Reviewerly/exHarmony huggingface dataset:
Scalable Direction-Search-Based Approach to Subspace Clustering

Subspace clustering finds a multi-subspace representation that best fits a high-dimensional dataset. The computational and storage complexities of existing algorithms limit their usefulness for large scale data. In this paper, we develop a novel scalable approach to subspace clustering termed Fast Direction-Search-Based Subspace Clustering (Fast DiSC). In sharp contrast to existing scalable solutions which are mostly based on the self-expressiveness property of the data, Fast DiSC rests upon a new representation obtained from projections on computed data-dependent directions. These directions are derived from a convex formulation for optimal direction search to gauge hidden similarity relations. The computational complexity is significantly reduced by performing direction search in partitions of sampled data, followed by a retrieval step to cluster out-of-sample data using projections on the computed directions. A theoretical analysis underscores the ability of the proposed formulation to construct local similarity relations for the different data points. Experiments on both synthetic and real data demonstrate that the proposed algorithm can often outperform the state-of-the-art clustering methods.
호텔경영학 교육과정에서 주말현장실습 운영방안 연구: D대학 호텔경영학전공 운영 사례 중심 현상학적 접근

This study aims to explain the importance of field training raised in the curriculum of the hotel management major, especially to consider the introduction of an operating system in the form of weekend field training. Experimental cases we tried to derive the implications through in-depth interview of 7 students, and research was conducted according to a phenomenological approach. First, looking at the motives for participation in the weekend field training, most of the cases were aimed at linking to employment or industry experience, and a combination of economic goals was accompanied by a complex choice. The activity content of the weekend field training was not much different from that of the existing hotel field training, so it was mainly in charge of auxiliary work for front-line workers in field work such as concierge and F&B service, and served as a practice process for career advancement. The operational results of the weekend field training include help in making career decisions for students, establishing a personal network with employees in the hotel, linking to long-term field training after the weekend field training as a short-term field training, salary paid by the hotel, and government subsidies. In terms of problems and improvement directions of weekend field training, curriculum formation to relieve physical and mental fatigue in combination with education/practice, difficulty in forming relationships with workers, and various experience opportunities through rotational work, uncertainty in the process linking practice, and due to the lack of preparation in advance, it was required to establish a systematic linkage system and reinforcement of additional educational content during the week.
Efficient Game-Theoretic Hypergraph Matching

Feature matching is a fundamental problem in computer vision. Compared with graph matching, hypergraph matching is able to encode more invariance between correspondences. Different from the majority of existing hypergraph matching algorithms, a game-theoretic algorithm has been developed by transforming hypergraph matching to hypergraph clustering, which is then solved within the framework of a non-cooperative multi-player clustering game. This algorithm obtains the final matches as a cluster of consistent candidate matches and has high accuracy and robustness to outliers in comparison with other competitors. However, in further works we find that this algorithm tends to generate a small number of matches, and the increase of number of matches can only be obtained at the cost of a huge computation load. Our investigation of the algorithm shows that it has a large requirement of internal similarity in a cluster, and therefore generates small clusters of high density. This motivates us to expand the cluster so that more candidate matches are accepted as final matches. For this purpose, we define the density of vertices in a hypergraph and expand the cluster based on relative density relationship among the vertices. In matching experiments with both synthetic and real datasets, our algorithm is shown to generate the same number of or more matches with much less running time in comparison with the original algorithm. Meanwhile, it preserves the advantage of high accuracy and robustness to outliers in comparison with some competitors.
Recognizing Multiple Text Sequences from an Image by Pure End-to-End Learning

We address a challenging problem: recognizing multiple text sequences from an image by pure end-to-end learning. It is twofold: 1) Multiple text sequences recognition. Each image may contain multiple text sequences of different content, location and orientation, we try to recognize all these texts in the image. 2) Pure end-to-end (PEE) learning. We solve the problem in a pure end-to-end learning way where each training image is labeled by only text transcripts of the contained sequences, without any geometric annotations. Most existing works recognize multiple text sequences from an image in a non-end-to-end (NEE) or quasi-end-to-end (QEE) way, in which each image is trained with both text transcripts and text locations. Only recently, a PEE method was proposed to recognize text sequences from an image where the text sequence was split to several lines in the image. However, it cannot be directly applied to recognizing multiple text sequences from an image. So in this paper, we propose a pure end-to-end learning method to recognize multiple text sequences from an image. Our method directly learns the probability distribution of multiple sequences conditioned on each input image, and outputs multiple text transcripts with a well-designed decoding strategy. To evaluate the proposed method, we construct several datasets mainly based on an existing public dataset and two real application scenarios. Experimental results show that the proposed method can effectively recognize multiple text sequences from images, and outperforms CTC-based and attention-based baseline methods.
U-Net for Satellite Image Segmentation: Improving the Weather Forecasting

