Reviewerly/exHarmony huggingface dataset:
Hybrid Error-Management Strategies in Quantum Repeater Networks

A quantum network is expected to enhance distributed quantum computing and quantum communication over a long distance while providing unconditional security. As quantum entanglement is essential for a quantum network, major issues from various types of noise and decoherence prevent it from being realized, and research has been intensively active to obtain optimal configurations for a quantum network. In this work, we address the performance of a quantum network capable of quantum error correction and entanglement purification. Our results show that one should distribute Bell pairs as fast as possible while balancing the deployment of fidelity enhancement. We also show suitable hybrid strategies in quantum cryptography tasks under some noise regimes that need to use purification and quantum error correction together. Our results suggest that using purification to distribute high fidelity Bell pairs and preserving them for application using quantum error correction is a promising way to achieve a near-term quantum network for secure communication.
Prediction of cost of power transmission and transformation project based on neural network

In the face of the increasingly high cost of intelligent substations, it is necessary to strengthen the management and control of intelligent substation engineering cost within a reasonable range. To achieve the purpose of saving construction resources, reducing costs and increasing efficiency has become a key problem that must be solved at this stage. Currently, the engineering design and estimation process of power transmission and transformation projects lacks of the function of forecasting. In order to solve this problem, this paper establishes a neural network-based power transmission and transformation project cost prediction model, establishes a cost system, and uses the principal component analysis method to extract the main influencing factors of the power transmission and transformation project cost. Through testing and verification, the prediction results of the established model are more accurate, and it can reasonably predict the cost of power transmission and transformation projects.
Implementation Problem Management to Achieve Human Welfare Using Machine Learning

The main objective of the mission is to create a mid-level customer satisfaction problem management system using RDBMS, HTML, CSS and JAVASCRIP. Chat bots typically provide a text-based user interface where users can report any kind of problem they are suffering from and find suitable solutions provided by mentors. Problem management systems are typically state-full services that remember previous commands (and sometimes conversations) to provide functionality. If problem management system technology were integrated into a popular web service, it would undoubtedly be used by more people.
Marketing Risk Path Identification of Small and Medium-sized Energy Enterprises Based on ISM-MIMAC

To explore the path identification of marketing risk factors in small and medium-sized energy enterprises, this study extracts 16 factors influencing marketing from 3 aspects of the external environment, internal management and information feedback through literature analysis and expert interviews. In this paper, the interpretative structural model (ISM) is used to construct the multi-level structural diagram of influencing factors and the influencing factors are divided into three layers: direct factors, intermediate factors and fundamental factors. Then the matrix impact of cross multiplication applied to classification (MICMAC) is used to analyze the factors affecting the risk of small and medium-sized energy enterprises, and then the driving force and dependency classification diagram of marketing risk factors is drawn. The results show that the dependence and driving force of financing risk, partners, marketing personnel and other factors are relatively weak, and risk treatment can be prioritized. The factors such as lack of policies, sudden factors and organizational structure have high driving forces, which are the key factors that constitute the marketing risks of energy enterprises, and should be focused on. This study takes the marketing risk factors of small and medium-sized energy enterprises as the research object, and the research results have certain universality, which can provide new solutions for small and medium-sized energy enterprises to face marketing risks.
Diffusion-Based 3D Human Pose Estimation with Multi-Hypothesis Aggregation

In this paper, a novel Diffusion-based 3D Pose estimation (D3DP) method with Joint-wise reProjection-based Multi-hypothesis Aggregation (JPMA) is proposed for probabilistic 3D human pose estimation. On the one hand, D3DP generates multiple possible 3D pose hypotheses for a single 2D observation. It gradually diffuses the ground truth 3D poses to a random distribution, and learns a denoiser conditioned on 2D keypoints to recover the uncontaminated 3D poses. The proposed D3DP is compatible with existing 3D pose estimators and supports users to balance efficiency and accuracy during inference through two customizable parameters. On the other hand, JPMA is proposed to assemble multiple hypotheses generated by D3DP into a single 3D pose for practical use. It reprojects 3D pose hypotheses to the 2D camera plane, selects the best hypothesis joint-by-joint based on the reprojection errors, and combines the selected joints into the final pose. The proposed JPMA conducts aggregation at the joint level and makes use of the 2D prior information, both of which have been overlooked by previous approaches. Extensive experiments on Human3.6M and MPI-INF-3DHP datasets show that our method outperforms the state-of-the-art deterministic and probabilistic approaches by 1.5% and 8.9%, respectively. Code is available at https://github.com/paTRICK-swk/D3DP.
Analysis to Mitigate Risks in Information Security for Management in Higher Education Institutions

