of teaching methods, and use multimedia equipment to assist teaching, exploring a teaching method that can not only adapt to the development of the times, but also effectively promote the improvement of college students' foreign language proficiency.Through the investigation of the application of multimedia-assisted teaching in foreign language teaching, it verified several problems in foreign language teaching, and designed the application of multimedia platform, so that multimedia equipment-assisted teaching can be reasonably used in foreign language teaching, and cultivate students' cross-cultural communication.At the same time, students should be motivated to participate actively in learning practice so as to improve the quality of foreign language teaching.
Ranking Loss and Sequestering Learning for Reducing Image Search Bias in Histopathology

Recently, deep learning has started to play an essential role in healthcare applications, including image search in digital pathology. Despite the recent progress in computer vision, significant issues remain for image searching in histopathology archives. A well-known problem is AI bias and lack of generalization. A more particular shortcoming of deep models is the ignorance toward search functionality. The former affects every model, the latter only search and matching. Due to the lack of ranking-based learning, researchers must train models based on the classification error and then use the resultant embedding for image search purposes. Moreover, deep models appear to be prone to internal bias even if using a large image repository of various hospitals. This paper proposes two novel ideas to improve image search performance. First, we use a ranking loss function to guide feature extraction toward the matchingoriented nature of the search. By forcing the model to learn the ranking of matched outputs, the representation learning is customized toward image search instead of learning a class label. Second, we introduce the concept of sequestering learning to enhance the generalization of feature extraction. By excluding the images of the input hospital from the matched outputs, i.e., sequestering the input domain, the institutional bias is reduced. The proposed ideas are implemented and validated through the largest public dataset of whole slide images. The experiments demonstrate superior results compare to SOTA.
Multi-modal Analysis of Textbook Discourse——Taking the High School English Textbook of PEP (2019 Edition) as an Example

Multi-modal discourse analysis is one of the hot topics in contemporary linguistic research.Based on systemic functional linguistics, visual grammar, and the complementary theory of conceptual inter symbol, this paper makes case study and statistical analysis of the images in new senior high school English textbooks published by People's Education Press, explores the multi-modal features of the textbooks and puts forward some suggestions on how to make use of the multi-modal features of the textbooks to promote modern English teaching.