The clouds organization plays a huge role in forecasting the weather and Earth's future climate; therefore developing a better intelligent model is a way to accurately predict weather and predict weather and meteorological disasters, such as hurricane and tornado. In this paper, we classified the patterns of clouds into four types (sugar, flower, fish, and gravel) proposed by Rasp et al. and performed image segmentation. All the datasets were adopted from the Kaggle Competition. U-net was used as the basic structure and ResNet was applied to the original U-net structure after the data analysis. In addition, three different loss functions were used for training, the Test-time Augmentation was performed before feeding the test data to the model and the Amendment method was used to modify the results. The final dice coefficient reaches up to 0.665, which is an outstanding outcome that reflects the robustness of our method and training.
Energy Minimum Regularization in Continual Learning

How to give agents the ability of continuous learning like human and animals is still a challenge. In the regularized continual learning method OWM, the constraint of the model on the energy compression of the learned task is ignored, which results in the poor performance of the method on the dataset with a large number of learning tasks. In this paper, we propose an energy minimization regularization(EMR) method to constrain the energy of learned tasks, providing enough learning space for the following tasks that are not learned, and increasing the capacity of the model to the number of learning tasks. A large number of experiments show that our method can effectively increase the capacity of the model and reduce the sensitivity of the model to the number of tasks and the size of the network.
Rooftop photovoltaics potential assessment：A framework to integrate technological, economic, environmental and social benefit

Buildings have a significant impact on global and local energy demand and climate change. Consequently, the issue of photovoltaic (PV) potential assessment of building roofs have been extensively studied. Although studies have adopted different methods to predict the various potentials of rooftop PVs, as yet there is no understanding of a comprehensive framework for evaluating the potential of rooftop PVs. This study aims to develop a new framework to evaluate the technical, economic, environmental, and social benefits of rooftop PVs in old communities in Nanjing. The study found that the total annual electricity generation of rooftop PVs in the two districts of Nanjing was 431,136,314 KWh, which can meet about 20% of the residential electricity demand in the study area; the total electricity generation in the life cycle of 25 years was 5327909814 KWh; The economic benefit was 4.06 yuan/wp; the CO2 emission efficiency was 20.02%; the social benefit was 11.224 yuan/KWh. The results of this study will potential provide valuable reference suggestions for urban planners from the perspective of the benefits brought by rooftop PV in the two districts.
A Maximum-Flow Model for Digital Elastica Shape Optimization

The Elastica is a curve regularization model that integrates the squared curvature in addition to the curve length. It has been shown to be useful for contour smoothing and interpolation, for example in the presence of thin elements.
Recursive Recognition of Offline Handwritten Mathematical Expressions

In this paper we propose a method for Offline Handwritten Mathematical Expression recognition. The method is a fast and accurate thanks to its architecture, which include both a Convolutional Neural Network and a Recurrent Neural Network. The CNN extracts features from the image to recognize and its output is provided to the RNN which produces the mathematical expression encoded in the LATEX language. To process both sequential and non-sequential mathematical expressions we also included a deconvolutional module which, in a recursive way, segments the image for additional analysis trough a recursive process. The results obtained show a very high accuracy obtained on a large handwritten data set of 9100 samples of handwritten expressions.
Prediction of water-light output in water-light complementary systems

With the increasing demand of electricity consumption for social development, China has vigorously promoted the development of renewable energy such as wind and solar energy in recent years. Since photovoltaic power generation has the characteristics of randomness, intermittency and volatility, which are not conducive to dispatching and grid-connection, and hydroelectric power generation has the characteristics of fast start-up and good peak adjustment, etc. Therefore, through the study of multiple linear regression model neural network prediction model Markov chain and other methods, the hydraulic resource output prediction model and photovoltaic resource output prediction model are built. The combination of the two can be used to predict the output of water and light resources more accurately and promote the development of water and light complementary industry in China. At present, there are few researches on the coupling of hydroelectric output and photovoltaic output at home and abroad. In this paper, the prediction of water-light output in water-light complementary systems by other scholars is reviewed.
Unsupervised Contrastive Photo-to-Caricature Translation based on Auto-distortion