The problems of security in educational institutions in the world and especially in Ecuador are evident for its management. The objective is to carry out an analysis to determine how to mitigate the risks in information security for management in higher education institutions. It was used the deductive method and exploratory research were used for the analysis of information. Turned out a relevant contribution in information security from several researchers from Ecuador, China, South Korea, Japan, the Republic of Latvia, Newark, Delaware, Islamic, among others, a context diagram of all the relevant actors and forms and trends for the evaluation of an indicator to mitigate the risks of information in a higher education institution. It was concluded that the relevant contributions of the countries, the context diagram, forms and trends obtained as results in this research should be applied in a higher education institution to mitigate the risks of information.
Continuous Indeterminate Probability Neural Network

This paper introduces a general model called CIPNN - Continuous Indeterminate Probability Neural Network, and this model is based on IPNN, which is used for discrete latent random variables. Currently, posterior of continuous latent variables is regarded as intractable, with the new theory proposed by IPNN this problem can be solved. Our contributions are Four-fold. First, we derive the analytical solution of the posterior calculation of continuous latent random variables and propose a general classification model (CIPNN). Second, we propose a general auto-encoder called CIPAE - Continuous Indeterminate Probability Auto-Encoder, the decoder part is not a neural network and uses a fully probabilistic inference model for the first time. Third, we propose a new method to visualize the latent random variables, we use one of N dimensional latent variables as a decoder to reconstruct the input image, which can work even for classification tasks, in this way, we can see what each latent variable has learned. Fourth, IPNN has shown great classification capability, CIPNN has pushed this classification capability to infinity. Theoretical advantages are reflected in experimental results.
Requirement Formalisation using Natural Language Processing and Machine Learning: A Systematic Review

Improvement of software development methodologies attracts developers to automatic Requirement Formalisation (RF) in the Requirement Engineering (RE) field. The potential advantages by applying Natural Language Processing (NLP) and Machine Learning (ML) in reducing the ambiguity and incompleteness of requirement written in natural languages is reported in different studies. The goal of this paper is to survey and classify existing work on NLP and ML for RF, identifying challenges in this domain and providing promising future research directions. To achieve this, we conducted a systematic literature review to outline the current state-of-the-art of NLP and ML techniques in RF by selecting 257 papers from common used libraries. The search result is filtered by defining inclusion and exclusion criteria and 47 relevant studies between 2012 and 2022 are selected. We found that heuristic NLP approaches are the most common NLP techniques used for automatic RF, primary operating on structured and semi-structured data. This study also revealed that Deep Learning (DL) technique are not widely used, instead classical ML techniques are predominant in the surveyed studies. More importantly, we identified the difficulty of comparing the performance of different approaches due to the lack of standard benchmark cases for RF.
Review for Handwritten Devanagari Character Recognition using ML Algorithms

Devanagari Character Recognition is a system in which handwritten Image is recognized and converted into a digital form. Devanagari handwritten character recognition system is based on Deep learning technique, which manages the recognition of Devanagari script particularly Hindi. This recognition system mainly has five stages i.e. Pre-processing, Segmentation, Feature Extraction, Prediction and Post processing. This paper has analyzed the approach for recognition of handwritten Devanagari characters. There are various approaches to solve this. Some of the methods along with their accuracy and techniques used are discussed here. Depending upon the dataset and accuracies of each character the techniques differs.
An algorithm of modulated signal recognition based on statistical parameters

This paper deals with the modulation identification of blind modulation signals. This paper proposes a new characteristic parameter to distinguish 8PSK from other MPSK(2 and 4) signals. The simulation results show that the average recognition accuracy of the algorithm can reach 90% when the signal noise ratio (SNR) is 8 dB, and it can recognize thirteen modulation signals of CW, AM, FM, LSB, USB, 2ASK, 4ASK, BPSK, QPSK, 8PSK, 2FSK, 4FSK, and 16QAM.
Philosophical prerequisites for researching the personal and developmental potential of knowledge of the prospective engineer

