Reviewerly/exHarmony huggingface dataset:
Adaptive sparse dropout: Learning the certainty and uncertainty in deep neural networks

Dropout is an important training method for deep neural networks, because it can help avoid over-fitting. Traditional dropout methods and many extended dropout methods, omit some of the neurons’ activation values according to the probabilities. These methods calculate the activation probability of neurons using the designed formula, without providing a plausible explanation of the calculation method. This paper proposes an adaptive sparse dropout (AS-Dropout) method for neural network training. The algorithm maps the neurons’ activation values in a layer to a relative linear range of a sigmoid function, determines the ratio of active neurons by a probability calculation process, and drops most of neurons according to the probabilities. The probability calculation depends on the activation values of the neurons. The selection of active neurons is according to the probabilities. Therefore, AS-Dropout learns both the certainty and uncertainty in deep neural networks. Additionally, since only a small number of neurons are active, AS-Dropout increases the sparsity of the network. We applied AS-Dropout in different neural network structures. When evaluated on MNIST, COIL-100, and Caltech-101 datasets, the experimental results demonstrated that, overall, AS-Dropout substantially outperformed the traditional dropout and some improved dropout methods.
Role-Aware Modeling for N-ary Relational Knowledge Bases

N-ary relational knowledge bases (KBs) represent knowledge with binary and beyond-binary relational facts. Especially, in an n-ary relational fact, the involved entities play different roles, e.g., the ternary relation PlayCharacterIn consists of three roles, Actor, Character and Movie. However, existing approaches are often directly extended from binary relational KBs, i.e., knowledge graphs, while missing the important semantic property of role. Therefore, we start from the role level, and propose a Role-Aware Modeling, RAM for short, for facts in n-ary relational KBs. RAM explores a latent space that contains basis vectors, and represents roles by linear combinations of these vectors. This way encourages semantically related roles to have close representations. RAM further introduces a pattern matrix that captures the compatibility between the role and all involved entities. To this end, it presents a multilinear scoring function to measure the plausibility of a fact composed by certain roles and entities. We show that RAM achieves both theoretical full expressiveness and computation efficiency, which also provides an elegant generalization for approaches in binary relational KBs. Experiments demonstrate that RAM outperforms representative baselines on both n-ary and binary relational datasets.
DeepShadows: Separating low surface brightness galaxies from artifacts using deep learning

Searches for low-surface-brightness galaxies (LSBGs) in galaxy surveys are plagued by the presence of a large number of artifacts (e.g., objects blended in the diffuse light from stars and galaxies, Galactic cirrus, star-forming regions in the arms of spiral galaxies, etc.) that have to be rejected through time consuming visual inspection. In future surveys, which are expected to collect hundreds of petabytes of data and detect billions of objects, such an approach will not be feasible. We investigate the use of convolutional neural networks (CNNs) for the problem of separating LSBGs from artifacts in survey images. We take advantage of the fact that we have available a large number of labeled LSBGs and artifacts from the Dark Energy Survey, that we use to train, validate, and test a CNN model. That model, which we call DeepShadows, achieves a test accuracy of 92.0%, a significant improvement relative to feature-based machine learning models. We also study the ability to use transfer learning to adapt this model to classify objects from the deeper Hyper-Suprime-Cam survey, and we show that after the model is retrained on a very small sample from the new survey, it can reach an accuracy of 87.6%. These results demonstrate that CNNs offer a very promising path in the quest to study the low-surface-brightness universe.
Supervised compression of big data

Abstract The phenomenon of big data has become ubiquitous in nearly all disciplines, from science to engineering. A key challenge is the use of such data for fitting statistical and machine learning models, which can incur high computational and storage costs. One solution is to perform model fitting on a carefully selected subset of the data. Various data reduction methods have been proposed in the literature, ranging from random subsampling to optimal experimental design‐based methods. However, when the goal is to learn the underlying input–output relationship, such reduction methods may not be ideal, since it does not make use of information contained in the output. To this end, we propose a supervised data compression method called supercompress , which integrates output information by sampling data from regions most important for modeling the desired input–output relationship. An advantage of supercompress is that it is nonparametric—the compression method does not rely on parametric modeling assumptions between inputs and output. As a result, the proposed method is robust to a wide range of modeling choices. We demonstrate the usefulness of supercompress over existing data reduction methods, in both simulations and a taxicab predictive modeling application.
CricShotClassify: An Approach to Classifying Batting Shots from Cricket Videos Using a Convolutional Neural Network and Gated Recurrent Unit

