Reviewerly/exHarmony huggingface dataset:
Professional socialization of children in virtual museum space

The article analyzes the possibilities of competitive and exhibition activities in the virtual museum space for the development of professionally oriented interests and inclinations in children aged 7-14 years. A set of the most effective methods, techniques and forms of organizing competition and exhibition activities has been determined, a specially developed diagnostic toolkit has made it possible to analyze the experimental data obtained as a result of the use of empirical research methods: observation and survey. It was revealed that schoolchildren aged 7-14 are at different stages of professional maturity, from the stage of awakening (younger students) to the stage of professional socialization (younger adolescents). Experimental data have confirmed that competition and exhibition activities contribute to the development of creative thinking, self-actualization of the personality of schoolchildren. The huge educational potential of the competition and exhibition activity has been confirmed. The developed program of competitive and exhibition activities of a chemical orientation, contributing to the formation of professionally oriented interests and inclinations in children aged 7-14 years, will be useful to teachers, museum specialists.
Research on Text Summarization Generation Based on LSTM and Attention Mechanism

With the development of Internet technology, the phenomenon of text information overload occurs frequently, and automatic text summarization technology has become a research hotspot. However, in real situations, there is no enough data accumulation in many fields, and lack of high-quality labeled summarization data. Therefore, the paper realizes the generation of Chinese text summarization based on LSTM and attention mechanism, by utilizing LSTM to capture semantic features and combining the attention mechanism based on contextual semantics. Experiments show that the text summarization generation model we constructed has an obvious improvement in F1 value compared with other models. It can complete the task of text summarization generation and solve the problem of low-quality text summarization in some fields.
DiffGNN: Capturing Different Behaviors in Multiplex Heterogeneous Networks for Recommendation

Learning from multiplex heterogeneous networks is a crucial task in many real-world applications such as recommender systems. Usually, a multiplex heterogeneous network has multiple types of nodes and edges (or relations). Multiplex heterogeneous network embedding aims to learn from abundant structural and semantic information of a graph and embed nodes into low-dimensional representations. Existing works usually split the graph into several relation-specific subgraphs to distinguish different relations. However, these works either omit the important information of metapath in aggregation or fail to fully utilize the multiplex property in the network. To tackle the above challenges, we propose a novel model DiffGNN, which is designed to capture different behaviors in an elegant and efficient manner. DiffGNN adopts two powerful modules, i.e., the relation-specific attention (RsAtt) and metapath aware aggregation (MetAware), where MetAware aggregates information from different metapaths in each relation-specific subgraph and RsAtt combines and integrates the information with attentive weights. The experiments are conducted on three real-world datasets, and the experimental results show that our DiffGNN achieves significant improvement compared to the state-of-the-art models.
Can VM Live Migration Improve Job Throughput? Evidence from a Real World Cluster Trace

Cloud resource providers are putting more and more emphasis on efficiently management the resources of their data centers to achieve high utilization while minimizing energy consumption. Despite these efforts, an analysis of recent data center traces reveals that the utilization of CPU and memory resources has not improved significantly over the past decade. Resource overcommitment is a promising approach to improve resource utilization, because most workloads show a significant gap between their guaranteed and actually consumed resources. A wrong prediction of the actual usage, however, can lead to a severe performance degradation on overloaded nodes. Combining resource overcommitment with live migration of tasks can alleviate the situation, but its prohibitively high cost has so far prevented a wide adoption. Recent and rapid advancements in networking technology, however, are changing the status quo. With throughputs surpassing 100 Gb/s in 2021, even large tasks can be migrated within a few seconds. In light of these improvements, we believe it is time to rethink the application of resource overcommitment and live migration to improve data center resource utilization. Based on real-world cluster traces published by Google in 2019, we show that combining resource overcommitment with task live migration can reduce the mean task turnaround time by 16%, demonstrating that further research in this direction is both warranted and promising.
Detecting Organ Failure in Motor Vehicle Trauma Patients: A Machine Learning Approach

Motor vehicle accidents are prevalent throughout the U.S.; over five million crashes occur each year. Typically, patients are appropriately diagnosed only after being transported to nearby medical centers, which currently takes about 7–15 minutes on average. This project aims to expedite this diagnostic process by training neural networks on Intensive Care Unit (ICU) data to automate the identification of injuries caused by motor vehicle accidents. Data was aggregated from the Beth Israel Deaconess Medical Center in Boston, Massachusetts via the Medical Information Mart for Intensive Care III (MIMIC-III) database, for patients' electrocardiogram (ECG) and respiratory rate reports. Natural Language Processing was used to isolate the patients of interest and classify them based on the area of injury, i.e., any of eight combinations of the three vital organs: brain, heart, and lungs. Upon isolation of the waveform data, noise was filtered via normalization, Butterworth and forward-backward filter application, and Fast Fourier transformation. The trained Artificial Neural Network (ANN) contained 23 dense layers with the number of neurons decreasing per two layers with ReLU activation and 10% dropout. Multiclass activation was used for the final layer, in order to allow training for detection of multiple areas of trauma. The trained Convolutional Neural Network (CNN) had 12 convolutions, utilizing batch normalization, six pooling, one flatten, and two dense layers. Both models used AdamOptimizer. The CNN and ANN produced F1 scores of 0.82 and 0.56 respectively, suggesting the models could potentially perform accurate early detection of injury area and expedite hospital treatment of trauma patients.
Kalman Filter Based Extended Object Tracking with a Gaussian Mixture Spatial Distribution Model

