Reviewerly/exHarmony huggingface dataset:
Reducing Spelling Inconsistencies in Code-Switching ASR Using Contextualized CTC Loss

Code-Switching (CS) remains a challenge for Automatic Speech Recognition (ASR), especially character-based models. With the combined choice of characters from multiple languages, the out-come from character-based models suffers from phoneme duplication, resulting in language-inconsistent spellings. We propose Contextualized Connectionist Temporal Classification (CCTC) loss to encourage spelling consistencies of a character-based non-autoregressive ASR which allows for faster inference. The model trained by CCTC loss is aware of contexts since it learns to predict both center and surrounding letters in a multi-task manner. In contrast to existing CTC-based approaches, CCTC loss does not require frame-level alignments, since the context ground truth is obtained from the model’s estimated path. Compared to the same model trained with regular CTC loss, our method consistently improved the ASR performance on both CS and monolingual corpora.
Siamese Graph Convolution Network for Face Sketch Recognition: An application using Graph structure for face photo-sketch recognition

In this paper, we present a novel Siamese graph convolution network (GCN) for face sketch recognition. To build a graph from an image, we utilize a deep learning method to detect the image edges, and then use a superpixel method to segment the edge image. Each segmented superpixel region is taken as a node, and each pair of adjacent regions forms an edge of the graph. Graphs from both a face sketch and a face photo are input into the Siamese GCN for recognition. A deep graph matching method is used to share messages between cross-modal graphs in this model. Experiments show that the GCN can obtain high performance on several face photo-sketch datasets, including seen and unseen face photo-sketch datasets. It is also shown that the model performance based on the graph structure representation of the data using the Siamese GCN is more stable than a Siamese CNN model.
Lightweight Network and Model Aggregation for Automatic Modulation Classification in Wireless Communications

This paper proposes a decentralized automatic modulation classification (DecentAMC) method using light network and model aggregation. Specifically, the lightweight network is designed by separable convolution neural network (S-CNN), in which the separable convolution layer is utilized to replace the standard convolution layer and most of the fully connected layers are cut off, the model aggregation is realized by a central device (CD) for edge device (ED) model weights aggregation and multiple EDs for ED model training. Simulation results show that the model complexity of S-CNN is decreased by about 94% while the average CCP is degraded by less than 1% when compared with CNN and that the proposed AMC method improves the training efficiency when compared with the centralized AMC (CentAMC) using S-CNN.
Chinese Coreference Resolution via Bidirectional LSTMs using Word and Token Level Representations

Coreference resolution is an important task in the field of natural language processing. Most existing methods usually utilize word-level representations, ignoring massive information from the texts. To address this issue, we investigate how to improve Chinese coreference resolution by using span-level semantic representations. Specifically, we propose a model which acquires word and character representations through pre-trained Skip-Gram embeddings and pre-trained BERT, then explicitly leverages span-level information by performing bidirectional LSTMs among above representations. Experiments on CoNLL-2012 shared task have demonstrated that the proposed model achieves 62.95% F1-score, outperforming our baseline methods.
Reflectance-Oriented Probabilistic Equalization for Image Enhancement

Despite recent advances in image enhancement, it remains difficult for existing approaches to adaptively improve the brightness and contrast for both low-light and normal-light images. To solve this problem, we propose a novel 2D histogram equalization approach. It assumes intensity occurrence and co-occurrence to be dependent on each other and derives the distribution of intensity occurrence (1D histogram) by marginalizing over the distribution of intensity co-occurrence (2D histogram). This scheme improves global contrast more effectively and reduces noise amplification. The 2D histogram is defined by incorporating the local pixel value differences in image reflectance into the density estimation to alleviate the adverse effects of dark lighting conditions. Over 500 images were used for evaluation, demonstrating the superiority of our approach over existing studies. It can sufficiently improve the brightness of low-light images while avoiding over-enhancement in normal-light images.
Checking PRNU Usability on Modern Devices

The image source identification task is mainly addressed by exploiting the unique traces of the sensor pattern noise, that ensure a negligible false alarm rate when comparing patterns extracted from different devices, even of the same brand or model. However, most recent smartphones are equipped with proprietary in-camera processing that can possibly expose unexpected correlated patterns within images belonging to different sensors.In this paper, we first highlight that wrong source attribution can happen on smartphones belonging to the same brand when images are acquired both in default and in bokeh mode. While the bokeh mode is proved to introduce a correlated pattern due to the specific in-camera post-processing, we also show that natural images also expose such issue, even when a reference from flat images is available. Furthermore, different camera models expose different correlation patterns since they are reasonably related to developers’ choices. Then, we propose a general strategy that allows the forensic practitioner to determine whether a questioned device may suffer from these correlated patterns, thus avoiding the risk of false image attribution.
Creative graphic works of first-year students of Bachelors’ degree of engineering educational program