Photo-to-caricature translation aims to synthesize the caricature as a rendered image exaggerating the features through sketching, pencil strokes, or other artistic drawings. Style rendering and geometry deformation are the most important aspects in photo-to-caricature translation task. To take both into consideration, we propose an unsupervised contrastive photo-to-caricature translation architecture. Considering the intuitive artifacts in the existing methods, we propose a contrastive style loss for style rendering to enforce the similarity between the style of rendered photo and the caricature, and simultaneously enhance its discrepancy to the photos. To obtain an exaggerating deformation in an unpaired/unsupervised fashion, we propose a Distortion Prediction Module (DPM) to predict a set of displacements vectors for each input image while fixing some controlling points, followed by the thin plate spline interpolation for warping. The model is trained on unpaired photo and caricature while can offer bidirectional synthesizing via inputting either a photo or a caricature. Extensive experiments demonstrate that the proposed model is effective to generate hand-drawn like caricatures compared with existing competitors.
Analysis of Image Generation by different Generator in GANs

Abstract GAN is very useful in the field of image generation. Many related GANs have been proposed for generating images from the description. However, the research about analysis of image generation by different Generator in GANs is still insufficient. In this paper, different methods such as CNN and Resnet are used as the Generator in GANs for image generation. The subjective evaluation is used to analyze the performance of different generators of GAN. The result shows that the CNN-based generator performs better than the Resnet-based generator. And the increase of the number of parameters in the model will change the image quality. The analysis of different methods as the generator in GAN has a great referential significance in the field of image generation.
”You Should Probably Read This”: Hedge Detection in Text

Humans express ideas, beliefs, and statements through language. The manner of expression can carry information indicating the author’s degree of confidence in their statement. Understanding the certainty level of a claim is crucial in areas such as medicine, finance, engineering, and many others where errors can lead to disastrous results. In this work, we apply a joint model that leverages words and part-of-speech tags to improve hedge detection in text and achieve a new top score on the CoNLL-2010 Wikipedia corpus.
Negotiation in Hidden Identity: Designing Protocol for Werewolf Game

The automation of complex negotiations is required to coordinate various AIs. In complex negotiations, the goals of each agent may not be shared because some agents may benefit from not disclosing their own information during negotiation. This uncooperative situation requires an agent to have the ability to infer the intention of the others from the communication, and also to persuade the others through communication. Such negotiation in hidden identity is a pioneering area on negotiation research. In the past, the authors have proposed an AI competition in the Werewolf game in order to research negotiation in hidden identity. To eliminate noise caused by the difficulty of handling natural language, it is necessary to create a model of communication between agents in such a competition. In this paper, we analyze the elements necessary for communication between agents in the Werewolf game and design the protocol between agents based on this analysis.
Semantic-Syntax Cascade Injection Model for Aspect Sentiment Triple Extraction

Aspect sentiment triple extraction aims to extract all aspects, opinions, and sentiments in a sentence and pair them into triples. The main challenge lies at mining the dependency between the aspect and corresponding opinion with the specific sentiment. Existing methods capture the dependency via either pipeline framework or collapsed sequence labeling model. However, the pipeline framework may suffer from error propagation, while collapsed tags cannot deal with complex pairing situations where the overlap or long dependency exists. In this paper, we propose a novel semantic-syntax cascade injection model (SSCIM) to address above issues. SSCIM adopts a cascade framework with joint training schema, where its lower layer extracts the aspects and injects those aspects into the upper layer to extract opinion and sentiment simultaneously. Such design is inspired by the fact that the sentiment is often conveyed in opinions, and the joint training schema alleviates error propagation effectively. Moreover, a novel semantic-syntax information injection gate (IIG) is designed to bridge the upper and lower layers of our model, enabling SSCIM to better capture the dependency between aspects and opinions. Experimental results on four benchmark datasets demonstrate the superior performance of the proposed model over state-of-the-art baselines.
An Improved Automatic Extraction of Chinese Mathematical Terminology with Iterated Dilated Residual Gated Convolutions