Extended object tracking methods are often based on the assumption that the measurements are uniformly distributed on the target object. However, this assumption is often invalid for applications using automotive radar or lidar data. Instead, there is a bias towards the side of the object that is visible to the sensor. To handle this challenge, we employ a Gaussian Mixture (GM) density to model a more detailed measurement distribution across the surface and extend a recent Kalman filter based elliptic object tracker called MEM-EKF* to get a closed-form solution for the measurement update. An evaluation of the proposed approach compared with classic elliptic trackers and a recent truncation-based approach is conducted on simulated data.
Passages

Federico Biancuzzi and Shane Warden's Masterminds of Programming: Conversations with the Creators of Major Programming Languages is a treasure. The book consists of interviews with the creators of, in order, C++, Python, APL, Forth, BASIC, AWK, Lua, Haskell, ML, SQL, Objective C, Java, C#, UML, Perl, PostScript, and Eiffel. Each chapter asks similar, but not identical questions, and the above-mentioned masterminds, including Larry Wall, James Gosling, Brian Kernighan, Bertrand Meyer, Robin Milner, Simon Peyton-Jones, Guido van Rossum, and Bjarne Stroustrop give a wide variety of answers. Some of the masterminds are charming, and many are contentious, even cranky; they are also, almost all, full of deep insights into the deepest problems of software engineering. This insight comes in two forms; first, programming languages are the mechanisms by which software engineering solutions are almost always produced. Second, perhaps even more importantly, creating and evolving a widely-used programming language is a heroic, Herculean, critical software engineering task. All of these masterminds have succeeded in a massive software engineering task; they are not mere ivory tower thinkers about software engineering, but have, in some cases, entire lives shaped by a single, extremely complex, software project. More on that key point below.
Fuzzy Word Sense Induction and Disambiguation

An inherent property of natural languages is the possibility of distinct meanings for the same word in different sentences. Word sense induction (WSI) is the unsupervised process of discovering the meanings of a word. The meanings form a sense inventory, which is used for word sense disambiguation (WSD). Fuzzy logic’s capability at uncertainty representation makes it perfectly applicable for handling the vague information processed in natural languages for WSI and WSD. In this article, a novel fuzzy-based methodology is proposed for extracting meaningful information from ambiguous words, where both word senses and sense inventories are modeled as linguistic variables. The proposed method aims to gather a term set of level-2 fuzzy values for the variables representing words’ meanings, to achieve WSI. The values in the term set are, then, used for linguistic approximation using a fuzzy inference system designed for WSD based on word’s context. The fuzzy word senses are extracted from an input corpus by word substitution, i.e., predicting words suitable as substitutes for the target word using masked language models. These fuzzy substitute sets are, then, clustered to discover similarities in the semantics they represent. Finally, each cluster is reformed into a sense value and added to the term set for the target word. The experimental results show that the proposed system outperforms the systems submitted to the standard SemEval 2010 and 2013 WSI and WSD tasks and achieves comparable performance with other fuzzy and nonfuzzy state-of-the-art methods.
Edge-enhanced Generative Adversarial Network for Reconstruction of Compressed Image

The images are often compressed to reduce storage usage or accelerate image transmission. However, the compression process always results in the loss of image details, such as edge details, which degrades the visual experience. Plenty of reconstruction methods have been proposed, but it is yet challenging to enhance edge details more precisely. In this paper, we propose a GAN-based image reconstruction architecture mainly for edge enhancement. Our model improves by cycleGAN; the model's input adds the extracted edge of the compressed image to promote generating more precise edge information. To further optimize the image edge details, we define a new edge loss function to improve the quality of the generated image. Lastly, we train and test the images from the CelebA dataset and the ACDC medical dataset. The experimental results show that the reconstructed images are clear under the high compression ratio and have more precise image edge details.
Detecting Document Versions and Their Ordering in a Collection