Changes taking place in the dynamically developing industrial production and society determine the key resource and wealth of the information society knowledge, meanings, ideas, educational design and creativity of the subject of engineering activity. The problem of the personal development potential of knowledge as the most important attribution that becomes in the space of professional self-development of the prospective engineer is relevant for modern society. Today, the need for innovative transformations in the training of engineers of a new formation with the appropriate amount of knowledge for the successful solution of scientific, technical and production-organizational problems is obvious. The idea of coevolution of natural and social epistemically manifests itself in modern bionic engineering projects, awakening professional education to investigate the phenomenon of interpenetration of knowledge and mechanisms of cognition. The paper attempted to assess the existing aspects of the problem under consideration, to justify the relevance of the appeal to philosophical and scientific knowledge, the teaching of knowledge, the fundamental provisions of which will provide the required result, both for professional and personal development of prospective engineers. To determine the philosophical prerequisites for the personal-developing potential of knowledge, we turned to epistemology, a science that is more occupied with the value-meaning question: What is knowledge for a person? What matters more in the development of personality as a subject of cognition? The study has found that in the changed conditions of modern information and communicative civilization, epistemology puts forward the requirement to rethink traditional ideas about knowledge, how to obtain it and justify it. Its task-target functionality includes understanding the categorical essence of knowledge, identifying the grounds for its reliability, considering the mechanisms and models of cognitive processes; search for prerequisites for the developing potential of knowledge; awareness of the world around us, and the opportunities to live and act in it.
Privacy-Preserving Content-Based Task Allocation

Content-based task allocation is an important part of MCS, which can effectively help data requesters find suitable sensing users to collect data. However, users’ task information may reveal sensitive information, e.g., location and hobbies. An intuitive scheme is to encrypt task information and perform task queries in ciphertexts. However, considering that MCS has the characteristics of a large scale of users, many types of tasks, and resource-limited devices, schemes based on traditional time-consuming cryptographic tools will consume tremendous computing resources of the mobile crowdsensing platform. In order to solve the challenges, this chapter firstly proposes a basic privacy-preserving content-based task allocation scheme, named PPTA, based on random matrix multiplication technology and polynomial functions, which can achieve efficient task queries without revealing task privacy. In addition, this chapter extends PPTA so that it can efficiently support more functionalities in task allocation, such as conjunctive task allocation, Top-z task allocation, task allocation with access control, and task recovery. Through detailed security analysis, we demonstrate that PPTA can well preserve task privacy and query privacy. Experiments based on real mobile crowdsensing applications demonstrate that PPTA can achieve high data encryption and task query efficiency.
Overview of MCS Technology

This book studies user privacy issues in data collection and data processing, which mainly involves the privacy-preserving content-based task allocation, the privacy-preserving location-based task allocation, the privacy-preserving truth discovery with truth transparency, and the privacy-preserving truth discovery with truth hiding. To realize the above schemes, the basic knowledge used in this chapter includes polynomial function, secure hash function, searchable encryption, asymmetric scalar-product-preserving encryption, polynomial fitting, truth discovery, and public-key cryptosystem supporting distributed decryption. The relevant basic knowledge is introduced as follows.
Real-time Enhancement Algorithm of Low-light Image Based on Zero-DCE

At present, all walks of life, such as industry, transportation, medical care, etc., can use computer vision technology to promote the development of the industry and solve the problems of the industry. However, some industries have complex operation scenarios and are easily affected by weather and other factors, resulting in blurred and dimmed images. In response to such problems, many image enhancement algorithms have emerged and achieved good results. However, most of these methods perform image enhancement on all video images, which leads to deliberate enhancement of some normal images, resulting in overexposure; at the same time, it also causes waste of computing resources, and it is difficult to guarantee the real-time performance of the algorithm. In view of the above content, this paper first proposes a low-light image judgment method to judge whether the current video frame needs to be preprocessed. In addition, through experiments and comparison of the current mainstream low-light image preprocessing algorithms, the Zero-DeE algorithm that is more suitable for field use is selected, and the self-made drilling multi-exposure sequence data set is combined to train the optimization model, which has both subjective processing effect and objective index evaluation raised.
A two-stream attention mechanisms network

In the field of neurology, the concept of attention mechanism is frequently applied to neural networks. Focusing on the application of attention mechanisms in feature map channels and spatial, we propose a two-stream attention networks (called TSAN) based on channel and spatial that can be applied to neural networks. The TSAN network consists of two consecutive parts. The input feature map is first passed through the channel submodule and then through the space sub-module to obtain a feature layer with attention weights. The channel sub-module compresses channel information by using global maximum pooling. The channel sub-module compresses each input feature map into a learnable weight to represent the features of the feature map. The spatial sub-module also uses the global maximum pooling layer to aggregate the location information of the feature maps along the channel axes. By performing spatial compression along the channel axis, the spatial sub-module can generate a corresponding learnable weight for each spatial location. Meanwhile, to further capture the spatial location dependence over long distances, the spatial sub-module will receive inputs from two consecutive feature maps. Since TSAN is a tiny universal block, it can be seamlessly inserted into any CNN architecture with negligible overhead. Also, TSAN can be trained together with the underlying CNN. We validate the neural network with TSAN by conducting extensive experiments using ImageNet, CIFAR-100 datasets. Our experiments show that TSAN can improve the robustness and accuracy of various models for image classification and object detection, which demonstrates the wide applicability of TSAN.
Psychological background of the leadership qualities formation in youth

