Reviewerly/exHarmony huggingface dataset:
A novel syntax-aware automatic graphics code generation with attention-based deep neural network

Recent advances in deep learning have made it possible to automatically translate graphical user interface (GUI) into code by an encoder-decoder framework. This framework generally uses deep convolutional neural network (CNN) to extract image features, which are then translated into hundreds of code tokens by a code generator based on a recurrent neural network (RNN). However, there are two challenges in the implementation of this framework: one is how to make full use of the information contained in the GUI and domain specified language (DSL) code, the other is how to make generated DSL code conform to syntax rules. To fully leverage the information in GUI and DSL code, we first propose a model named HGui2Code that integrates visual attention-enabled GUI features (extracted by CNN) with DSL attention-enabled semantic features (extracted by LSTM). Besides, we propose SGui2Code, a novel model that makes use of a ON-LSTM network to generate DSL code that is correct in syntax. HGui2code pays more attention to semantic information, while SGui2code focuses on grammar rules. Extensive experimental results show that our models outperform state-of-the-art methods on the web dataset, yielding 5.5% higher accuracy with the HGui2Code model and 1.5% using the SGui2Code model respectively. Although our models do not have huge boost on IOS and Android dataset, DSL code generated by our models are very close to the layout of components in corresponding GUI.
Structural Analogy from a Single Image Pair

Abstract The task of unsupervised image‐to‐image translation has seen substantial advancements in recent years through the use of deep neural networks. Typically, the proposed solutions learn the characterizing distribution of two large, unpaired collections of images, and are able to alter the appearance of a given image, while keeping its geometry intact. In this paper, we explore the capabilities of neural networks to understand image structure given only a single pair of images, and . We seek to generate images that are structurally aligned : that is, to generate an image that keeps the appearance and style of , but has a structural arrangement that corresponds to . The key idea is to map between image patches at different scales. This enables controlling the granularity at which analogies are produced, which determines the conceptual distinction between style and content. In addition to structural alignment , our method can be used to generate high quality imagery in other conditional generation tasks utilizing images and only: guided image synthesis, style and texture transfer, text translation as well as video translation. Our code and additional results are available in https://github.com/rmokady/structural-analogy/
Blockchain-based Secure Data Storage for Door Lock System

A smart home uses information technology to manage the devices inside the home to provide convenience for its owners. The most widely used technology for smart home systems is IoT. Nevertheless, IoT has challenges related to data security, moreover for the large scale and distributed IoT network properties. A thing that related to data security is data storage security. One of the data that needs to be secure is the door lock data access. This data must not be vulnerable to counterfeiting and hacking, because a door is directly related to the safety of the homeowner. Door lock access data can be used to find out who and when someone is in or out of the house, hence we can increase the home security if something suspicious happens. However, we must guarantee the integrity of the door lock access data. A method that can be used to ensure data integrity is making a blockchain-based system as data storage because the properties of blockchain are immutable and nonrepudiation. This study uses the Ethereum blockchain platform to store door lock access data and smart contracts to manage the policies. In addition, this study conducts a test to analyze the security level of the proposed system by using an avalanche effect method. The test results show the avalanche effect index for the proposed system is about 96% on average which means the proposed system can be categorized as a secure system.
A photoelastic and numeric study of the stress field in the vicinity of two interacting cracks: Stress intensity factors, T-stresses and higher order terms

The multi-parameter description of the stress field in the neighborhood of the crack tips of two interacting cracks based on the photoelastic study and finite element analyses is given. Digital photoelasticity method is used for obtaining the isochromatic and isoclinic patterns in the plate with two collinear horizontal and two inclined cracks in anisotropic linear elastic material. Stress intensity factors, T-stresses and coefficients of the higher order terms in the multi-parameter Williams series expansion are experimentally determined. The finite element analysis for the cracked plates with the same configurations has been performed. Stress intensity factors, T-stress and coefficients of nine-term asymptotic expansion for the stress field are numerically obtained and are compared with the experimental results. The comparison shows good agreement of experimental and numerical estimations of these fracture mechanics parameters. Very good agreement is shown to exist between the digital photoelasticity method and finite element results confirming the effectiveness of the photoelasticity technique in obtaining the coefficients of higher order terms of the Williams series expansion from the experimental stress field around the crack tip.
IPTV Channel Zapping Recommendation With Attention Mechanism

