Reviewerly/exHarmony huggingface dataset:
Deciphering Ancient Chinese Oracle Bone Inscriptions Using Case-Based Reasoning

Ancient oracle bone inscriptions (OBIs) are important Chinese cultural artefacts, which are difficult and time-consuming to decipher even by the most expert paleographers and, as a result, a large proportion of excavated OBIs remain unidentified. In practice, OBIs are deciphered by translating between different writing systems; Chinese writing systems have evolved over time and ancient OBIs can be deciphered by translating their inscriptions to a known inscription in an adjacent writing system, but this is a complex and time-consuming process. In this paper we propose a novel case-based system, to support this task, allowing a paleographer to present an unknown inscription (image) as a query, to receive a set of similar images from an adjacent writing system with associated scholarly information, and so help guide the deciphering of the query. One important contribution of this work involves the use of an auto-encoder to learn suitable image representations to capture the relationship between two adjacent writing systems. We demonstrate the effectiveness of this approach using a novel, purpose-built case base, and discuss its use in a paleographic setting.
LGACN: A Light Graph Adaptive Convolution Network for Collaborative Filtering

To relieve information flood on the web, recommender system has been widely used to retrieve personalized information. In recommender system, Graph Convolutional Network (GCN) has become a new frontier technology of collaborative filtering. However, existing methods usually assume that neighbor nodes have only positive effects on the target node. A few methods analyze the design of traditional GCNs and eliminate some invalid operations. However, they have not considered the possible negative effects of neighbors to adapt collaborative filtering. Thus, we argue that it is crucial to take the positive and negative effects of neighbors into consideration for collaborative filtering.In this paper, we aim to alter the neighbor aggregation method and layer combination mechanism of GCN to make it more applicable for recommendation. Inspired by LightGCN, we propose a new model named LGACN (Light Graph Adaptive Convolution Network), including the most important component in GCN - neighborhood aggregation and layer combination - for collaborative filtering and alter them to fit recommendations. Specifically, LGACN learns user and item embeddings by propagating their positive and negative information on the user-item interaction graph by an adaptive attention-based method and uses the self-attention mechanism to combine the embeddings learned at each layer as the final embedding. Such a neat model is not only easy to implement but also interpretable, outperforming strong recommender baselines. Our model achieves about \(15\%\) relative improvement on Amazon-book and \(5\%\) relative improvement on Yelp2018 compared with LightGCN.
Dynamic Mixture of Experts Models for Online Prediction

A mixture of experts models the conditional density of a response variable using a mixture of regression models with covariate-dependent mixture weights. We extend the finite mixture of experts model by allowing the parameters in both the mixture components and the weights to evolve in time by following random walk processes. Inference for time-varying parameters in richly parameterized mixture of experts models is challenging. We propose a sequential Monte Carlo algorithm for online inference based on a tailored proposal distribution built on ideas from linear Bayes methods and the EM algorithm. The method gives a unified treatment for mixtures with time-varying parameters, including the special case of static parameters. We assess the properties of the method on simulated data and on industrial data where the aim is to predict software faults in a continuously upgraded large-scale software project.
Development of the Russian education system: regional practices

The author considers the regional aspects of the development of the national education system as a factor and resource of the economic and socio-cultural development of the Russian Federation. The main trends of sustainable development of Russian regions and regional educational systems are identified.
Research on Image Caption Based on Multiple Word Embedding Representations

Word embedding representation has been a research hot spot in the field of computer vision and natural language processing. The semantic quality of representation severely affects the effects of machine translation and image caption. The key factors affecting the quality of word embedding representation are capture of contextual semantics and solution of the problem of polysemy. In this paper, the principles of the current mainstream word embedding representation methods were explored, such as sparse representation (One-Hot), static representation (Word2Vec, GloVe), dynamic representation (ELMo, BERT) representation and based on position information (NN.Embedding-Position Embedding). The impact of those word embedding representations on image caption task was researched attentively. And those representations methods were compared experimentally using the MSCOCO data based on the same image caption model. The pros and cons of these methods were judged according to the accuracy and richness of the extracted label data. Experimental results show that the BERT model representation with dynamic bidirectional semantics has the best performance in image caption tasks, but it requires longer training time. The NN.Embedding representation has the second best image semantic description effect, and its training speed is the fastest.
Extracting Temporal Correlations Using Hierarchical Spatio-Temporal Feature Maps

