Reviewerly/exHarmony huggingface dataset:
Interpolation and Sharpening for Image Upsampling

This paper presents a series of studies and discussions on upsampling of low-resolution images to obtain high-resolution images from interpolation methods. Firstly, the traditional nearest neighbor interpolation, bilinear interpolation and bicubic interpolation methods are reproduced and optimized respectively. Then different sharpening algorithms are discussed to solve the problem of blurred edges. By compensating the contours of the image, thus making the image sharp. The fourth-order and eighth-order Laplace operators are used respectively. Meanwhile, this paper refers to the idea of RCAS in FSR technology in order to find the Laplace operator that makes the best effect. Subsequently, the lanczos4 function is used to further solve the problem of distortions caused by the traditional interpolation method. The lanczos interpolation and linear interpolation methods are also applied to the non-edge and edge of the image, respectively, to achieve the best clarity. Finally, by combining two different sharpening algorithms with Lancozs algorithm, the images are upsampled with less distortion as possible. The software algorithm is also implemented on FPGA for verification.
A dwell point detection algorithm for smart pen

This paper presents a dwell point detection algorithm for smart pen. The main innovations of the method are: (a) The tracking of a smart pen is achieved by a common monocular camera. (b) A method of vertex adsorption is proposed. (c) The definition of a dwell point is introduced, and the auxiliary line intent is calculated by detecting the dwell point. Compared to the collision body detection method in Unity3D, the method in this paper improves the accuracy of doing auxiliary line intent and the smoothness of user operation. The effectiveness of the method is demonstrated by the use of the method in a smart pen interacting with a Unity3D-based three-dimensional geometry teaching system.
Human resources and internationalisation processes: a cognitive-based view

The paper aims at contributing to the complex debate about the role of human resources in advancing or hampering companies' internationalisation processes. Adopting a cognitive-based view, a set of variables for coding the role of human resources in internationalisation processes are identified, and their relations with companies' return on sales (ROS) in foreign countries are tested using structural equation modelling. From empirical research involving 638 employees from 45 Italian small and medium enterprises, the findings extend the managerial literature on internationalisation processes and human resource management, highlighting the impact of employees' previous international job experience, culture of origin, cultural orientation towards globalisation and educational background on companies' ROS in foreign countries.
A Parareal Architecture for Very Deep Convolutional Neural Networks

Due to the large number of layers in deep neural networks (DNNs) [11, 12], DNN training is time-consuming and there are demands to reduce training time these days. Recently, multi-GPU parallel computing has become an important topic for accelerating DNN training [2, 6]. In particular, Günther et al. [6] considered the layer structure of ResNet [8] as the forward Euler discretization of a specific ODE and applied a nonlinear in-time multigrid method [3] by regarding the learning process of the network as an optimal control problem.
Current aspects of quality assurance of Greek teacher professional training in the conditions of European integration

The article deals with the сurrent aspects of Greek teacher training quality assurance in the context of European integration processes. The entry of a higher pedagogical school into the European educational space is a complex and multifaceted task that requires the study of the best foreign experience in ensuring the quality of teacher training in the context of deepening European integration. Inside the education system teachers have a complex mission as far as the production and reproduction of official and unofficial school knowledge is concerned. The analysis of teacher education and training institutions development, and the recentpolicy initiatives and legislation, suggest that these Departments Teacher Education Policies and Developments in Europe are under the influences of diverse social and political forces. Therefore, any attempts to introduce reforms will be affected by the pressures and possibilities carried by the initiatives and practices of the EU and other supranational entities of global governance, initiatives which are spreading rapidly across national space.
Entropy and mutual information

Entropy is a fundamental concept in information theory, proposed by Shannon (1948). We shall first define the entropy of a discrete random variable, and show on some examples that it can be interpreted in two ways, either as an amount of randomness, or as an amount of information. This duality information/randomness may look mysterious, but it is at the heart of the probabilistic formalization of phenomena for which some unknown information is mathematically treated as if it were random. That this point of view is at all pertinent is not a priori obvious, but is attested by the importance of probability theory in the mathematics of the second half of the 20th century and by its impact in applications, the last one being probably the fantastic (and frightening) development of supervised learning.
Questions of Intellectual Property and Fundamental Values in the Digital Age

