Reviewerly/exHarmony huggingface dataset:
DBR: A Depth-Branch-Resorting Algorithm for Locality Exploration in Graph Processing

Unstructured and irregular graph data causes strong randomness and poor locality of data access in graph processing. In order to alleviate this problem, this paper proposes a Depth-Branch-Resorting (DBR) Algorithm for locality exploration in graph processing, and the corresponding graph data compression format DBR_DCSR. The DBR algorithm and DBR_DCSR format are tested and verified on the framework GraphBIG. The results show that in terms of execution time, the DBR algorithm and DBR_DCSR format reduce GraphBIG execution time by 55.6% compared with the original GraphBIG framework, and 71.7%, 11.46% less than the frameworks of Ligra, Gemini respectively. While compared with the original GraphBIG framework, the optimized GraphBIG framework in DBR_DCSR format has a maximum reduction of 87.9 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">%</sup> in data movement and 52.3% in data computation. Compared to the Ligra, Genimi, the amount of data movement are reduced by 33.5% and 49.7 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">%</sup> , the amount of data calculation reduced by 54.3% and 43.9% respectively.
Mixed Context Techniques in the Adaptive Arithmetic Coding Process for DC Term and Lossless Image Encoding

Context modeling plays a very important role in data compression. In the arithmetic coding process, one usually classifies the data to be encoded into several contexts according to the characteristic of the causal part. Each context has a frequency table, which reflects the probability distribution of the data belonging to this context. However, in practice, it often happens that the characteristic of the causal part is between that of two contexts. For example, if we assign the context according to whether the average gradient of the causal part is greater or smaller than 10, when the average gradient is around 10, if we assign the case into one of the contexts, since the input is not typical for both two contexts, the coding gain may not be achieved. In this work, we proposed an idea of mixed context. That is, in the intermediate case, the frequency table is constructed from that of the two adjacent contexts. Moreover, after encoding the data, the frequency table of both the two contexts are adjusted. Experiments for the example of JPEG DC term and lossless image encoding show that, with the proposed algorithm, the coding efficiency can be much improved and the frequency table converges to the truth probability distribution quickly.
Dual Prototypical Network for Robust Few-shot Image Classification

Deep neural networks have outperformed humans on some image recognition and classification tasks. However, with the emergence of various novel classes, it remains a chal-lenge to continuously expand the learning capability of such networks from a limited number of labeled samples. Metric-based approaches have been playing a key role in few-shot image classification, but most of them measure the distance between samples in the metric space using only a single metric function. In this paper, we propose a Dual Prototypical Network (DPN) to improve the test-time robustness of the classical prototypical network. The proposed method not only focuses on the distance of the original features, but also adds perturbation noise to the image and calculates the distance of noisy features. By enforcing the model to predict well under both metrics, more representative and robust class prototypes are learned and thus lead to better generalization performance. We validate our method on three fine-grained datasets in both clean and noisy settings.
On the Adversarial Transferability of ConvMixer Models

Deep neural networks (DNNs) are well known to be vulnerable to adversarial examples (AEs). In addition, AEs have adversarial transferability, which means AEs generated for a source model can fool another black-box model (target model) with a non-trivial probability. In this paper, we investigate the property of adversarial transferability between models including ConvMixer, which is an isotropic network, for the first time. To objectively verify the property of transferability, the robustness of models is evaluated by using a benchmark attack method called AutoAttack. In an image classification experiment, ConvMixer is confirmed to be weak to adversarial transferability.
Convolutional Layers Acceleration By Exploring Optimal Filter Structures

CNN models are becoming more and more mature, many of them adopt deeper structures to better accomplish the task objectives, such that the increased computational and storage burdens are unfavorable for the implementation in edge devices. In this paper, we propose an approach to optimize the filter structure by starting from the convolutional filter and finding their minimum structure. The reductions of the filters for the minimum structure in terms of space and channels, the number of model parameters and the computational complexity are effectively reduced. Since the current channel pruning method prunes the same channel for each convolutional layer, which easily leads to a trade-off between the pruning rate and accuracy loss. Instead we propose a new channel pruning approach to find the most suitable required channels for each filter to provide a more detailed pruning method. Experiments conducted on the classification CNN models, such as VGG16 and ResNet56, show that the proposed method can successfully reduce the computations of the models without losing much model accuracy effectively. The proposed method performs well in compressing the model and reducing the number of parameters required by the models for real applications.
Non-Autoregressive Speech Recognition with Error Correction Module