Abstract Such a form of organization of extracurricular work as a scientific circle is considered in this article. The purpose and content of the scientific circle on descriptive geometry, engineering and computer graphics are exposed.
Research on imbalanced data set preprocessing based on deep learning

Imbalance of data sets is a widespread problem, and unbalanced data has a great impact on classification results. The traditional data preprocessing methods based on the imbalance of data sets mainly include under sampling and over sampling. Oversampling data preprocessing has the problems of over fitting and fuzzy boundary, under sampling data preprocessing method will discard the useful information of samples. In this paper, a deep learning oversampling model is proposed to solve the problems of the above methods. The model uses the data generation algorithm, the variational auto variable code algorithm, to learn the features of a few samples in the unbalanced data set, and finally combines the newly generated samples and the original data sets to form a new data set. Experimental results show that the accuracy of newly generated data is higher than that of oversampling or under sampling methods. The experimental results show that the variational self-encoding algorithm of the generative model algorithm has better preprocessing results for imbalanced data sets.
Towards Efficient Age Estimation by Embedding Potential Gender Features

Human age estimation from face image has drawn increasing research attention due to its many meaningful applications such as demographics analysis and surveillance monitoring. However, most existing methods directly extract age-specific features for age estimation and ignore age-related gender information. In this paper, we propose a simplified deep learning network for age estimation by simultaneously learning aging and potential gender features. Specifically, we first learn the potential gender information from face images. Then, we employ a two-stream convolutional neural network to simultaneously learn and concatenate the aging and gender latent appearance features. Third, we feed the multi-type features into a compact convolution network, named AgeNetwork, to further learn the age-specific features. Finally, we use a deep regression function to estimate the detailed ages. Extensive experimental results demonstrate the promising effectiveness and efficiency of our proposed method in comparison with state-of-the-arts.
Creating Emotion Recognition Algorithms Based on a Convolutional Neural Network for Sentiment Analysis

The objective of the research is the development and evaluation testing of a speed-optimized «Dual-trained Lazy CNN» neural network model for language-dependent sentiment analysis in Slavic languages in specific cultural context, as well as the software implementation of the resulting network. Convolutional neural networks are easy to train and implement. To train them, a standard error back propagation algorithm is used, and because the filter weights are evenly distributed the number of parameters in the convolutional neural network is small. From the viewpoint of computational linguistics, convolutional neural networks are a powerful tool for classification, that, however, does not have any language intuition, which significantly complicates the analysis of algorithm errors. However, it is convolutional networks that are widely used in text data analysis tasks. Neural networks always work with big data and often require a lot of processing power. Therefore, to simplify computations, it makes sense to use «lazy» NNs. A «lazy» neural network is a network, which, if it receives input data that repeats the patterns that were previously processed by it, returns a result that was obtained earlier.
Selective Kernel and Motion-emphasized Loss Based Attention-guided Network for HDR Imaging of Dynamic Scenes

Ghost-like artifact caused by ill-exposed and motion areas is one of the most challenging problems in high dynamic range (HDR) image reconstruction. When the motion range is small, previous methods based on optical flow or patch-match can suppress ghost-like artifacts by first aligning input images before merging them. However, they are not robust enough and still produce artifacts for challenging scenes where large foreground motions exist. To this end, we propose a deep network with an attention module and motion-emphasized loss function to produce ghost-free HDR images. In the attention module, we use the channel and spatial attention to guide the network to emphasize important components such as motion and saturated areas automatically. To be robust to images with different resolutions and objects with distinct scales, we adopt the selective kernel network as the basic framework for channel attention. In addition to the attention module, the motion-emphasized loss function based on the motion and ill-exposed areas mask is designed to help the network reconstruct motion areas. Experiments on the public dataset indicate that the proposed SK-AHDRNet produces ghost-free results where detail in ill-exposed areas is well recovered. The proposed method scores 43.17 with PSNR metric and 61.02 with HDR-VDP-2 metric on test which outperforms all conventional works. According to quantitative and qualitative evaluations, the proposed method can achieve state-of-the-art performance.
Reduction of Garbage Outputs and Constant Inputs in Design of Combinational Circuits Using Reversible Logic