Self-organizing maps (SOM) are popularly used for applications in learning features, vector quantization and recalling spatial input patterns. The adaptation rule in SOM is based on the Euclidean distance between the input vector and the neuronal weight vector along with a neighborhood function that brings in topological arrangement of the neurons. It is capable of learning the spatial correlations among the data but fails to capture temporal correlations present in a sequence of inputs. We formulate a potential function based on a spatio-temporal metric and create hierarchical vector quantization feature maps by embedding memory structures similar to long short-term memories (LSTM) across the feature maps to learn the spatiotemporal correlations in the data across clusters. We derive the learning rule from first principles and estimate the computational complexity. Simulation results over a synthetic data set and a real world climate data set show that our algorithm is capable of learning the spatio-temporal mappings.
Anti-counterfeiting image generation algorithm based on microstructure dots

With the popularity of high-precision printing equipment, leading to the widespread spread of pirated products. Because of some disadvantages in traditional anti-counterfeiting printing, a half-tone anti-counterfeiting image generation algorithm based on microstructure dots is proposed. First, the word library automatically generates the character word library containing the grayscale and morphological differences, and then matches and replaces the image according to the similarity of the image, so as to realize the generation of the anti-counterfeiting image. Experimental results show that the anti-counterfeiting image based on microstructure dot generated by this algorithm is visually small from the original image, and after the anti-counterfeiting image is enlarged, the embedded micro characters are clearly visible, and the algorithm has broad application prospects in packaging anti-counterfeiting.
Self-supervised Multi-view Clustering for Unsupervised Image Segmentation

At present, the main idea of CNN-based unsupervised image segmentation is clustering a single image in the framework of CNNs. However, the single image clustering is very difficult to obtain enough supervision information for network learning. For solving this problem, we propose a Self-supervised Multi-view Clustering (SMC) structure for unsupervised image segmentation to mine additional supervised information. Based on the observation that the predicted pixel-level labels and the input images have the same spatial features, the multi-view images acquired by data augmentation are clustered to obtain the multi-view results and the proposed SMC uses the differences among these results to learn self-supervised information. Moreover, a Hybrid Self-supervised (HS) loss is proposed to make full use of the self-supervised information for further improving the prediction accuracy and the convergence speed. Extensive experiments in BSD500 and PASCAL VOC 2012 datasets demonstrate the superiority of our proposed approach.
Intelligent Error Correction of Oral English Pronunciation Based on Speech Recognition Technology

According to the basic structure model of speech recognition system, this paper introduces the basic technology of digital signal processing and pattern recognition involved in preprocessing, feature parameter extraction and model matching. Using feature parameter extraction, forced association and dynamic time warping, speech scoring based on feature comparison and HMM is studied respectively. At the same time, Viterbi alignment algorithm, process pruning and model storage optimization are also improved, which makes the speed of speech recognition in the platform faster and it provides users better response. Finally, the performance and accuracy of the system are tested compared with the scoring algorithm based on continuous hidden Markov model. The experiment results show that the recognition speed is significantly improved. The model can basically reflect the learners' English pronunciation level, correct most of the learners' errors, and it is helpful to the training of English pronunciation level.
A Random Selection Based Substitution-box Structure Dataset for Cryptology Applications

The cryptology science has gradually gained importance with our digitalized lives. Ensuring the security of data transmitted, processed and stored across digital channels is a major challenge. One of the frequently used components in cryptographic algorithms to ensure security is substitution-box structures. Random selection-based substitution-box structures have become increasingly important lately, especially because of their advantages to prevent side channel attacks. However, the low nonlinearity value of these designs is a problem. In this study, a dataset consisting of twenty different substitution-box structures have been publicly presented to the researchers. The fact that the proposed dataset has high nonlinearity values will allow it to be used in many practical applications in the future studies. The proposed dataset provides a contribution to the literature as it can be used both as an input dataset for the new post-processing algorithm and as a countermeasure to prevent the success of side-channel analyzes.
Attention-Based Phonetic Convolutional Recurrent Neural Networks for Language Identification