Recognizing the sport of cricket on the basis of different batting shots can be a significant part of context-based advertisement to users watching cricket, generating sensor-based commentary systems and coaching assistants. Due to the similarity between different batting shots, manual feature extraction from video frames is tedious. This paper proposes a hybrid deep-neural-network architecture for classifying 10 different cricket batting shots from offline videos. We composed a novel dataset, CricShot10, comprising uneven lengths of batting shots and unpredictable illumination conditions. Impelled by the enormous success of deep-learning models, we utilized a convolutional neural network (CNN) for automatic feature extraction, and a gated recurrent unit (GRU) to deal with long temporal dependency. Initially, conventional CNN and dilated CNN-based architectures were developed. Following that, different transfer-learning models were investigated—namely, VGG16, InceptionV3, Xception, and DenseNet169—which freeze all the layers. Experiment results demonstrated that the VGG16–GRU model outperformed the other models by attaining 86% accuracy. We further explored VGG16 and two models were developed, one by freezing all but the final 4 VGG16 layers, and another by freezing all but the final 8 VGG16 layers. On our CricShot10 dataset, these two models were 93% accurate. These results verify the effectiveness of our proposed architecture compared with other methods in terms of accuracy.
Graph-based neural network models with multiple self-supervised auxiliary tasks

Self-supervised learning is currently gaining a lot of attention, as it allows neural networks to learn robust representations from large quantities of unlabeled data. Additionally, multi-task learning can further improve representation learning by training networks simultaneously on related tasks, leading to significant performance improvements. In this paper, we propose three novel self-supervised auxiliary tasks to train graph-based neural network models in a multi-task fashion. Since Graph Convolutional Networks are among the most promising approaches for capturing relationships among structured data points, we use them as a building block to achieve competitive results on standard semi-supervised graph classification tasks.
Conditional Variational Capsule Network for Open Set Recognition

In open set recognition, a classifier has to detect unknown classes that are not known at training time. In order to recognize new categories, the classifier has to project the input samples of known classes in very compact and separated regions of the features space for discriminating samples of unknown classes. Recently proposed Capsule Networks have shown to outperform alternatives in many fields, particularly in image recognition, however they have not been fully applied yet to open-set recognition. In capsule networks, scalar neurons are replaced by capsule vectors or matrices, whose entries represent different proper-ties of objects. In our proposal, during training, capsules features of the same known class are encouraged to match a pre-defined gaussian, one for each class. To this end, we use the variational autoencoder framework, with a set of gaussian priors as the approximation for the posterior distribution. In this way, we are able to control the compactness of the features of the same class around the center of the gaussians, thus controlling the ability of the classifier in detecting samples from unknown classes. We conducted several experiments and ablation of our model, obtaining state of the art results on different datasets in the open set recognition and unknown detection tasks.
Visual Image Caption Generator Using Deep Learning

Image Caption Generation has always been a study of great interest to the researchers in the Artificial Intelligence department. Being able to program a machine to accurately describe an image or an environment like an average human has major applications in the field of robotic vision, business and many more. This has been a challenging task in the field of artificial intelligence throughout the years. In this paper, we present different image caption generating models based on deep neural networks, focusing on the various RNN techniques and analyzing their influence on the sentence generation. We have also generated captions for sample images and compared the different feature extraction and encoder models to analyse which model gives better accuracy and generates the desired results.
Non-interactive Batch Arguments for NP from Standard Assumptions

We study the problem of designing non-interactive batch arguments for $$\mathsf {NP}$$ . Such an argument system allows an efficient prover to prove multiple $$\mathsf {NP}$$ statements, with size smaller than the combined witness length. We provide the first construction of such an argument system for $$\mathsf {NP}$$ in the common reference string model based on standard cryptographic assumptions. Prior works either require non-standard assumptions (or the random oracle model) or can only support private verification. At the heart of our result is a new dual mode interactive batch argument system for $$\mathsf {NP}$$ . We show how to apply the correlation-intractability framework for Fiat-Shamir – that has primarily been applied to proof systems – to such interactive arguments.
Global context-aware multi-scale features aggregative network for salient object detection