Today’s intellectual property debates, in both law and the larger society, are a bellwether of changing justice needs in the twenty-first century. As the digital age democratizes technological opportunities, it brings intellectual property law into mainstream everyday culture. This generates debates about the relationship between the constitutional interest in “the progress of science and useful arts” and other fundamental values, such as equality, privacy, and distributive justice. These values, which were not explicitly part of intellectual property regimes in prior eras, are especially challenged in today’s internet world. The article (which was presented as the annual Nies Lecture in April 2022 at Marquette Law School) explains how intellectual property law, originally envisioned as a regime to enable markets in intellectual goods, is becoming a framework through which to discuss essential sociopolitical issues. The result is to refigure the substance of “progress” in terms demonstrating the urgent relationship of art and science with social justice today.
A Deep System for Breast Tumor Classification from Mammograms

Medical imaging has continued to evolve and uses increasingly precise and efficient technologies for processing medical images and extracting relevant information. However, in contrast to this development, the analysis of breast cancer remains topical research that is very delicate to address despite the various screening techniques available to predict this disease. In this paper, a novel system based on deep learning is proposed to detect and classify breast tumors in mammograms into benign and malignant. Two approaches have been adopted in this study according to two publicly available mammographic datasets for research of different sizes. In the first approach, we have built a model on the Mammographic Image Analysis Society (MIAS) dataset which is a small set. To make this dataset sufficient to build a predictive model over, the first approach is passed through three steps, which are as follows: First, the Regions Of Interest (ROI) are extracted from each abnormal region of mammographic images of MIAS set, then, Roi-patches are generated from each ROI obtained in the first step, finally, ROI-patches are classified as benign or malignant using a pre-trained Convolutional Neural Network (CNN) architectures. While the second approach uses the cropped images extracted from the Curated Breast Imaging Subset of Digital Database for Screening Mammography (CBIS-DDSM) which are considered sufficient to build a model over it. Data preprocessing as well as Data augmentation has been applied on both MIAS and CBIS-DDSM datasets. Experiments are evaluated through transfer learning techniques based on Convolutional Neural Networks (CNNs) architectures: Dense-Net121, ResNet50, VGG16, and mobileNetV2 to check their accuracy scores. Experimental results are satisfactory showing the proposed approaches’ ability to distinguish between breast tumor benign and malignant from Mammograms with an overall sensitivity of 91% and accuracy of 90%.
Credit Card Fraud Detection Based on Multiple Machine Learning Models

Credit cards have long been one of the most popular methods of making payments. But this kind of payment still carry risks —— credit card fraud. Machine learning has played an essential role in detecting credit card fraud. This study uses machine learning methods to provide most accurate prediction of fraudulent transactions. These algorithms include Random Forest, Logistic Regression, SVM, and Extreme Gradient Boosting (XGBoost). Dataset used in this study is from Kaggle and is highly skewed. In order to find out if imbalanced dataset will affect predictions of models, three oversampling methods, Random oversampling, SMOTE, and ADASYN, are used to resample dataset for comparative analysis. From the analyzed results, oversampling methods tend to cause overfitting of models. XGBoost is the only technique that are not affected by oversampling methods. Since overfitting is a problem that should be avoided in classification problem, original data is selected for further evaluation of models. Performance of four algorithms are evaluated based on F-measure, Cohen's Kappa, AUC score, and accuracy. The results show that Random Forest and XGBoost outperform SVM and Logistic Regression. As a result, a fusion model is constructed based on these two classifiers. Log loss and brier score are used to further evaluate their predictions. The results show that Random Forest and XGBoost provide best predictions and close to each other. Fusion model has a little improvement compared to these two classifiers.
PIANOVISION - An Augmented Reality Piano Platform

PianoVision presents the best version yet of an educational piano app for Oculus Quest. Not only does it overlay notes onto the real piano using passthrough and hand tracking, but it also responds to user’s playing in real time through connections with MIDI-enabled keyboards.
An Innovative Approach To Software Modularization Based On The Artificial Fish Swarm Algorithm

It gets more and more expensive to maintain the complete software system as time goes on since the software architecture grows more complicated and the software code is more difficult to understand. This issue may be solved by taking the essential parts out of the source code and organizing them into the appropriate subsystems. Hierarchy-based partitioning is more complex and less successful in solving issues with huge software modules. In order to do this, this work suggests an innovative artificial fish swarm method as a meta-heuristic to solve the software module clustering problems (SMCPs) and presents a mutation operator and a local search strategy to handle the premature situation. The performance of the suggested method is assessed on a range of real-world software systems by comparison to the most recent meta-heuristic methods. The outcomes of the experiments demonstrate that the suggested methodology works better than those of other methods.
Research on the Construction of Accounting Big Data Analysis Platform Based on Cloud Computing