Language identification is the task of identifying the language of the spoken utterance. Deep neural models such as LSTM-RNN with attention mechanism shown great potential in language identification. The language cues like phonemes and their co-occurrences are an important component while distinguishing the languages. The acoustic feature-based systems do not utilize phonetic information. So the phonetic feature-based LSTM-RNN models have shown improvement over the raw-acoustic features. These methods require a large amount of transcribed speech data to train the phoneme discriminator. Obtaining transcribed speech data for low resource Indian languages is a difficult task. To alleviate this issue, we investigate the usage of pre-trained rich resource phonetic discriminators for low resource target languages to extract the phonetic features. We then trained an attention CRNN based end-to-end utterance level language identification (LID) system with these discriminative phonetic features. We used open-source LibriSpeech English data to train the phoneme discriminator with sequence discriminate objective lattice-free maximum mutual information (LF-MMI). We achieved overall 20% absolute improvements over the baseline acoustic features CRNN model. We also investigate the significance of the duration in LID.
Assessing Bayesian Semi‐Parametric Log‐Linear Models: An Application to Disclosure Risk Estimation

Summary We propose a method for identifying models with good predictive performance in the family of Bayesian log‐linear mixed models with Dirichlet process random effects for count data. Their wide applicability makes the assessment of model performance crucial in many fields, including disclosure risk estimation, which is the focus of the present work. Rather than assessing models on the whole contingency table, we target the specific objective of the analysis and propose a two‐stage model selection procedure aimed at limiting a form of bias arising in the process of model selection. Our proposal combines two different criteria: at the first stage, a path in the model search space is identified through a strongly penalized log‐likelihood; at the second, a small number of semi‐parametric models is evaluated through a context‐dependent score‐based information criterion. Tested on a variety of contingency tables, our method proves to be able to identify models with good predictive performance in a few steps, even in the presence of large tables with many sampling and structural zeros. We carefully discuss the proposed method in the context of the literature on model assessment and contextualize the illustrative application in the recent debate on statistical disclosure limitation. Finally, we provide examples of further applications in different research areas.
Proceedings 37th International Conference on Logic Programming (Technical Communications)

Among all procedures, keyhole neurosurgery is one of the most hazardous ones, due to the complexity of the brain environment and the high density of vital structures.Respect of the kinematic constraints of the probe selected to perform the procedure and of the preferences dictated by the surgeon's experience are essential to find the safest path to the surgical target.This work presents and optimisation and classification strategy for neurosurgical interventions.The framework relies on Answer Set Programming to translate the requirements and the expert's knowledge into the objectives of the optimisation procedure.The semantics of Answer Set Programming grants extensive flexibility, as it allows to easily change the requirements to optimise and their priority based on the specific needs of the clinical case.* We thank Eden2020 project consortium partners for precious advice during the project activities.We thank M. Riva for providing the dataset and the manual trajectories for the CED environment simulation.
Carpet Tiles

Moving to another country to study can be daunting and is connected to practical and emotional challenges. Here we present the results of an exploratory study on how smart textiles could be used to support international students in student housing to feel at home. We undertook seven situated interviews. Based on three key findings - the importance of social connections, cultural elements (food and carpets) and the limited duration of stay – the concept of a modular carpet with different interactive elements was developed. The carpet offers a social space, where students can spend time with friends. Its modularity allows personalization to the available space and room layout. With a proof of concept prototype we aim to open up a design space for tangible (textile) interventions.
Research on LFM Combined Signal Ranging Method

This paper analyzes the influence of PCW signal, LFM signal and positive and negative LFM signal in the application of speed measurement and ranging in engineering. The influence of target speed on LFM signal ranging is analyzed. Due to the coupling between time delay and frequency, single LFM signal cannot eliminate the Doppler time delay in ranging, which leads to ranging error. After matched filtering of the combined LFM signal based on different frequency bands and pulse widths, the ranging error can be eliminated. In this paper, a method of ranging using combined FM signal is proposed, and the simulation analysis of this method is carried out, which can better estimate the distance of moving target, and has reference value for engineering use.
Research on a new topic crawler based on HITS algorithm and semantic fusion

The topic crawler needs to filter for Web prior to crawl Web page, and the crawl strategy of traditional topic crawler is to evaluate the content or evaluate the link relation, for the former topic crawler usually lack of overall importance, it is difficult to reflect the whole situation of the Web, and the theme crawler implemented by the latter will crawl to a large number of topic irrelevant pages in the later stage of crawling. In this paper, a new topic crawler algorithm is designed. The SVSM model is built to calculate the topic relevance of web pages, and HITS algorithm is used to calculate the comprehensive score of web pages. The topic crawler crawls the web page in the order of ranking from highest to lowest. In this paper, the new crawler is compared with two traditional crawlers under the same experimental environment, and the accuracy of the new topic crawler is 23% higher than that of the traditional topic crawler.
Automatic CNN Compression Based on Hyper-parameter Learning