Deep convolutional neural networks have gained aggressive success in salient object detection. This paper uses the Multi-Scale Feature Extraction Module (MFEM) for each backbone level to get multi-scale contextual knowledge. We propose the Cross Feature Aggregation Modules(CFAM) to integrate the various features from adjacent levels, which comparatively propagate less noise due to small up-/down sampling rates. To further refine individual-level integrated features, we design Self Interactive Modules (SIRM) at each decoder stage. The SIRM utilizes the spatial- and channel-wise attention to suppress the non-salient regions while assigning more weights to the foreground salient object to visualize the submissive regions (i.e., some salient regions looking like non-salient regions) of the salient objects. Our network can enhance size-varying objects’ illustration proficiency by adopting the multi-scale feature extraction capability in each module. Besides, we develop the Global Context Flow Module (GCFM) to get the global context knowledge at different points in the decoder, which aims to acquire the association among different salient regions and mitigate the dilution of high-level features. Our proposed model (i.e., GCMANet) follows a supervised way to generate the saliency maps. The results produced over publicly available datasets verify that our model outperforms its counterparts in quantitative and qualitative measurements.
Tripoline

For compute-intensive iterative queries over a streaming graph, it is critical to evaluate the queries continuously and incrementally for best efficiency. However, the existing incremental graph processing requires a priori knowledge of the query (e.g., the source vertex of a vertex-specific query); otherwise, it has to fall back to the expensive full evaluation that starts from scratch.
Adaptive center pixel selection strategy in Local Binary Pattern for texture classification

Local Binary Pattern (LBP) is widely used in texture classification because of its powerful capability to extract texture features of a center pixel. However, LBP has three main drawbacks: (1) limited by the low resolution of imaging device, the quality of texture image is degraded, and some real existing pixels with more texture information are unavoidably lost. (2) Center pixel gc is the most important factor to extract correct LBP pattern. However, by far LBP and its variants do not show any solutions to enhance the robustness of center pixel gc. (3) Some LBP patterns include important texture microstructures, but they are ignored by uniform patterns. At the same time, some uniform patterns can be corrupted by noise and misclassified into non-uniform patterns. These LBP patterns therefore all lost their discrimination capability. In order to overcome these three disadvantages, in this paper, we propose a novel adaptive center pixel selection (ACPS) strategy. Inspired by image super-resolution techniques, ACPS firstly applies the interpolation method to recover the lost real existing pixels and generate the center pixel candidates with more texture information. Then, the gradient information is used to obtain the edge image aiming to find the non-uniform patterns at edge points which may contain complicated texture microstructures. After generating the center pixel candidates and edge image, we introduce ACPS strategy into the LBP framework. By adaptively selecting the optimal center pixel from all center pixel candidates, one non-uniform pattern at the edge point can be possibly recovered to the uniform pattern, and regain its discrimination power. It is worth noting that any other LBP variants can also employ the ACPS strategy to more effectively extract its texture features. By observing the experimental results on representative texture databases of Outex, UIUC, CUReT, XU_HR, ALOT and KTHTIPS2b after introducing the ACPS strategy into LBP and its variants of LTP, CLBP, BRINT, CRDP, FbLBP, and CJLBP, the texture classification performances can be significantly improved.
Learning large-scale fuzzy cognitive maps using an evolutionary many-task algorithm

Fuzzy cognitive maps (FCMs) are a powerful tool for simulating and analyzing complex systems. Many efficient methods based on evolutionary algorithms have been proposed to learn small-scale FCMs. However, large number of function evaluations of those methods make them difficult to cope with large-scale FCM learning problems. To overcome this issue, we propose a random inactivation-based batch many-task evolutionary algorithm, termed as IBMTEA-FCM. Inspired by the probability of knowledge sharing in different tasks, the problem of FCM learning is first modeled as a many-task optimization problem, in which each task represents learning local connections of a node in a single FCM. To ensure the effectiveness of knowledge transfer, all tasks are randomly divided into multiple batches to optimize separately. In this method, an evolutionary many-task framework is employed to overcome the proposed many-task FCM learning problem and we randomly deactivate weighted edges to ensure the sparsity of FCM in the evolutionary process. The performance of IBMTEA-FCM is validated on both synthetic datasets and a practical study of gene regulatory network reconstruction. Compared with existing classical methods, the experimental results show that IBMTEA-FCM can learn large-scale FCMs with higher accuracy and less computational cost.
Target Recovery for Robust Deep Learning-Based Person Following in Mobile Robots: Online Trajectory Prediction

The ability to predict a person’s trajectory and recover a target person in the event the target moves out of the field of view of the robot’s camera is an important requirement for mobile robots designed to follow a specific person in the workspace. This paper describes an extended work of an online learning framework for trajectory prediction and recovery, integrated with a deep learning-based person-following system. The proposed framework first detects and tracks persons in real time using the single-shot multibox detector deep neural network. It then estimates the real-world positions of the persons by using a point cloud and identifies the target person to be followed by extracting the clothes color using the hue-saturation-value model. The framework allows the robot to learn online the target trajectory prediction according to the historical path of the target person. The global and local path planners create robot trajectories that follow the target while avoiding static and dynamic obstacles, all of which are elaborately designed in the state machine control. We conducted intensive experiments in a realistic environment with multiple people and sharp corners behind which the target person may quickly disappear. The experimental results demonstrated the effectiveness and practicability of the proposed framework in the given environment.
BPFINet: Boundary-aware progressive feature integration network for salient object detection