Given the iterative and collaborative nature of authoring and the need to adapt the documents for different audience, people end up with a large number of versions of their documents. These additional versions of documents increase the required cognitive effort for various tasks for humans (such as finding the latest version of a document, or organizing documents), and may degrade the performance of machine tasks such as clustering or recommendation of documents. To the best of our knowledge, the task of identifying and ordering the versions of documents from a collection of documents has not been addressed in prior literature. We propose a three-stage approach for the task of identifying versions and ordering them correctly in this paper. We also create a novel dataset for this purpose from Wikipedia, which we are releasing to the research community (https://github.com/natwar-modani/versions). We show that our proposed approach significantly outperforms state-of-the-art approach adapted for this task from the closest previously known task of Near Duplicate Detection, which justifies defining this problem as a novel challenge.
Camera Trajectory Optimization for Maximizing Optical Character Recognition on Static Scenes with Text

Camera systems in motion are subject to significant blurring effects that lead to a loss of information during the image capture. This is especially damaging for optical character recognition for which edge preservation is critical to achieving a high recognition rate. Using non-blind motion deblurring, a trajectory and point spread function can be designed to maximize the recognition rate while meeting endpoint constraints. Optimization through the use of radial basis function networks can therefore be used as a way to find ideal trajectories to reduce blurring effects and preserve text sharpness. This paper investigates this problem using simulation of a blurred image capture process. The simulation is automated using radial basis function network optimization and a genetic algorithm to determine trajectories with the best recognition rate. Optimized trajectories yielded recognition scores with up to 65% improvement compared to a comparable linear profile. Results are then analyzed using spectral analysis to understand why the chosen trajectories preserve text edges. These findings can be applied to a wide variety of controlled mobile camera platforms, such as autonomous automobiles or unmanned aerial vehicles, to improve their ability to gather information from their environment.
Code Representation Based on Hybrid Graph Modelling

Several sequence- or abstract syntax tree (AST)-based models have been proposed for modelling lexical-level and syntactic-level information of source code. However, an effective method of learning code semantic information is still lacking. Thus, we propose a novel code representation method based on hybrid graph modelling, called HGCR. HGCR is a code information extraction model. Specifically, in HGCR, two novel graphs, the Structure Graph (SG) and the Execution Data Flow Graph (EDFG), are first extracted from AST to model the syntactic structural and semantic information of source code, respectively. Then, two improved graph neural networks are applied to learn the graphs to obtain an effective code representation. We demonstrate the effectiveness of our model on two common code understanding tasks: code classification and code clone detection. Empirically, our model outperforms state-of-the-art models.
On Deploying the Artificial Sport Trainer into Practice

Computational Intelligence methods for automatic generation of sport training plans in individual sport disciplines have achieved a mature phase. In order to confirm their added value, they have been deployed into practice. As a result, several methods have been developed for generating well formulated training plans on computers automatically that, typically, depend on the collection of past sport activities. However, monitoring the realization of the performed training sessions still represents a bottleneck in automating the process of sport training as a whole. The objective of this paper is to present a new low-cost and efficient embedded device for monitoring the realization of sport training sessions that is dedicated to monitor cycling training sessions. We designed and developed a new bike computer, i.e. the AST-Monitor, that can be mounted easily on almost every bicycle. The aforementioned bike computer is based on the Raspberry Pi device that supports different external sensors for capturing the data during the realization of sport training sessions. An adjusted GUI tailored to the needs of athletes is developed, along with the hardware. The proof of concept study, using the AST-Monitor in practice, revealed the potential of the proposed solution for monitoring of realized sport training sessions automatically. The new device also opens the door for the future utilization of Artificial Intelligence in a wide variety of sports.
Design of a Linear Layer for a Block Cipher Based on Type-2 Generalized Feistel Network with 32 Branches

In spite of the research for a linear layer of Type-2 Generalized Feistel Network (Type-2 GFN) over more than 10 years, finding a good 32-branch permutation for Type-2 GFN is still a very hard task due to a huge search space. In terms of the diffusion property, Suzaki and Minematsu investigated the required number of rounds to achieve the full diffusion when the branch number is up to 16. After that, Derbez et al. presented a class of 32-branch permutations that achieves the 9-round full diffusion and they prove that this is optimal. However, this class is not suitable to be used in Type-2 GFN because it requires a large number of rounds to ensure a sufficient number of active S-boxes. In this paper, we present how to find a good class of 32-branch permutations for Type-2 GFN. To achieve this goal, we convert Type-2 GFN into a LBlock-like structure, and then we evaluate the diffusion property and the resistance against major attacks, such as differential, linear, impossible differential and integral attacks by an MILP. As a result, we present a good class of 32-branch permutations that achieves the 10-round full diffusion, ensures differentially/linearly active S-boxes of 66 at 19 round, and has the 18/20-round impossible differential/integral distinguisher, respectively. The 32-branch permutation used in WARP was chosen among this class.
Supervised Sliding Window Smoothing Loss Function Based on MS-TCN for Video Segmentation