Sparse regularization method, such as L <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</inf> or L <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2,1</inf> regularization, is the most popular method which can induce sparse models. However, it introduces new hyper-parameters, which not only affects the degree of model sparsity, but also determines whether the model can be effectively trained. So how to automatically select hyper-parameters becomes an important and open problem for regularization-based model compression method. In general, we propose an automatic CNN model compression framework with cross-validation gradient which can automatically adjust the hyper-parameters and combine model parameter learning with hyper-parameter learning together. Specifically, in order to solve the hyper-parameter gradient (cross-validation gradient), we introduce auxiliary variables to transform the non-differentiable problem of L <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</inf> norm to a derivable form and obtain the derivative of model parameters with respect to hyper-parameters. Then the cross-validation gradient can be finally solved by the chain rule. Secondly, unlike common cross-validation methods, we propose a alternative learning methods for parameter learning with hyper-parameter learning. It is an unified framework which do not need to training from scratch after each hyper-parameters update which save a lot of time compared with manual parameter adjustment. Thirdly, we do not need to specify the sparsity rate which is also take much time for pruning methods. Classical CNN structures such as VGG, ResNet and DensNet are tested on CIFAR-10 and CIFAR-100 datasets to prove the effectiveness of our algorithm. Our code is avaliable at: https://github.com//tnn2018/AHLC.
Human activity recognition for static and dynamic activity using convolutional neural network

Evaluated activity as a detail of the human physical movement has become a leading subject for researchers. Activity recognition application is utilized in several areas, such as living, health, game, medical, rehabilitation, and other smart home system applications. An accelerometer was popular sensors to recognize the activity, as well as a gyroscope, which can be embedded in a smartphone. Signal was generated from the accelerometer as a time-series data is an actual approach like a human actifvity pattern. Motion data have acquired in 30 volunteers. Dynamic actives (walking, walking upstairs, walking downstairs) as DA and static actives (laying, standing, sitting) as SA were collected from volunteers. SA and DA it's a challenging problem with the different signal patterns, SA signals coincide between activities but with a clear threshold, otherwise the DA signal is clearly distributed but with an adjacent upper threshold. The proposed network structure achieves a significant performance with the best overall accuracy of 97% . The result indicated the ability of the model for human activity recognition purposes.
Loss-based active learning via double-branch deep network

Due to the limitation of data annotation and the ability of dealing with label-efficient problems, active learning has received lots of research interest in recent years. Most of the existing approaches focus on designing a different selection strategy to achieve better performance for special tasks; however, the performance of the strategy still needs to be improved. In this work, we focus on improving the performance of active learning and propose a loss-based strategy that learns to predict target losses of unlabeled inputs to select the most uncertain samples, which is designed to learn a better selection strategy based on a double-branch deep network. Experimental results on two visual recognition tasks show that our approach achieves the state-of-the-art performance compared with previous methods. Moreover, our approach is also robust to different network architectures, biased initial labels, noisy oracles, or sampling budget sizes, and the complexity is also competitive, which demonstrates the effectiveness and efficiency of our proposed approach.
E-commerce Search Engine Marketing Mechanism Analysis and Optimization

This paper starts from the concept of search engine optimization and summarizes its working principle. At the same time, a vertical search engine based on Nutch is designed and implemented. MapReduce is introduced into the link weight factor and time decay factor to improve the LinkRank sorting algorithm, etc. With targeted analysis and optimization of the main category page, it enables more search engine to be friendly. Then, combined with the Nutch open source framework and the proposed e-commerce topic data collection scheme, a prototype framework of e-commerce based topic search engine is designed, and the search ranking is optimized by combining the Nutch scoring mechanism in the searcher. Finally, through the optimization of website structure mode, reasonable setting of keywords, external chain construction and other methods, the popularity of the website and the ranking of retrieval results are improved. The empirical test shows that such scheme achieves the goal that the target keywords rank first in search engine search results.
Can You See It?

Today's smartphones and wearable devices come equipped with an array of inertial sensors, along with IMU-based Human Activity Recognition models to monitor everyday activities. However, such models rely on large amounts of annotated training data, which require considerable time and effort for collection. One has to recruit human subjects, define clear protocols for the subjects to follow, and manually annotate the collected data, along with the administrative work that goes into organizing such a recording.