Reviewerly/exHarmony huggingface dataset:
A Virtual Machine Placement Strategy with Low Resource Consumption

A virtual machine placement strategy based on sine and cosine perturbation and reverse learning particle swarm optimization is proposed to solve the problem of insufficient optimization of internal resource consumption in data center. First of all, the integer encoding method is used to solve the shortcoming of the tedious operation of binary encoding on the virtual machine placement problem. Secondly, the quality of the initial solution is improved by the inverse learning strategy to initialize the population, the method of sine and cosine perturbation is used to avoid the particle swarm optimization algorithm falling into the locally optimal solution, and the ability of exploration and development is explored by the open downward parabola adaptive adjustment. Then, with minimizing resource consumption as the optimization goal, a constrained optimization model for virtual machine placement in the data center is established. Finally, the relevant experiments prove that this strategy can effectively reduce resource consumption and ensure service quality, and it has a good application prospect.
A small-spot deformation camouflage design algorithm based on background texture matching

In order to solve the problem of poor fusion between the spots of deformation camouflage and the background, a small-spot deformation camouflage design algorithm based on background texture matching is proposed in this research. The combination of spots and textures improved the fusion of the spot pattern and the background. An adversarial autoencoder convolutional network was designed to extract background texture features. The image adversarial loss was added and the reconstruction loss was improved to improve the clarity of the generated texture pattern and the generalization ability of the model. The digital camouflage was formed by obtaining the mean value of the square area and replacing the main color. At the same time, the spots in the square area with a side length of 2 s were subjected to simple linear iterative clustering to form irregular small-spot camouflage. A dataset with a scale of 1050 was established in the experiment. The training results of three different loss functions were investigated. The results showed that the proposed loss function could enhance the generalization of the model and improve the quality of the generated texture image. A variety of digital camouflages with main colors and irregular small-spot camouflage were generated, and their efficiency was tested. On the one hand, intuitive evaluation was given by personnel observing the camouflage pattern embedded in the background and its contour map calculated by the canny operator. On the other hand, objective comparison result was formed by calculating the 4 evaluation indexes between the camouflage pattern and the background. Both results showed that the generated pattern had a high degree of fusion with the background. This model could balance the relationship between the spot size, the number of main colors and the actual effect according to actual needs.
Analysis of Collective Intention based on Knowledge Graph

In the course of combat, the analysis of target intention is one of the important factors to obtain decision-making superiority. However, the fast increment of the number of various targets in the battlefield space brings challenges to intention analysis. In the pattern of joint operation, operational units usually organize in the form of groups and coordinate with each other based on unified tasks, so that targets in a group will show a unified collective intention. In this paper, we propose a knowledge graph based method to analyze collective intention and introduce the specific process by taking aircraft groups as an example. Firstly, characteristics which may affect collective intention are discussed. Secondly, structural features of the aircraft formation and other characteristics are extracted to construct an aircraft group knowledge graph, in which relations between the above information and common collective intentions are assigned as well. Finally, collective intentions are inferred comprehensively through the Savage criterion based on the knowledge graph and the rationality of the method is demonstrated by two application examples.
Effects of Virtual Reality Exposure Therapy on Dentophobia in Clients of Dental Offices in Isfahan, Tehran, and Shahrekord (Iran)

Background: Dentophobia can reduce the number of regular visits to dentists and endanger the oral health of people. New technologies such as virtual reality (VR) can be used in the treatment of psychological problems such as dentophobia. Objectives: The present study aimed to investigate the effectiveness of virtual reality exposure therapy (VRET) on dentophobia in clients of dental offices in the cities of Isfahan, Tehran, and Shahrekord in 2020. Methods: The research method was quasi-experimental with a pre-test, post-test, and follow-up design and a control group. The statistical population of this study consisted of all clients with dentophobia visiting private dental clinics of Isfahan, Tehran, and Shahrekord in 2020. Thirty clients with dentophobia who were willing to participate in the research were selected as the sample using convenience sampling and randomly divided into experimental and control groups (n = 15 per group). The experimental group underwent eight sessions (15-minutes sessions per week) of virtual reality exposure therapy, while the control group received no intervention. The follow-up was performed after 45 days. The research instruments included the Modified Dental Anxiety Scale (MDAS), Samsung Gear 360 (2017 Edition), and HTC VIVE Pro Virtual Reality Headset. Data were analyzed using repeated-measures ANOVA. Results: The results showed that virtual reality exposure therapy effectively decreased dentophobia in clients of dental offices in the experimental group (P = 0.0001). Conclusions: According to research findings, virtual reality exposure therapy was an efficient therapy for improving dentophobia and reducing phobia symptoms in clients of dental offices.
Distant Finetuning with Discourse Relations for Stance Classification