Recently, more and more videos have been uploaded to the network, so that video analysis task has been one of the most important applications in various fields. At present, video analysis methods can be divided into two kinds: weakly supervised video action segmentation and supervised video action segmentation. The former uses a sliding window or Markov model, while the latter uses the TCN model. In this paper, we introduce the Supervised Sliding Window Smooth Loss Function (SSWS) into the TCN baseline, which is a complement to MS-TCN smoothing loss function TMSE. In this method, three discriminant frames are selected from the video prediction sequence and combined into an adaptive sliding window to selectively smooth the whole prediction sequence. In particular, it doubles the penalty when it slides to the wrong place in the category. Compared to TMSE, our method effectively increases the receptive field of smoothing loss function. And, the proposed new supervised loss function only penalizes error frames. The experiment shows that compared with the Smoothing loss function TMSE of MS-TCN, SSWS has significantly improved in the three datasets: 50Salads, GTEA and the Breakfast Dataset.
Construction of Teaching Quality Evaluation System in Colleges and Universities Based on BP Neural Network

The core task of colleges and universities is teaching. How to improve the quality of teaching is the key for colleges and universities to send better talents to the society. Therefore, to improve the quality of teaching is of great importance to colleges and universities. Scientific and reasonable evaluation of teaching is an important way to improve teaching quality. Teaching management system is an important measure to improve work efficiency and unify management. Therefore, the development of university teaching management system and the establishment of Scientific University teaching quality evaluation system is an important measure to strengthen university teaching management and improve teaching quality. This paper first introduces the background knowledge of teaching quality evaluation, and analyzes the advantages and disadvantages of various traditional evaluation methods. Then the paper introduces the Teaching Quality Evaluation System based on BP neural network. According to the actual characteristics of teaching work in colleges and universities and the requirements of educational development for teaching quality, a reasonable and scientific evaluation index system is established, and BP neural network is introduced into the quality evaluation of teaching in colleges and universities, which verifies the reliability of the model.
Research on Intelligent Health Diagnosis of Tunnel Structure System Based on Wavelet Neural Network

This paper proposes to establish a model of wavelet neural network to diagnose and predict the health of the tunnel structure. First, the dynamic response signal of the tunnel structure can be collected by wavelet transform, so the feature that best reflects the defect structure can be extracted. The node coefficient of wavelet packet can be used as the feature input vector of the neural network structure. Establish a wavelet neural network model, adjust the MSE value of the model by adjusting the relevant network and setting parameters, get the optimal model, and finally, use the trained neural network model to simulate effectively. The results show that this method has a good effect on the health diagnosis of the tunnel structure, and it can be used in subsequent engineering practice to be popularized.
Group-Based Deep Reinforcement Learning in Multi-UAV Confrontation

The application of deep reinforcement learning (DRL) algorithms in multi-agent environments has become more and more popular. However, most DRL algorithms do not solve the problem of group cooperation. Each agent explores in a direction that is beneficial to itself, but ignores the situation of its teammates, which is easy to fall into the local optimum. This paper aims to solve this problem in a multi-UAV confrontation scenario. We try to find the optimal cooperative policy by dividing UAVs into several groups and make UAVs learn to cooperate with teammates autonomously. Specifically, we propose an algorithm called group-based actor-critic (GBAC). We group UAVs by setting the observation radius, and we use a double Q network to process rewards. We divide rewards into individual rewards and group rewards. The Q network is used to process individual rewards, and the group-Q network is used to process group rewards. As a result, UAVs can get higher rewards through group cooperation. The performance of UAVs trained by our method outperforms other DRL methods. In this paper, we use the group-based DRL method to solve the problem of group cooperation and maximize the expected return in multi-UAV confrontation.
Residual Geometric Feature Transform Network for 3D Surface Super-Resolution

In 3D reconstruction, how to recover high-resolution 3D surface details from the existing low-resolution 3D surface is still a challenging problem. Due to the unstructured and irregular characteristics of 3D data, it is usually difficult to obtain extremely dense 3D surface and capture detailed local features. To tackle this problem, this article introduces an effective deep convolutional network, namely RGFTNet, to perform 3D surface super-resolution in 2D normal domain. To restore dense surface details and learn sharp geometry structures simultaneously, a shape prior acquisition method is designed to achieve the high-quality shape normal from the input low-resolution one. Subsequently, the extracted shape normal as the shape prior is incorporated into a deep convolutional network through the Geometric Feature Transform (GFT) layer. Experimental results show the superiority of the proposed RGFTNet over several recent advances on both the computer-generated and the real-world data.