The traditional accounting model has obvious disadvantages: high cost, low efficiency, tedious work, and there is a large development space in the existing accounting system. This paper introduces the development process of cloud computing, and expounds the shortcomings of the existing accounting information system, and leads to the construction of accounting big data analysis platform based on cloud computing. The feasibility, construction principles, function construction, system architecture and other aspects of the platform construction are analyzed in depth, and the basic outline of the platform is presented to the readers, which provides some reference for the follow-up scholars ' research.
Analysis and diagnosis system for various industrial network protocols

For a comprehensive and systematic analysis of the communication quality in the industrial field, it is important for manufacturing enterprises to study effective diagnosis and evaluation methods and develop software tool to meet the actual needs of industrial communication networks for high quality, rapid diagnosis and positioning of anomalies. By analyzing the characteristics of various industrial network protocols, this paper studies the measurement methods of the key indicators of the real-time performance of industrial communication networks and the methods of communication anomaly diagnosis, and on this basis, designs and implements the analysis and diagnosis system for various industrial network protocols.
Study on the Development Status and Prospect of Blockchain Technology in the Energy Field

The initial exploration of the application of blockchain technology in the energy field has yielded many results. In the context of energy interconnection, information exchange, data sharing and China's "2030 carbon peak, 2060 carbon neutral" strategy, blockchain technology will play a greater role in the energy field, and the consensus mechanism is the energy block The key technology that the chain urgently needs to break through. First, this paper analyzes the opportunities and challenges faced by energy development, discusses the value and technical support of blockchain technology in the energy field, summarizes the current application status, and then proposes an on-chip consensus algorithm based on VRF, each shard can be processed in parallel On-chip transactions, run the consensus mechanism to verify and package these transactions, improve transaction execution efficiency, and verify the effectiveness and speed of the consensus mechanism proposed in this paper through example simulations. Finally, the application goals and several policy recommendations of energy blockchain are put forward, in order to provide reference for future energy planning.
Research on Dehazing of Cable Tunnel Image Based on Dark Channel Prior

The cable tunnel is an architectural structure to protect the cable. The building is located underground, and the cable is not visible to the naked eye on the ground like overhead lines. In order to protect and maintain the cable, there is a cable monitoring system. Among them, the most important thing for the monitoring system is to capture the images in the cable tunnel, and the generation of fog in the tunnel is unfavorable for the monitoring images. So there is a dehazing method. The traditional dehazing method is roughly divided into a dehazing method based on a physical model and a non-physical model. This paper studies the dehazing of the cable tunnel image based on the dark channel prior algorithm of the physical model.
Super-Resolution Reconstruction Algorithm Based on Improved Generative Adversarial Network

The current super-resolution algorithm for generative adversarial networks (SRGAN) has problems such as an unstable model training process and excessive smoothing of reconstructed images, which can affect the quality of generated images to a large extent. In this paper, based on SRGAN, all BN layers in the generative network are removed, and WGAN is used instead of JS scatter to optimize the discriminate network, This efficiently prevents the phenomenon of gradient disappearance and resolves the issue of unstable training of generative adversarial networks. The SA module is added to the vgg19 feature extraction network to obtain better feature information and improve the quality of the generated images. The experiments show that the proposed method has better stability in the training process compared with the traditional SRGAN on the DIV2K datasets, improvements are made to the reconstructed images' peak signal-to-noise ratio (PSNR), structural similarity (SSIM), and visual effect performance.
Fingertip detection based on shapelet

This paper proposes a shapelet-based fingertip detection method. Firstly, an image of a hand with five fingers opening is collected, and then it is preprocessed by some measures, including median filtering, binarization according to the skin color space, morphological processing and palm edge extraction. Then, the palm position is calculated using the extracted palm contour, and the distances from the palm position to all points of the palm contour are calculated in turn and converted into a sequence. The sequences of the same length corresponding to five fingertip parts can be extracted, and the sequences of other parts can be divided into sequences with similar lengths to the fingertip sequences for shapelet extraction. Finally, the extracted shapelet is matched with the sequences converted from other palm images, and the position of the fingertip can be accurately detected.
Design of Lightweight Human Resource Management System Based on Scrum

The research is based on Scrum of agile development, and taking a human resource management system of ABC Company as an example. This study is devoted to the rapid development of this system with the lightweight design framework of Spring Boot, a feasible development process of the first Sprint for Scrum is described and the feedback on application is positively. Finally, further work is put forward in the future. Firstly, an integrated platform for common interface should be developed to connect with other related sub-systems for ABC Company. Secondly, artificial intelligence algorithms are required to be embedded in the system to achieve the function of intelligent push of talent information.
Research on the application of voice interaction technology based on WebRTC in the Smart Water Supply System