Automatic term extraction (ATE) is the primary work in ontology construction, text summarization, and knowledge graph. For automatic extraction of Chinese mathematical terminology, we propose an improved model base on Robustly Optimized BERT Pre-training Approach (RoBERTa), Iterated Dilated Residual Gated CNN (IDRG-CNN), and Bidirectional LSTM with a CRF layer (BiLSTM-CRF). To evaluate the model, we annotate a corpus of Chinese terms for probability theory and mathematical statistics. We divide the annotated corpus into a training set, verification set, and test set and ensure that the terms of the three data sets are not repeated to evaluate the model's generalization ability. Empirical results show that this model can effectively extract terms and reveal that the model has strong generalization ability, particularly which improves the ability to recognize long terms.
Human Speech and Text Emotion Analysis : A Survey on Emotion Analysis using Deep Neural Networks

Human Emotion Analysis is an extremely wide area of study which can have several implementations for various applications. To work on any kind of project related to Emotion detection it is imperative to collect information on the available models, datasets and performance statistics related to them. This review analysis aims to consolidate a set of unique as well as state-of-the-art approaches that have been proposed and/or carried out by various individuals in this field. The proposed research work has consolidated studies across different datasets majorly focusing on the standard The Interactive Emotional Dyadic Motion Capture (IEMOCAP) dataset and Social media datasets. Our survey spans multiple modes of data such as text, speech, speech transcriptions [6] and motion capture [10] to provide a more in-depth analysis of the accuracies and improvement achieved across mentioned modes of data. Models based on Convolution Neural Networks (CNN) [6][8] and Recurrent Neural Networks(RNN) [10] have been reviewed upon among others reflecting the efficiency of each model.
Research on Spam Web Page Detection Based on Unbalanced Data Processing

Most Internet users are used to paying attention to a few websites with the top ranking of search engines. According to statistics, 95% of users are only interested in the search results of the first five pages, and a large number of websites with the lower ranking are selectively ignored by users. The purpose of this paper is to make use of the discrimination results of multiple classifiers, and build an effective combination classifier for spam web page recognition from several aspects such as feature selection, feature segmentation and classifier difference measurement. The new two-view features are combined in different ways to generate single-view data, and this set of data is used as training data to construct classification algorithm. Experimental results show that the proposed method can effectively identify illegal publishers who commit fraudulent clicks, and meet the requirements of click fraud detection in online advertising.
Cyber technologies in organized crime investigations

Today's human population got dependable on cyber technologies bringing us a lot of benefits as well as nightmares. Some studies suggest that there are several billions internet connections worldwide and they are not only in hands of the Legal Forces, but rather with the criminals, terrorists and the other harmful actors. So, as we all are dependable on new technologies - our opponents are at the same position as well. Through this research article, we plan to talk about the challenges of the organized crime to modern societies, their impacts to world's population, culture and economy and finally provide some correlations between the cyberspace and the criminal environment suggesting how some heavy cases in criminology could get investigated using computers, web and mobile technologies.
Practical Integral Distinguishers on SNOW 3G and KCipher-2

In this paper, we explore the security against integral attacks on well-known stream ciphers SNOW 3G and KCipher-2. SNOW 3G is the core of the 3GPP confidentiality and integrity algorithms UEA2 and UIA2, and KCipher-2 is a standard algorithm of ISO/IEC 18033-4 and CRYPTREC. Specifically, we investigate the propagation of the division property inside SNOW 3G and KCipher-2 by the Mixed-Integer Linear Programming to efficiently find an integral distinguisher. As a result, we present a 7-round integral distinguisher with 23 chosen IVs for KCipher-2. As far as we know, this is the first attack on a reduced variant of KCipher-2 by the third party. In addition, we present a 13-round integral distinguisher with 27 chosen IVs for SNOW 3G, whose time/data complexity is half of the previous best attack by Biryukov et al.
Exploring and Exploiting the Hierarchical Structure of a Scene for Scene Graph Generation

The scene graph of an image is an explicit, concise representation of the image; hence, it can be used in various applications such as visual question answering or robot vision. We propose a novel neural network model for generating scene graphs that maintain global consistency, which prevents the generation of unrealistic scene graphs; the performance in the scene graph generation task is expected to improve. Our proposed model is used to construct a hierarchical structure whose leaf nodes correspond to objects depicted in the image, and a message is passed along the estimated structure on the fly. To this end, we aggregate features of all objects into the root node of the hierarchical structure, and the global context is back-propagated to the root node to maintain all the object nodes. The experimental results on the Visual Genome dataset indicate that the proposed model outperformed the existing models in scene graph generation tasks. We further qualitatively confirmed that the hierarchical structures captured by the proposed model seemed to be valid.