Internet Protocol TV (IPTV) normally has the advantage of providing far more TV channels than the traditional TV services, while as the other side of the coin it has the problem of information overload. Users of IPTV usually have difficulties finding channels matching their interests. In this paper, using a large IPTV dataset, we analyze channel zapping behaviors of IPTV users and discover various patterns that can be used to generate more accurate channel zapping recommendations. Based on user behavior analysis, we develop several base and fusion recommender systems that generate in real-time a short list of channels for users to consider whenever they want to switch channels. A deep neural network model that consists of a “Recommender System Attention (RS Attention)” module and a “Channel Attention” module capturing the static and dynamic user switching behaviors is also developed to further improve the recommendation accuracy. Evaluation on the IPTV dataset demonstrates that our fusion recommender can achieve 41% hit ratio with only three candidate channels, and our attention neural network model further pushes it up to 45%. Our recommender systems only take as input user channel zapping sequences, and can be easily adopted by IPTV systems with low data and computation overheads.
Subclass Heterogeneity Aware Loss for Cross-Spectral Cross-Resolution Face Recognition

One of the most challenging scenarios of face recognition is matching images in presence of multiple covariates such as cross-spectrum and cross-resolution. In this paper, we propose a Subclass Heterogeneity Aware Loss (SHEAL) to train a deep convolutional neural network model such that it produces embeddings suitable for heterogeneous face recognition, both single and multiple heterogeneities. The performance of the proposed SHEAL function is evaluated on four databases in terms of the recognition performance as well as convergence in time and epochs. We observe that SHEAL not only yields state-of-the-art results for the most challenging case of Cross-Spectral Cross-Resolution face recognition, it also achieves excellent performance on homogeneous face recognition.
Multivariate Outlier Detection in Applied Data Analysis: Global, Local, Compositional and Cellwise Outliers

Abstract Outliers are encountered in all practical situations of data analysis, regardless of the discipline of application. However, the term outlier is not uniformly defined across all these fields since the differentiation between regular and irregular behaviour is naturally embedded in the subject area under consideration. Generalized approaches for outlier identification have to be modified to allow the diligent search for potential outliers. Therefore, an overview of different techniques for multivariate outlier detection is presented within the scope of selected kinds of data frequently found in the field of geosciences. In particular, three common types of data in geological studies are explored: spatial, compositional and flat data. All of these formats motivate new outlier concepts, such as local outlyingness, where the spatial information of the data is used to define a neighbourhood structure. Another type are compositional data, which nicely illustrate the fact that some kinds of data require not only adaptations to standard outlier approaches, but also transformations of the data itself before conducting the outlier search. Finally, the very recently developed concept of cellwise outlyingness, typically used for high-dimensional data, allows one to identify atypical cells in a data matrix. In practice, the different data formats can be mixed, and it is demonstrated in various examples how to proceed in such situations.
A Robust Image Encryption Method Using Chaotic Skew-Tent Map

Encryption is one of the most frequently used tools in data communications to prevent unwanted access to the data. In the field of image encryption, chaos-based encryption methods have become very popular in the recent years. Chaos-based methods provide a good security mechanism in image communication. In this chapter, chaotic skew-tent map is adapted to encode an image. Seventy-two bit external key is considered (besides the initial parameters of the chaotic system) initially, and after some processing operations, 64 bit internal key is obtained. Using this key, every pixel is processed. The internal key is transformed using some basic operations to enhance the security. The decryption method is very simple so that authentic users can retrieve the information very fast. Every pixel is encrypted using some basic mathematical operations. The values of various test parameters show the power and efficiency of the proposed algorithm, which can be used as a safeguard for sensitive image data and a secure method of image transmission.
AICF: Attention-based item collaborative filtering

Item-to-item collaborative filtering (short for ICF) has been widely used in ecommerce websites due to his interpretability and simplicity in real-time personalized recommendation. The focus of ICF is to calculate the similarity between items. With the rapid development of machine learning in recent years, it takes similarity model instead of cosine similarity and Pearson coefficient to calculate the similarity between items in recommendation. However, the existing similarity models can not sufficient to express the preferences of users for different items. In this work, we propose a novel attention-based item collaborative filtering model(AICF) which adopts three different attention mechanisms to estimate the weights of historical items that users have interacted with. Compared with the state-of-the-art recommendation models, the AICF model with simple attention mechanism Self-Attention can better estimate the weight of historical items on non-sparse data sets. Due to depth models can model complex connection between items, our model with the more complex Transformer achieves superior recommendation performance on sparse data. Extensive experiments on ML-1M and Pinterest-20 show that the proposed model greatly outperforms other novel models in recommendation accuracy and provides users with personalized recommendation list more in line with their interests.
Concentration and distribution of lithium in catchment sediments of China: Conclusions from the China Geochemical Baselines project