Autoregressive models have achieved good performance in the field of speech recognition. However, the autore-gressive model uses recursive decoding and beam search in the inference stage, which leads to its slow inference speed. On the other hand, the non-autoregressive model naturally cannot utilize the context since all tokens are output at one time. To solve this problem, we propose a position-dependent non-autoregressive model. And in order to make better use of contextual information, we propose a pre-trained language model for speech recognition, which is placed behind the non-autoregressive model as an error correction module. In this way, we exchanged a smaller amount of calculation for the improvement of the recognition rate. Our method not only greatly reduces the computational cost, but also maintains a good recognition rate. We tested our model on the public Chinese speech corpus AISHELL-1. Our model achieves a 6.5% character error rate while the real-time factor is only 0.0022, which is 1/17 of the autoregressive model.
GNSS Carrier Tracking via Variational Adaptive Kalman Filter

In this paper, the carrier tracking for global navigation satellite system (GNSS) receivers is implemented by the powerful Kalman filter (KF) technique. To exploit the potential of the KF, efforts are dedicated in terms of two aspects. First, a 4-state model which can reflect the signal dynamics more accurately is introduced into the KF. By using the 4-state model rather than the widely used 3-state model, better tracking performance can be obtained. Second, the adaptive KF (AKF) is used to overcome the inherent defects of the KF. The variational AKF is successfully adopted in the GNSS carrier tracking. And it shows superior performance compared with existing filters. Simulation results confirm the applicability and superiority of the proposed method.
Combined 2D and 3D Convolution Residual Attention Network for Hand Gesture Recognition

Hand gesture recognition is a classical problem in human-computer interaction research. In this paper, a learning-based model is proposed for hand gesture recognition. Our model receives RGB and depth channels input. To recognize the hand gesture, the segmentation of hand region is the important issue. At first, we apply patch embedding layer to encode all the frames as several patches. Then, these encoded patches are fed into 3D convolution network. The 3D convolution layer can simultaneously learn the spatial and temporal feature of the video. The 3D convolution network also contains attention block, which is used to enhance the crucial feature map value. Besides, the encoded patches pass through the local decoder to recover the depth frames of the video. This operation can preserve the depth information in encoded patches. At last, we perform the linear classifier for the output of 3D convolution network to get the result of hand gesture. Our method achieves 80.5% accuracy in the NV-Gesture dataset and 89.6% accuracy in the SKIG dataset.
Highly Robust Action Retrieval using View-invariant Pose Feature and Simple yet Effective Query Expansion Method

Action retrieval and detection utilizing view-invariant pose based feature achieve high precision. However the technology has a problem of low recall because of the large individual differences in action. Query-expansion(QE) methods are well known as effective ways to improve recall in object detection and retrieval task, but few research adapt it to the action retrieval task. We focused on the query expansion method and proposed new query generation method in which two queries containing missing points complement each other's missing points to perform high-recall action retrieval. The experimental results are reported to show that our method outperforms the state-of-the-art methods in a simulated dataset with annotated multi-view 2D poses and a real-world video dataset.
Controllable voice conversion based on quantization of voice factor scores

Various voice conversion models using deep learning have been proposed. However, most focus on speaker identity con-version from one person to another. To the best of our knowledge, there is no model that controls the factors that determine voice tone (e.g. husky and nasal) and transforms it. We propose voice factor quantized splitternetVC (VFQ-SplitterNetVC), a voice conversion model that can control voice tone by learning factors that determine voice tone, such as the degree of huskiness, and their strength and weakness, based on SplitterNetVC and vector quantized-variational auto encoder (VQ- VAE). We evaluated the naturalness of the converted voice, speaker similarity, and voice tone control performance of the proposed model in subjective experiments. We found that VFQ-SplitterNetVC showed high speaker similarity and voice tone control performance.
A title generation method with Transformer for journal articles

While many methods of summarization have been proposed, there have been few methods to generate a title, especially for journal articles. However, the differences between summarization and creating a title are length and clause form. We propose a title generation model for a journal article based on Transformer, which refers to a wide range of the article. We propose to narrow down the abstract sentences to only important sentences before title generation so that the author's claim can be easily reflected in the title. We applied our method to journal articles published on arXiv.org and found that our model generated a title including words in the original title.
An Application of Network Communication Technology in Classroom Cloud Notes