In order to achieve voice operation of the Smart Water Supply System, the voice signal noise reduction algorithm in industrial environment is studied and simulated. The simulation results show that the algorithm can effectively improve the signal-to-noise ratio. Using WebRTC voice processing technology as the front-end tool, the experiment of integrating voice processing module into the Smart Water Supply System is carried out. The results show that the speech recognition system can achieve 98% recognition accuracy in real operation environment, and the response time of the device is not more than 1500ms, which can meet the requirements of the voice operation of the Smart Water Supply System.
X-Ray Image Compression Using Variational Auto-encoder

With the rapid development of medical industry, medical imaging related equipment is becoming more and more advanced, image data are becoming larger and larger, and it is more and more common for medical images to be stored and viewed in the cloud. Therefore, effective image compression of medical images plays a vital role in today's medical information system. In this paper, a variational self-encoder based on deep learning is proposed to compress medical images. We have carried out image compression experiments on CHESTX-Ray8 data set of the National Institute of Health (NIH), and compared the performance with another deep learning compression method and the compression method without deep learning. Experimental results show that this algorithm outperforms other methods in Peak Signal-to-Noise Ratio (PSNR).
A method for improving the efficiency of FIR filter operation

When the software realizes the FIR filter, it usually carries on the cyclic shift to the multiple input data according to the FIR filtering formula. After the completion of the shift, it can get the filtering result by multiplying with the FIR parameters. The higher the filtering order, the more the filtering channels, the longer the software running time will be consumed. In order to save software running time, this paper proposes a software implementation method which called copying the filter parameters. The FIR parameters are copied once, and only the latest input data is assigned in the operation process. It can directly multiply the data and parameters to get the filtering result, so as to save the software running time.
Personalized recommendation algorithm of books based on the diffusion of reader's interest

The ever-growing books help readers acquire knowledge faster than ever before. But the huge scale of these resources also easily makes people fall into the dilemma of "Information-Explosion", which prevents the reader from easily locating the books that are really suitable for them. To alleviate this dilemma, we analyzes the principle of the "Networks-Based-Inference" algorithm (NBI), which is a classical heuristic algorithm for recommendation. We also proposes an improved algorithm—NBI algorithm using Interest Diffusion (NBI-ID), that derives from this classical algorithm. This improved algorithm inherits the advantages of NBI method in simplicity and effectiveness, and optimizes the allocation of initial information in the process of information diffusion with an interest related indicator. Thus increasing the efficiency of the recommendation results. Experiments on the GoodBooks dataset show that the proposed algorithm improves in accuracy, recall and diversity compared to the classic NBI, CF (Collaborative Filtering) and GRM (Global Ranking Method) algorithms.
A Chinese word segmentation method based on dictionary and HMM

Aiming at the problems of ambiguity segmentation and low success rate of new words discovery in Chinese word segmentation, this paper proposes a Chinese word segmentation method based on dictionary and Hidden Markov Model. Through forward maximum matching algorithm and backward maximum matching algorithm, the coarse segmentation results are obtained, and the ambiguous fragments are collected and input into the Hidden Markov model. The Hidden Markov Model performs secondary word segmentation through word order tagging and identifies new words, and adds new words to the dictionary to improve the dictionary. The experimental results show that the proposed algorithm improves the problem of low success rate of ambiguity recognition and new word discovery, improves the accuracy, recall and F1 value of ordinary text segmentation, and improves the problem that Jieba segmentation ability decreases in professional text.
An encrypted traffic classification model based on the raw traffic and spatiotemporal characteristics

Deep learning techniques are frequently utilized and produce effective results in the classification of encrypted traffic. In the current encryption traffic classification process, the network traffic characteristics are not sufficiently extracted, which is a concern. An encrypted traffic classification model based on raw network traffic and its spatiotemporal characteristics is proposed in this paper. The raw network traffic is divided into sessions, and the packets inside each session are then split into 784-byte slices, and the traffic is then described using the slice data. The time feature vector and the spatial feature vector are then created by combining ResNet and GRU models to generate features from raw network data in parallel. The traffic is then classified using the combined features. According to experimental findings, the proposed model's recognition accuracy on the ISCX-NonVPN-VPN2016 dataset reached 99.36%, which is an improvement over other approaches currently in use.