Approaches for the stance classification task, an important task for understanding argumentation in debates and detecting fake news, have been relying on models which deal with individual debate topics. In this paper, in order to train a system independent from topics, we propose a new method to extract data with silver labels from raw text to finetune a model for stance classification. The extraction relies on specific discourse relation information, which is shown as a reliable and accurate source for providing stance information. We also propose a 3-stage training framework where the noisy level in the data used for finetuning decreases over different stages going from the most noisy to the least noisy. Detailed experiments show that the automatically annotated dataset as well as the 3-stage training help improve model performance in stance classification. Our approach ranks 1\(^{\text {st}}\) among 26 competing teams in the stance classification track of the NLPCC 2021 shared task Argumentative Text Understanding for AI Debater, which confirms the effectiveness of our approach.
Incorporate Lexicon into Self-training: A Distantly Supervised Chinese Medical NER

Medical named entity recognition (NER) tasks usually lack sufficient annotation data. Distant supervision is often used to alleviate this problem, which can quickly and automatically generate annotated training datasets through dictionaries. However, the current distantly supervised method suffers from noisy labeling due to limited coverage of the dictionary, which will cause a large number of unlabeled entities. We call this phenomenon an incomplete annotation problem. To tackle the incomplete annotation problem, we propose a novel distantly supervised method for Chinese medical NER. Specifically, we propose a high recall self-training mechanism to recall potential unlabeled entities in the distant supervision dataset. To reduce error in the high recall self-training, we propose a fine-grained lexicon enhanced scoring and ranking mechanism. Our method improves 3.2% and 5.03% compared to the baseline models on the dataset we proposed and a benchmark dataset for Chinese medical NER.
Joint Embedding of Words and Labels for Sentiment Classification

With the fast growth of social networks, sentiment analysis on the web has been a popular research topic. Recently, word embedding-based sentiment analysis methods have reached outstanding performance compared to the traditional methods. However, word embeddings always ignore information from dataset’s labels. Inspired by the LEAM model proposed by Wang et al. [Joint embedding of words and labels for text classification (2018), arXiv:1805.04174], we propose a method that jointly learns information of words and sentiment labels, which can improve the performance of the label embedding model. We defined a set of sentiment lexicons and used it to represent sentiment labels in the proposed method. We finally conducted experiments on the Yelp dataset, which reached 65.03% accuracy when using the same setup as the baseline model, and 65.22% accuracy when using optional window sizes.
FocusGAN: Preserving Background in Text-Guided Image Editing

Text-guided image editing (TIE) seeks to manipulate images by the guidance of language. However, the existing TIE methods always overlook the target-irrelevant pixels and the editing may make the background discolored, distorted, or partially disappear. To overcome this problem, we propose a novel TIE method named FocusGAN, which edits the text-relevant pixels precisely as well as keeps the background invariant. Specifically, we build a network of two stages. In each stage, we first construct a channel-wise subject focusing attention to make the generator focus on the sub-region that best matches each word. Then, the word-level discriminator provides fine-grained feedback by correlating words with image areas, so that the generator can manipulate specific visual attributes without affecting the background. Last, we propose a background-keeping cyclic loss to further improve the invariance of the background and to encourage the edit of the subject that matches the given text. Experiments on CUB and Oxford datasets demonstrate that our approach can effectively keep the background invariant in manipulating images using natural language descriptions.
Pedagogical Conditions of Media Education in Primary School

The media and its use in the educational process, which has global importance, are the twenty-first century's challenge. In the pedagogical process, media education is addressed to each child within the context of any subject, ensuring the academic subject's required objectives and successful instructional activity, as well as the final results. Media is a tool in the pedagogical process for early learning games, aesthetic education, language skills, and mathematics, religion or ethics. It is not enough to have the developed equipment to use the media. In elementary school, the classroom equipped with appropriate media is more attractive for the students; it will facilitate the educational, developing and upbringing process and make it more stimulating. For the development of imagination, creativity, and independent search skills in elementary school, the supervised use of media during the educational process is particularly important. The media equipment is selected in accordance with the secondary school's curriculum, with targets set and the experience evaluated regularly. The controlled use of technological equipment is necessary in this case. Students' cognitive and artistic skills might be harmed by excessive media use in elementary school. It is justified that today's primary school students are extremely informed due to the media, their thinking skills are more developed. Today, there is a need for parent-teacher support processes for the child, strict content control, and pedagogical guidance from computer &amp; Internet prevention. The use of technologies in the educational process is one of the urgent problems of modern pedagogy. The introduction of media technology in primary school has both advantages and disadvantages. Media education helps students navigate the information. They contribute to the development of positive motivation for learning, activate the cognitive, research and creative activities of younger schoolchildren, but can harm their health and negatively affect their worldview. The fact is that the 21st century is the age of high technologies. Therefore, education, which provides a high level of general knowledge, should be replaced by education focused on the creative development of each student's personality. Therefore, today it is necessary to revise the content of higher education, methods and types of education, that is, to apply pedagogical innovation.
Bendable Color ePaper Displays for Novel Wearable Applications and Mobile Visualization