If there is no network communication in the Android world, then the Android world is a collection of isolated islands. The most common network request framework in Android development is the Retrofit framework. In addition, Volley and OkHttp, which are widely used, have their own advantages and disadvantages. The best comprehensive performance is the Retrofit framework, which has a convenient and easy-to-use request interface and can easily automate entity resolution. Decouple the parsing and asynchronous request frameworks, and the superior data access and storage performance makes network requests concise and elegant. Finally, an example is given to illustrate the application of Retrofit framework in cloud notes in classes.
Partially Occluded Self-taught Learning via Residual Connection Union

Self-taught learning is a promising technology that utilizes easily accessible label-free images to narrow the performance gap in few-shot learning. During data collection, it naturally comes along with numerous partially occluded samples that miss discriminative information. However, few works have seriously studied the impact of partially occluded samples on self-taught learning. In this paper, we propose a cross combination oriented auto-encoder for partially occluded self-taught learning. The proposed auto-encoder is composed of multi-scale Residual Connection Unions (RCU). The innovation of RCU lies in its capability of integrating local attention and self-attention into a united structure, which facilitates exploring the potential relation between convolution and self-attention operators under a different receptive field. Extensive experiments on our built and Caltech 101 datasets demonstrate that the proposed method sharply outperforms the existing state-of-the-art competitors in heavily occluded cases.
Physiological study on the effect of game events in response to player's laughter

To investigate whether computer's automatic responses to our emotional expression influences our cognitive and emotional involvement in a virtual world, this study examined to measure the player's physiological reactions to game events presented in response to the players' spontaneous laughter. Participants played two conditional virtual games in our experiments, and their electrocardiogram, electrodermal activity, and facial electromyography (corrugator supercilii muscle and zygomaticus major muscle) were recorded during the games. The experiment consisted of two conditions, namely advantageous event condition and disadvantageous event condition. In the advantageous event condition, the system responded to the player's laughter with an event that benefitted the player. In the disadvantageous event condition, the system responded to the player's laughter with an event that annoyed the player. A three-way analysis of variance was performed using these physiological signals to test the hypothesis that there is time-series variation in physiological responses between both event types and event durations. As a result, a significantly slower heart rate was observed after the presentation of an event in both the advantageous/disadvantageous event conditions. This result suggests that the players paid more attention to the game when any event was generated against their laughter. Moreover, both type of events to the player's laughter more activated electrodermal activity and corrugator supercilii muscle. In particular, the disadvantageous events to the player's laughter more activated corrugator supercilii muscle than the advantageous event. These results suggest that players were more emotionally engaged in the game when they encountered troublesome or fortunate situations while laughing.
Continuous authentication for smartphones using face images and touch-screen operation

Conventional user authentication methods for smartphones using PINs, passwords, pattern locks, etc. have a problem in that user authentication is not performed continuously after the first authentication success; therefore, there is a risk that an authenticated smartphone might be used improperly by unauthorized individuals. We propose a novel continuous authentication method for smartphones that uses face images and touch-screen operation and evaluated its effectiveness.
Partial face recognition based on sparse non-local regularization weighting

Although the unobstructed face recognition technology has gradually become mature, some face recognition technologies are still in the initial stage, and there are still many difficulties. In the current epidemic situation, people wear masks when they travel. Therefore, the acquired face image is occluded. Because of this, the acquired face image is incomplete, which will lead to insufficient prior information and greatly reduce the face recognition rate. To this end, a partial face recognition method based on sparse non-local regularization weighted coding is proposed. The algorithm uses a full convolutional neural network to obtain facial features for the variability of partial face images. The image of any size is trained and learned, and the face alignment operation of the original image is not needed, and the face information is better preserved. According to the characteristics of local sparse and non-local self-similarity, non-local regularization weighted coding is introduced in the sparse representation classifier. The experimental results show that the proposed method not only solves the arbitrary block of face image recognition, but also has higher recognition and better robustness than the existing algorithms of dynamic feature matching. (Abstract)
Autoencoder performance analysis with adaptive and trainable activation function to compress images