With the ever-increasing demand for lithium (Li), understanding its concentration and distribution in the pedosphere is essential for alleviating the Li shortage in China. The China Geochemical Baselines (CGB) project, which commenced in 2008, provided comparable global-scale geochemical Li data. A total of 3394 top (0–25 cm) and 3394 deep (below 100 cm) catchment sediment samples were collected. The range of Li concentrations in the top and deep samples were 5.37–400 mg·kg−1 with a median value of 30.0 mg·kg−1, and 5.27–400 mg·kg−1 with a median value of 28.6 mg·kg−1, respectively. The Li concentrations in the top and deep catchment sediments of China showed large variations across regions but only mild variability across profiles. Parent rock, especially granite, predominantly governed the Li distribution in the catchment sediments of China. Landscapes, mineralization and weathering played regional-scale roles. Macroscopically, the low background area (<P25) was mainly distributed in the northern margin of the North China craton, and Hainan Island, and the high background area (>P75) was mainly distributed in the Altay, South China, Sanjiang and Himalayan orogenic belts, and the Yangtze craton. Based on a cumulative frequency of the 85th percentile, a total of 16 geochemical anomalies were identified, and eight important prospective Li resources were selected where many world-renowned Li deposits were formed.
Whence to Learn? Transferring Knowledge in Configurable Systems Using BEETLE

As software systems grow in complexity and the space of possible configurations increases exponentially, finding the near-optimal configuration of a software system becomes challenging. Recent approaches address this challenge by learning performance models based on a sample set of configurations. However, collecting enough sample configurations can be very expensive since each such sample requires configuring, compiling, and executing the entire system using a complex test suite. When learning on new data is too expensive, it is possible to use <i>Transfer Learning</i> to “transfer” old lessons to the new context. Traditional transfer learning has a number of challenges, specifically, (a) learning from excessive data takes excessive time, and (b) the performance of the models built via transfer can deteriorate as a result of learning from a poor source. To resolve these problems, we propose a novel transfer learning framework called BEETLE, which is a “bellwether”-based transfer learner that focuses on identifying and learning from the most relevant source from amongst the old data. This paper evaluates BEETLE with 57 different software configuration problems based on five software systems (a video encoder, an SAT solver, a SQL database, a high-performance C-compiler, and a streaming data analytics tool). In each of these cases, BEETLE found configurations that are as good as or better than those found by other state-of-the-art transfer learners while requiring only a fraction ( <inline-formula><tex-math notation="LaTeX">$\frac{1}{7}$</tex-math></inline-formula> th) of the measurements needed by those other methods. Based on these results, we say that BEETLE is a new high-water mark in optimally configuring software.
A Survey on Named Entity Recognition

Natural language processing is an important research direction and research hotspot in the field of artificial intelligence. Named entity recognition is one of the key tasks, which is to identify entities with specific meanings in the text, such as names of people, places, institutions, proper nouns, etc. Traditional named entity recognition methods are mainly implemented based on rules, dictionaries, and statistical learning. In recent years, with the rapid expansion of Internet text data scale and the rapid development of deep learning technology, a large number of deep neural network-based methods have emerged, which have greatly improved the accuracy of recognition. This paper attempts to summarize the traditional methods and the latest research progress in the field of named entity identification, and summarize and analyse its main models, algorithms and applications. Finally, the future development trend of named entity recognition is discussed.
A decision support system for mammography reports interpretation

Mammography plays a key role in the diagnosis of breast cancer; however, decision-making based on mammography reports is still challenging. This paper aims to addresses the challenges regarding decision-making based on mammography reports and propose a Clinical Decision Support System (CDSS) using data mining methods to help clinicians to interpret mammography reports.For this purpose, 2441 mammography reports were collected from Imam Khomeini Hospital from March 21, 2018, to March 20, 2019. In the first step, these mammography reports are analyzed and program code is developed to transform the reports into a dataset. Then, the weight of every feature of the dataset is calculated. Random Forest, Naïve Bayes, K-nearest neighbor (K-NN), Deep Learning classifiers are applied to the dataset to build a model capable of predicting the need for referral to biopsy. Afterward, the models are evaluated using cross-validation with measuring Area Under Curve (AUC), accuracy, sensitivity, specificity indices.The mammography type (diagnostic or screening), mass and calcification features mentioned in the reports are the most important features for decision-making. Results reveal that the K-NN model is the most accurate and specific classifier with the accuracy and specificity values of 84.06% and 84.72% respectively. The Random Forest classifier has the best sensitivity and AUC with the sensitivity and AUC values of 87.74% and 0.905 respectively.Accordingly, data mining approaches are proved to be a helpful tool to make the final decision as to whether patients should be referred to biopsy or not based on mammography reports. The developed CDSS may also be helpful especially for less experienced radiologists.
TE141K: Artistic Text Benchmark for Text Effect Transfer