This paper presents a toolkit that allows to easily prototype with bendable color ePaper displays for designing and studying novel body-worn interfaces in mobile scenarios. We introduce a software and hardware platform that enables researchers for the first time to implement fully-functional wearable and UbiComp applications with interactive, curved color pixel displays. Further, we provide a set of visual and sensory-rich materials for customization and mounting options. To technically validate our approach and demonstrate its promising potential, we implemented eight real-world applications ranging from personal information and mobile data visualizations over active notifications to media controls. Finally, we report on first usage experiences and conclude with a research roadmap that outlines future applications and directions.
Spices and Digital Competence in the Education in Mathematics and Information Technology

In this article is presented the usage of a dynamical software product, with which can be created applets concerning the world of spices. The dynamical product allows experimenting with the parameters of the objects, their positions, colors etc. Today’s information technology can be used in different areas of knowledge and motivate the students. The application of GeoGebra in the educational process can help the development of digital competences.
Radar emitter recognition based on CNN and LSTM

To solve the problem of low recognition efficiency under the low SNR of radar emitter signal recognition, CNN and LSTM are used to realize the recognition of signals in different intra-pulse modulation modes. Firstly, this paper achieves the local characteristics of the signals with CNN. Then capture the global characteristics with LSTM. Finally, construct the logical regression classification to complete the classification and recognition task. The simulation results show that when the SNR is -6dB, the overall recognition accuracy can reach 98%, and when the SNR is greater than -2dB, the accuracy rate is up to 100%. To verify the effect of different CNN layers and LSTM layers, comparative experiments are carried out. The results show that the appropriate increase of convolution layers is beneficial to improve the accuracy, while the lack of LSTM was not conducive to classification and recognition.
Towards the Efficient Multi-Platform Execution of Deep Neural Networks

Modern Systems-on-Chip (SoCs) based on Field-Programmable Gate Arrays (FPGAs) offer users significant flexibility in deciding the best approach to implement Convolutional Neural Networks (CNNs): a) in a fixed, hardwired general-purpose processor, or b) using the programmable logic to implement application-specific processing cores. This thesis proposes an automated toolflow that maps Tensorflow/Keras pre-trained models into different possible platforms: ARM core using the Neon extension and a soft-core GPU for FPGA. CNNs are heterogeneous, meaning that convolutional layers, for example, will have different resource access and computation requirements as the Fully Connected (FC) layers, hinting that different hardware may be optimal for different layer types. After evaluating the performance of different CNNs executed in an ARM Cortex-A9 and the soft-core GPU, it was found that convolutional layers were 5.9x faster in the soft-core GPU than in the ARM core. On the other hand, FC layers were executed faster in the ARM core. As a result, this work proposes a collaborative execution of CNNs using these two platforms together, running the convolutional and maxpooling layers in the soft-core GPU and the FC layers in the ARM core, achieving a speedup of 2x against using only the ARM core. Consequently, this thesis is exploring other mixes of hardware platforms or even using partial reconfiguration techniques.
Sandwich Batch Normalization: A Drop-In Replacement for Feature Distribution Heterogeneity

We present Sandwich Batch Normalization (SaBN), a frustratingly easy improvement of Batch Normalization (BN) with only a few lines of code changes. SaBN is motivated by addressing the inherent feature distribution heterogeneity that one can be identified in many tasks, which can arise from data heterogeneity (multiple input domains) or model heterogeneity (dynamic architectures, model conditioning, etc.). Our SaBN factorizes the BN affine layer into one shared sandwich affine layer, cascaded by several parallel independent affine layers. Concrete analysis reveals that, during optimization, SaBN promotes balanced gradient norms while still preserving diverse gradient directions – a property that many application tasks seem to favor. We demonstrate the prevailing effectiveness of SaBN as a drop-in replacement in four tasks: conditional image generation, neural architecture search (NAS), adversarial training, and arbitrary style transfer. Leveraging SaBN immediately achieves better Inception Score and FID on CIFAR-10 and ImageNet conditional image generation with three state-of-the-art GANs; boosts the performance of a state-of-the-art weight-sharing NAS algorithm significantly on NAS-Bench-201; substantially improves the robust and standard accuracies for adversarial defense; and produces superior arbitrary styl-ized results. We also provide visualizations and analysis to help understand why SaBN works. Codes are available at: https://github.com/VITA-Group/Sandwich-Batch-Normalization.
Rapid Face Image Retrieval for Large Scale Based on Spark and Machine Learning