Activation functions are essential keys to good performance in a neural network. Many functions can be used, and the choice of which one to use depends on the issues addressed. New adaptable and trainable activation functions have been studied lately, which are used to increase network performance. This study intends to evaluate the performance of an artificial neuron that uses adaptive and trainable functions in an Autoencoder network for image compression problems. The tested neuron, known as Global-Local Neuron, comprises two complementary components, one with global characteristics and the other with local characteristics. The global component is given by a sine function and the local component by the hyperbolic tangent function. The experiment was carried out in two stages. In the first one, different activation functions, GLN, Tanh, and Sine, were tested in an MLP-type Autoencoder neural network model. Different compression ratios were considered when varying the size of the Autoencoder bottleneck layer, and 48 samples were obtained for each value of this layer. The metrics used for the evaluation were the loss value obtained in the test set and the number of epochs necessary to reach a stopping criterion. In the second step, the classification accuracy of the images compressed by the encoder block of the previous model was evaluated, using a Wide Residual Networks (WRN) network and the Support Vector Machines (SVM) method. The results obtained indicated that the use of Global-Local Neuron improved the network training speed, obtained better classification accuracy for compression up to 50% in a WRN network, and proved the adaptability in image classification problems.
A Novelty Adversarial Loss for Classifying Unbalanced Anomaly Images

The class imbalance problem has lately witnessed substantial progress attributed to the powerful feature representation capability of deep convolutional neural networks (CNNs). However, most of existing adversarial methods are not proficient in learning the distribution of abnormal samples because their loss function is only designed for learning the distribution of normal samples. In this paper, inspired by the existing adversarial methods, we increase an encoder loss to obtain a discriminative margin for abnormal samples based on existing adversarial loss. We also collect a professional shockproof hammer dataset called SHD. Our method is implemented on the SHD dataset and public CIFAR-10 dataset, which needs a large number of normal samples and only a few abnormal samples. Experimental results demonstrate that our approach is capable of achieving an efficient classification score with a higher area under the curve (AUC) results compared to the existing common methods on the above two datasets.
Effects of incorporating a deep-unfolding framework into a deep neural network: implications for image restoration

Deep-learning-based image restoration methods typically require large datasets to achieve high levels of performance. However, there are some tasks for which acquisition costs render it difficult to obtain such datasets. In such cases, the datasets tend to be small, overfitting and poor generalization performance are encountered with models trained on a small dataset. Model-based optimization methods are widely employed in image restoration to avoid such problems, because they are effective when it is difficult to obtain a large amount of data. Thus, herein, we propose a method to improve the generalization performance, particularly under the non-ideal conditions of a limited amount of training data, by combining a deep neural network with a deep unfolding network. The deep neural network performs the preprocessing for the deep unfolding network. The deep unfolding network takes the data transformed by the deep neural network as input and restores the image to satisfy the given condition for regularization. The proposed method is applied to a small dataset for denoising and pixel interpolation, and it is confirmed that the generalization performance of the deep neural network is improved.
LCN: Label Correction Based on Network Prediction for Cross-Modal Retrieval with Noisy Labels

Cross-modal retrieval enables users to obtain information related to the input query by crossing different modalities (e.g., from a text to an image). Recently, the research community has tackled the challenging situation where noisy labels are included in the training data. The state-of-the-art method trains the network so that weights for correct labels in the loss function can be increased. However, we newly found that the network is finally overfitted to the remaining noisy labels as training progresses. Motivated by this finding, this paper proposes a method called Label Correction based on Network prediction (LCN) to correct noisy labels, unlike the state-of-the-art method that leaves noisy labels on training. Specifically, we assume that noisy labels are irrelevant to the data features. Based on this assumption, we can consider predicted labels (obtained by network prediction) that are relevant to data features are more reliable than given labels. Moreover, a novel scheme for adjusting the number of the predicted labels used for training is introduced into our method. This is useful for solving the problem that the network prediction is ambiguous in the early stages of training. Experimental results show that LCN dramatically alleviates the remaining noisy labels to achieve high performance in cross-modal retrieval in the real-world scenario.
Assignment of bug reports to software developers using a multi-population evolutionary method

Existing approaches assign bug reports using only data from previously fixed reports. This can result in assignments to inactive developers, as well as not considering newcomers. A significant portion of assignments typically do not consider the workload of developers, which can overwhelm some and make the revision/debugging/correction process more time-consuming. This work proposes an approach for assigning bug reports that combines the experience and recent activities of developers, as well as their workload. When a new report is received, the effort to fix the bug based on similar error is estimated and each developer’s affinity with the file containing the bug is calculated using a Fuzzy Inference system. Subsequently, the Golden Ball, a multi-population evolutionary metaheuristic, is used to assign these reports to developers according to affinity and workload. Experimental results show that, when compared with a brute force algorithm, the proposed approach reaches optimal values for assign in most cases (75% of the analyzed scenarios). The approach also obtained significantly better averages in 92.30% of the instances when compared to a Genetic Algorithm and 84.61% when compared to a Distributed Genetic Algorithm, and in only 23.07% of the instances there was no significant difference between the techniques.