Text effects are combinations of visual elements such as outlines, colors and textures of text, which can dramatically improve its artistry. Although text effects are extensively utilized in the design industry, they are usually created by human experts due to their extreme complexity; this is laborious and not practical for normal users. In recent years, some efforts have been made toward automatic text effect transfer; however, the lack of data limits the capabilities of transfer models. To address this problem, we introduce a new text effects dataset, TE141K1 1.Project page: https://daooshee.github.io/TE141K/. with 141,081 text effect/glyph pairs in total. Our dataset consists of 152 professionally designed text effects rendered on glyphs, including English letters, Chinese characters, and Arabic numerals. To the best of our knowledge, this is the largest dataset for text effect transfer to date. Based on this dataset, we propose a baseline approach called text effect transfer GAN (TET-GAN), which supports the transfer of all 152 styles in one model and can efficiently extend to new styles. Finally, we conduct a comprehensive comparison in which 14 style transfer models are benchmarked. Experimental results demonstrate the superiority of TET-GAN both qualitatively and quantitatively and indicate that our dataset is effective and challenging.
AI IN PRODUCTION: VIDEO ANALYSIS AND MACHINE LEARNING FOR EXPANDED LIVE EVENTS COVERAGE

As with many industries, TV and video production is likely to be transformed by artificial intelligence (AI) and machine learning (ML), with software and algorithms assisting production tasks that, conventionally, could only be carried out by people. Expanded coverage of a diverse range of live events is particularly constrained by the relative scarcity of skilled people, and it is a strong use case for AI-based automation. This article describes the recent research conducted by the British Broadcasting Corporation (BBC) on the potential production benefits of AI algorithms, using visual analysis and other techniques. Rigging small, static ultrahigh-definition (UHD) cameras, we have enabled a one-person crew to crop UHD footage in multiple ways and cut between the resulting shots, effectively creating multicamera HD coverage of events that cannot accommodate a camera crew. By working with programmakers to develop simple deterministic rules and, increasingly, training systems using advanced video analysis, we are developing a system of algorithms to automatically frame, sequence, and select shots, and construct acceptable multicamera coverage of previously untelevised types of events.
Cluster-Based Hungarian Approach to Task Allocation for Unmanned Aerial Vehicles

In the context of autonomy for Unmanned Aerial Vehicles (UAVs), task allocation plays a significant role for collaborative UAVs to make decisions in dynamic environments. This paper presents a novel Hungarian-based approach to challenging multi-task allocation (MTA) problems, where the number of UAVs is smaller than the number of tasks. We developed the Cluster-Based Hungarian Algorithm (CBHA), in which (1) tasks are grouped such that the number of UAVs is the same as the number of task groups, (2) the original Hungarian algorithm is applied, and (3) an algorithm for travel-salesman-problem (TSP) is applied to perform path planning for individual UAVs. The performance of the proposed CBHA was compared with the Consensus-Based Bundle Algorithm (CBBA) in Monti Carlo simulations, where different numbers of UAVs and tasks were adopted in the scenario of a team of unmanned aerial vehicles traveling through a number of targeting locations. The simulation result shows that the CBHA outperforms CBBA in all cases.
End-to-End Code-Switching TTS with Cross-Lingual Language Model

Code-switching text-to-speech (TTS) aims to enable a system to speak two languages with a single voice and in the same utterance. In this paper, we propose to incorporate cross-lingual word embedding into an end-to-end TTS system, to improve the voice rendering. The cross-lingual word embedding, generated from a pre-trained cross-lingual language model, is able to encode words of two languages in the same embedding space, therefore, allows words across languages to share each other's contextual information, which is useful for the voice rendering of code-switching content. To investigate the effectiveness of this idea, we conduct studies on two multi-speaker monolingual corpora, namely, THCHS30 Mandarin and LibriTTS English database. The evaluation results show that our proposed framework outperforms the baseline systems when presented with code-switching text input, and achieves state-of-the-art performance.
Incremental Semi-Supervised Learning for Multi-Genre Speech Recognition

In this work, we explore a data scheduling strategy for semisupervised learning (SSL) for acoustic modeling in automatic speech recognition.The conventional approach uses a seed model trained with supervised data to automatically recognize the entire set of unlabeled (auxiliary) data to generate new labels for subsequent acoustic model training.In this paper, we propose an approach in which the unlabelled set is divided into multiple equal-sized subsets.These subsets are processed in an incremental fashion: for each iteration a new subset is added to the data used for SSL, starting from only one subset in the first iteration.The acoustic model from the previous iteration becomes the seed model for the next one.This scheduling strategy is compared to the approach employing all unlabeled data in one-shot for training.Experiments using lattice-free maximum mutual information based acoustic model training on Fisher English gives 80% word error recovery rate.On the multi-genre evaluation sets on Lithuanian and Bulgarian relative improvements of up to 17.2% in word error rate are observed.