This paper proposed a distributed computing environment large-scale face image retrieval method. Based on Spark platform, a face retrieval method based on machine learning is established, which takes advantage of parallel computing to improve the efficiency of face image retrieval. In the method, SIFT algorithm is used for feature coding, PCA dimension reduction processing, HBase database is used for data storage, and KD-Tree query algorithm is used to match images similar to the query images. Meanwhile, large-scale computing engine uses Spark to process data to improve the retrieval efficiency. The CelebA dataset is selected to test the method, and the experimental results show the effectiveness of the method.
RTJTN: Relational Triplet Joint Tagging Network for Joint Entity and Relation Extraction

Extracting entities and relations from unstructured sentences is one of the most concerned tasks in the field of natural language processing. However, most existing works process entity and relation information in a certain order and suffer from the error iteration. In this paper, we introduce a relational triplet joint tagging network (RTJTN), which is divided into joint entities and relations tagging layer and relational triplet judgment layer. In the joint tagging layer, instead of extracting entity and relation separately, we propose a tagging method that allows the model to simultaneously extract entities and relations in unstructured sentences to prevent the error iteration; and, in order to solve the relation overlapping problem, we propose a relational triplet judgment network to judge the correct triples among the group of triples with the same relation in a sentence. In the experiment, we evaluate our network on the English public dataset NYT and the Chinese public datasets DuIE 2.0 and CMED. The F1 score of our model is improved by 1.1, 6.0, and 5.1 compared to the best baseline model on NYT, DuIE 2.0, and CMED datasets, respectively. In-depth analysis of the model's performance on overlapping problems and sentence complexity problems shows that our model has different gains in all cases.
Semantic-Based Classification of Long Texts on Higher Education in China

The development level of higher education (HE) is an important indicator of the development level and development potential of a country. The HE-related document is the mirror to reflect the develop process of the HE. The research of high education (HE) has been developing rapidly in China, resulting in a huge number of texts, such as relevant policies, speech drafts, and yearbooks. The traditional manual classification of HE texts is inefficient and unable to deal with the huge number of HE texts. Besides, the effect of direct classification is rather poor because HE texts tend to be long and exist as an imbalanced dataset. To solve these problems, this paper improves the convolutional neural network (CNN) into the HE-CNN classification model for HE texts. Firstly, Chinese HE policies, speech drafts, and yearbooks (1979–2020) were downloaded from the official website of Chinese Ministry of Education. In total, 463 files were collected and divided into four classes, namely, definition, task, method, and effect evaluation. To handle the huge number of HE texts, the Twitter-latent Dirichlet allocation (LDA) topic model was employed to extract word frequency and critical information, such as age and author, enhancing the training effect of CNN. To address the dataset imbalance problem, CNN parameters were optimized repeatedly through comparative experiments, which further improve the training effect. Finally, the proposed HE-CNN model was found more effective and accurate than other classification models.
Don’t Blindly Trust Your CNN: Towards Competency-Aware Object Detection by Evaluating Novelty in Open-Ended Environments

Real-world missions require robots to detect objects in complex and changing environments. While deep learning methods for object detection are able to achieve a high level of performance, they can be unreliable when operating in environments that deviate from training conditions. However, by applying novelty detection techniques, we aim to build an architecture aware of when it cannot make reliable classifications, as well as identifying novel features/data. In this work, we have proposed and evaluated a system that assesses the competence of trained Convolutional Neural Networks (CNNs). This is achieved using three complementary introspection methods: (1) a Convolutional Variational Auto-Encoder (VAE), (2) a latent space Density-adjusted Distance Measure (DDM), and (3) a Spearman’s Rank Correlation (SRC) based approach. Finally these approaches are combined through a weighted sum, with weightings derived by maximising the correct attribution of novelty in an adversarial ‘meta-game’. Our experiments were conducted on real-world data from three datasets spread across two different domains: a planetary and an industrial setting. Results show that the proposed introspection methods are able to detect misclassifications and unknown classes indicative of novel features/data in both domains with up to 67% precision. Meanwhile classification results were either maintained or improved as a result.