Recently, convolutional neural networks have improved the results of salient object detection by a significant margin. Most existing methods focus on aggregating multi-level features but pay little attention to the differences between their spatial resolution. Besides, the widely used binary cross entropy loss treats all pixels equally and neglects the fact that pixels near the salient boundaries are prone to being misclassified. To solve these problems, we propose a novel network named BPFINet to aggregate low-level detail features, high-level semantic information, and global information progressively by using the U-shape Feature Integration Modules (UFIMs). Moreover, a U-shape Self-Refinement Module (USRM) is proposed to generate multi-scale representation from the intra-layer features and fuse them progressively to generate features robust to scale variation of salient objects. Besides, a Channel Compression Module (CCM) is designed to reduce the channel number of certain features and enhance the features by leveraging channel-wise attention. Furthermore, an integrated loss is introduced to highlight pixels near the salient boundaries and solve the problem caused by the imbalance of foreground and background regions. Experimental results on six benchmark datasets prove that our BPFINet is competitive compared with 16 other state-of-the-art methods. The source code will be publicly available at https://github.com/clelouch/BPFINet .
Automatic 3D Seam Extraction Method for Welding Robot Based on Monocular Structured Light

The teaching programming mode and offline programming mode of welding robot are essential parts in today's manufacturing industry. However, these modes can't meet the automation requirements and adaptive ability of welding robot. To achieve 3D path acquisition of weld seam for robot autonomous programming, a fast and accurate offline 3D seam extraction method is proposed based on monocular structured light sensor. In this method, gray code and phase-shift code raster images are projected to the workpiece, and the 3D coordinates of the weld are calculated by collecting the workpiece images with raster patterns. The path fitting of robot is completed by polynomial fitting method. The novel monocular structured light sensor used by this paper has compact structure and small volume, and can adapt to a variety of welding working scenes. The combination of gray code and phase shift code makes the measurement accurate and fast, and achieves the effect of full resolution decoding. The experimental results show that the proposed method can complete the task of 3D weld extraction and path fitting, which has the characteristics of high efficiency and strong robustness.
RE-BERT

Traditionally, developers restricted themselves to collecting opinions from a small group of users by using techniques such as interviews, questionnaires, and meetings. With the popularization of social media and mobile applications, these professionals have to deal with crowd users' opinions, who want to voice the software's evolution. In this context, one of the main related tasks is the automatic identification of software requirements from app reviews. Recent studies show that existing methods fail at this task, since review texts usually contain informal language, contain grammatical and spelling errors, as well as the difficulty in filtering out irrelevant information that has no practical value for developers. In this paper, we present the RE-BERT (Requirements Engineering using Bidirectional Encoder Representations from Transformers). Our method innovates by using pre-trained neural language models to generate semantic textual representations with contextual word embeddings. Our RE-BERT performs fine-tuning of the BERT model with a focus on the local context of the software requirement tokens. A statistical analysis of the experimental results involving eight different apps showed that our RE-BERT outperforms existing state-of-the-art methods.
EdgeLSTM: Towards Deep and Sequential Edge Computing for IoT Applications

The time series data generated by massive sensors in Internet of Things (IoT) is extremely dynamic, heterogeneous, large scale and time-dependent. It poses great challenges (e.g. accuracy, reliability, stability) on the real-time analysis and decision making for different IoT applications. In this paper, we design, implement and evaluate EdgeLSTM, a unified data-driven system to enhance IoT computing at the network edge. The EdgeLSTM leverages the grid long short-term memory (Grid LSTM) to provide an agile solution for both deep and sequential computation, therefore can address important features such as large-scale, variety, time dependency and real time in IoT data. Our system exploits the advantages of Grid LSTM network and extends it with a multiclass support vector machine by rigorous regularization and optimization approaches, which not only has strong prediction capability of time series data, but also achieves fine-grained multiple classification through the predictive error. We deploy the EdgeLSTM into four IoT applications, including data prediction, anomaly detection, network maintenance and mobility management by extensive experiments. Our evaluation results of real-world time series data with different short-term and long-term time dependency from these typical IoT applications show that our EdgeLSTM system can guarantee robust performance in IoT computing.