Studying the background of successful leadership is one of the main applied problems of social and organizational psychology. For the formation of leadership qualities, it is important to have a favorable psychological climate around the individual, a sense of inner comfort, support of the immediate environment, adequate self-esteem, and a developed need-motivation sphere. A sensitive period for the leadership formation is early youth, when personal identity, worldview and value system are formed. The basis for the development of leadership qualities in youth is the development of personal qualities, namely: creating an image of the future and components of emotional intelligence. Objective. The research deals with the theoretical analysis of the leadership features, the clarification of the psychological conditions of its formation in the period of youth; experimental study of the manifestation of leadership qualities and their psychological conditions in youth. Methods. To achieve the set objective, we used a set of theoretical methods, such as: analysis and generalization of scientific information; the following empirical methods: observation; conversation; questionnaires “Do you have charisma?” (D. Konger); “Leadership potential” (D. Kotter); “Emotional intelligence” (R. Daft); “Creating the image of the future” (V. Hitt). The Pearson correlation method was used for statistical data processing. The research was conducted on the basis of the “School of Externs of Brovary Teaching and Educational Association of Brovary City Council, Kyiv region”, Brovary town. 30 respondents aged 17-18 took part in it. The results of the empirical study of psychological background of the leadership qualities formation of young men showed that young men have expressed leadership qualities (80%) and weakly expressed charisma (73.33%); a high level of emotional self-awareness (40%), emotional intelligence (26.67%) and self-possession (26.67%). The value of the correlation coefficient value between the indices of the level of charisma and indices of relationship management corresponds to strong correlation; between indices of leadership potential and criteria for realizing the image of the future (p≤0.01). We see the prospects for further research in the development and approval of the psychological training program for the formation of leadership qualities in youth.
Structural Imbalance Aware Graph Augmentation Learning

Graph machine learning (GML) has made great progress in node classification, link prediction, graph classification and so on. However, graphs in reality are often structurally imbalanced, that is, only a few hub nodes have a denser local structure and higher influence. The imbalance may compromise the robustness of existing GML models, especially in learning tail nodes. This paper proposes a selective graph augmentation method (SAug) to solve this problem. Firstly, a Pagerank-based sampling strategy is designed to identify hub nodes and tail nodes in the graph. Secondly, a selective augmentation strategy is proposed, which drops the noisy neighbors of hub nodes on one side, and discovers the latent neighbors and generates pseudo neighbors for tail nodes on the other side. It can also alleviate the structural imbalance between two types of nodes. Finally, a GNN model will be retrained on the augmented graph. Extensive experiments demonstrate that SAug can significantly improve the backbone GNNs and achieve superior performance to its competitors of graph augmentation methods and hub/tail aware methods.
Formalization of Quantum Intermediate Representations for Code Safety

Quantum Intermediate Representation (QIR) is a Microsoft-developed, LLVM-based intermediate representation for quantum program compilers. QIR aims to provide a general solution for quantum program compilers independent of front-end languages and back-end hardware, thus avoiding duplicate development of intermediate representations and compilers. Since it is still under development, QIR is described in natural language and lacks a formal definition, leading to ambiguity in its interpretation and a lack of rigor in implementing quantum functions. In this paper, we provide formal definitions for the data types and instruction sets of QIR, aiming to provide correctness and security guarantees for operations and intermediate code conversions in QIR. To validate our design, we show some samples of unsafe QIR code where errors can be detected by our formal approach.
SEM-POS: Grammatically and Semantically Correct Video Captioning

Generating grammatically and semantically correct captions in video captioning is a challenging task. The captions generated from the existing methods are either word-by-word that do not align with grammatical structure or miss key information from the input videos. To address these issues, we introduce a novel global-local fusion network, with a Global-Local Fusion Block (GLFB) that encodes and fuses features from different parts of speech (POS) components with visual-spatial features. We use novel combinations of different POS components - 'determinant + subject', 'auxiliary verb', 'verb', and 'determinant + object' for supervision of the POS blocks - Det + Subject, Aux Verb, Verb, and Det + Object respectively. The novel global-local fusion network together with POS blocks helps align the visual features with language description to generate grammatically and semantically correct captions. Extensive qualitative and quantitative experiments on benchmark MSVD and MSRVTT datasets demonstrate that the proposed approach generates more grammatically and semantically correct captions compared to the existing methods, achieving the new state-of-the-art. Ablations on the POS blocks and the GLFB demonstrate the impact of the contributions on the proposed method.