Reversible logic is one of the latest technologies for low power design. This paper focuses on reducing the garbage output (GO) and constant input(CI) in combinational circuits. GO and CI are some of the parameters in Reversible logic. Two new reversible gates DINV ( 3 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">*</sup> 3) and TwinSJ ( 5 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">*</sup> 5) are designed. 3:8, 4:16 and 5:32 Decoders are the circuits built. Garbage output reduction of 25 to 66 percent is achieved. Also 12.5 to 75 percent reduction of constant input is achieved.
Maintaining Privacy in Medical Imaging with Federated Learning, Deep Learning, Differential Privacy, and Encrypted Computation

The availability of datasets for algorithm training and evaluation is currently hampered due to medical data privacy regulations. The lack of structured electronic medical records and stringent legal criteria has made it difficult for patient data to be collected and shared in a consolidated data lake. For training algorithms, such as convolutional neural networks, this presents difficulties, often requiring vast training examples. To avoid the compromise of patient privacy when encouraging clinical studies on broad datasets aimed at enhancing patient care, it is mandatory to incorporate technological solutions to meet data security and usage criteria at the same time. We present an outline of current and cutting-edge techniques for secure and privacy-preserving artificial intelligence, with an accentuation on medical imaging applications and potential opportunities.
Memory State Verification Based on Inductive and Deductive Reasoning

Memory allocation and deallocation are the fundamental operations of embedded operating systems, which have been extensively used in many safety critical systems. The correctness of the operations is of paramount importance because their failure could incur severe consequences. While the system is running, the memory state can easily grow to a gigantic amount, which means that it is impossible to verify the huge memory states one by one. Therefore, it is a challenge how to verify the correctness of running memory state of the system. In this article, we propose a novel memory state verification method based on inductive and deductive reasoning. First, we abstract the memory state as a list of memory blocks, which will transform in memory operations. Second, we construct the generic model based on the transition function of the memory management and summarize the invariant properties of the memory state. Third, we use the inductive method to calculate the changes between the memory states, and verify that the memory state of the system always satisfy the global properties. All the proofs are implemented in the interactive theorem prover Coq. On the basis of our proposed model, we verify the correctness of a two-level segregated fit (TLSF) algorithm through some extensions, and we also apply this method to verify the correctness of the memory state of the embedded system at runtime.
Architecture design and hardware implementation of AES encryption algorithm

With the rapid development of the information society, network information security has attracted more attention. Traditional software encryption technology is becoming more and more difficult to ensure people's information security. How to quickly and efficiently ensure people's information security has become one of the key projects studied by scholars. Under this background, this paper designs a simple advanced encryption standard AES FPGA hardware implementation. The content includes the hardware structure design of AES encryption algorithm, comprehensive wiring with Quartus II 13.0, and simulation verification on Modelsim se 10.5.
Attentional Wavelet Network for Traditional Chinese Painting Transfer

Traditional Chinese paintings pay more attention to `Gongbi' and `Xieyi' in artworks, which raises a challenging task to generate Chinese paintings from photos. `Xieyi' creates high-level conception for paintings, while `Gongbi' refers to portraying local details in paintings. This paper proposes an attentional wavelet network for photo to Chinese painting transferring. We first introduce wavelets to obtain high-level conception and local details in Chinese paintings via 2-D haar wavelet transform. Moreover, we design high-level transform stream and local enhancement stream to dispose high frequencies and low frequency respectively. Furthermore, we exploit self-attention mechanism to compatibly pick up high-level information which is used to remedy the missing details when reconstructing the Chinese painting. To advance our experiment, we set up a new dataset named P2ADataset, with diverse photos and Chinese paintings on famous mountains around China. Experimental results comparing with the state-of-the-art style transferring algorithms verify the effectiveness of the proposed method. We will release the codes and data to the public.
FunEat: An Interactive Tableware for Improving Eating Habits in Children

Good eating habits are important for growing children. However, many children have anorexia and picky eating behaviors, causing a series of problems such as malnutrition and decreased resistance to illness. Our research focuses on guiding children to develop healthy eating habits through interactive animation and gamified design. We designed an intelligent dinner plate system, composed of a gravity-sensing dinner plate and a micro projection device, which can provide guidance through interactive animation when children eat. We conducted tests of the system with children aged 5-7 with picky eating behaviors. The results showed that the system had a positive effect on improving their eating habits.
Improving ranking function and diversification in interactive recommendation systems based on deep reinforcement learning

Recently, interactive recommendation systems based on reinforcement learning have been attended by researchers due to the consider recommendation procedure as a dynamic process and update the recommendation model based on immediate user feedback, which is neglected in traditional methods. The existing works have two significant drawbacks. Firstly, inefficient ranking function to produce the Top-N recommendation list. Secondly, focusing on recommendation accuracy and inattention to other evaluation metrics such as diversity. This paper proposes a deep reinforcement learning based recommendation system by utilizing Actor-Critic architecture to model dynamic users' interaction with the recommender agent and maximize the expected longterm reward. Furthermore, we propose utilizing Spotify's ANNoy algorithm to find the most similar items to generated action by actor-network. After that, the Total Diversity Effect Ranking algorithm is used to generate the recommendation items concerning relevancy and diversity. Moreover, we apply positional encoding to compute representations of the user's interaction sequence without using sequence-aligned recurrent neural networks. Extensive experiments on the MovieLens dataset demonstrate that our proposed model is able to generate a diverse while relevance recommendation list based on the user's preferences.
Deeply-learned and spatial–temporal feature engineering for human action understanding

Accurately recognizing various human actions is a key technique in many AI applications, such as visual tracking and human–computer interaction. Aiming at solving the difficulty that the local spatial–temporal features at low-layer is limited and the descriptiveness of middle-level features is weak, we propose a novel human action understanding framework by leveraging the spatial–temporal depth features. More specifically, based on the fact that violent action regions encode highly discriminative information during human action recognition, we employ human depth clue of video images to identify the salient regions for each human. The optical flow characteristics in the regions are utilized as the energy function to measure the regional representativeness. The deep learning architecture is proposed to identify the regions of motions by the energy function. In this way, the sample points are distributed in the areas with intense movements. The collected sample points are utilized as the learned features to capture human action. Based on the learned deep feature, an SVM classifier is used to identify different human action. Comprehensive experimental results have shown that the average recognition accuracy of our human action recognition algorithm reaches 92%, and also exhibits a high robustness to complicated backgrounds.
The world within reach: An image database of reach-relevant environments

Near-scale spaces are a key component of our visual experience: Whether for work or for leisure, we spend much of our days immersed in, and acting upon, the world within reach. Here, we present the Reachspace Database, a novel stimulus set containing over 10,000 images depicting first person, motor-relevant views at an approximated reachable scale (hereafter “reachspaces”), which reflect the visual input that an agent would experience while performing a task with her hands. These images are divided into over 350 categories, based on a taxonomy we developed, which captures information relating to the identity of each reachspace, including the broader setting and room it is found in, the locus of interaction (e.g., kitchen counter, desk), and the specific action it affords. Summary analyses of the taxonomy labels in the database suggest a tight connection between activities and the spaces that support them: While a small number of rooms and interaction loci afford many diverse actions (e.g., workshops, tables), most reachspaces were relatively specialized, typically affording only one main activity (e.g., gas station pump, airplane cockpit, kitchen cutting board). Overall, this Reachspace Database represents a large sampling of reachable environments and provides a new resource to support behavioral and neural research into the visual representation of reach-relevant environments. The database is available for download on the Open Science Framework (osf.io/bfyxk/).
POLA: Online Time Series Prediction by Adaptive Learning Rates

Online prediction for streaming time series data has practical use for many real-world applications where downstream decisions depend on accurate forecasts for the future. Deployment in dynamic environments requires models to adapt quickly to changing data distributions without overfitting. We propose POLA (Predicting Online by Learning rate Adaptation) to automatically regulate the learning rate of recurrent neural network models to adapt to changing time series patterns across time. POLA meta-learns the learning rate of the stochastic gradient descent (SGD) algorithm by assimilating the prequential or interleaved-test-then-train evaluation scheme for online prediction. We evaluate POLA on two real-world datasets across three commonly-used recurrent neural network models. POLA demonstrates overall comparable or better predictive performance over other online prediction methods.
IBN-STR: A Robust Text Recognizer for Irregular Text in Natural Scenes

Although text recognition methods based on deep neural networks have promising performance, there are still challenges due to the variety of text styles, perspective distortion, text with large curvature, and so on. To obtain a robust text recognizer, we have improved the performance from two aspects: data aspect and feature representation aspect. In terms of data, we transform the input images into S-shape distorted images in order to increase the diversity of training data. Besides, we explore the effects of different training data. In terms of feature representation, the combination of instance normalization and batch normalization improves the model's capacity and generalization ability. This paper proposes a robust scene text recognizer IBN-STR, which is an attention-based model. Through extensive experiments, the model analysis and comparison have been carried out from the aspects of data and feature representation, and the effectiveness of IBN-STR on both regular and irregular text instances has been verified. Furthermore, IBN-STR is an end-to-end recognition system that can achieve state-of-the-art performance.