Reviewerly/exHarmony huggingface dataset:
A sentiment analysis method for COVID-19 network comments integrated with semantic concept

In recent years, the new coronavirus COVID-19 has brought great disaster and loss to the world and is still spreading around the world. The situation in China is generally well controlled, and the lockdown has been removed, but the comments and messages about the epidemic persist online. For people working and living normally in China, their attitudes and views toward COVID-19 directly reflect the current situation of the pandemic. This paper collected Chinese microblogs, forums, and online comments, identified the latest comments about COVID-19, and conducted a sentiment analysis of them. Specifically, we proposed a new sentiment analysis method that integrated the semantics of words with the text analyzed. Different from the traditional sentiment analysis method which only relied on sentiment words, the proposed method extended the semantic concepts of affective words by integrating the semantic conceptual information about the affective words from the context of the comments and thus, provided information to support the final judgment of the affective opinions. The proposed approach incorporated the part-of-speech embedding information along with word embedding and relies on semantic concepts to enhance the emotional expression of words in context. The experimental results showed that by integrating the semantics of words, the accuracy of sentiment analysis is substantially improved, and it also reflected that different semantics of the same word have different influences on sentiment analysis. On several benchmark datasets, there was a 3–6% improvement in accuracy.
Multilinear subspace learning for Person Re-Identification based fusion of high order tensor features

Video surveillance image analysis and processing is an important field in computer vision and among the challenging tasks for Person Re-Identification (PRe-ID). The latter aims at finding a target person who has already been identified and appeared on a camera network using a powerful description of their pedestrian images. The success of recent research on person PRe-ID is largely backed into the effective features extraction and representation with a powerful learning of these features to correctly discriminate pedestrian images. To this end, two powerful features, Convolutional Neural Network (CNN) and Local Maximal Occurrence (LOMO) are modeled on a multidimensional data in the proposed method, High-Dimensional Feature Fusion (HDFF). Specifically, a new tensor fusion scheme is introduced to take advantage and combine two types of features in the same tensor data even if its dimensions are not the same. To improve the accuracy, we use Tensor Cross-View Quadratic Analysis (TXQDA) to perform multilinear subspace learning followed by the Cosine similarity for matching. TXQDA efficiently ensures the learning ability and reduces the high dimensionality resulting from high-order tensor data. The effectiveness of the proposed method is verified through experiments on three challenging widely-used PRe-ID datasets namely, VIPeR, GRID, and PRID450S. Extensive experiments show that the proposed method performs very well when compared with recent state-of-the-art methods.
A fast planning approach for 3D short trajectory with a parallel framework

For real applications of unmanned aerial vehicles, the capability of navigating with full autonomy in unknown environments is a crucial requirement. However, planning a shorter path with less computing time is contradictory. To address this problem, we present a framework with the map planner and point cloud planner running in parallel in this paper. The map planner determines the initial path using the improved jump point search method on the 2D map, and then it tries to optimize the path by considering a possible shorter 3D path. The point cloud planner is executed at a high frequency to generate the motion primitives. It makes the drone follow the solved path and avoid the suddenly appearing obstacles nearby. Thus, vehicles can achieve a short trajectory while reacting quickly to the intruding obstacles. We demonstrate fully autonomous quadrotor flight tests in unknown and complex environments with static and dynamic obstacles to validate the proposed method. In simulation and hardware experiments, the proposed framework shows satisfactorily comprehensive performance.
Single-image super-resolution based on an improved asymmetric Laplacian pyramid structure

Large-scale factor image super-resolution whose scale factor is greater than 4 is significant in real-world applications for single image super-resolution. The current image super-resolution techniques for large-scale factor, however, frequently upsample low-resolution images in a single pass, leading to edge artifacts in the reconstructed images. In this article, we provide an improved asymmetric Laplacian pyramid network to further realize large-scale factor image super-resolution and fully utilize various size features. A distinct architecture is applied at each layer of the pyramid for improving feature extraction. We extend the first layer of the pyramid with a lightweight transformer design, which enables the model to efficiently collect the contextual information in the sequence by utilizing the multi-headed attention mechanism. Additionally, we combine improved dense skip connections with recursive operations to form a deep dense recursive convolutional neural network that fuses low-level and high-level features while broadening the network's receptive field. The quantitative and qualitative analysis on benchmark dataset's demonstrate that our method provides superior performance in both PSNR and SSIM and is more in line with human visual perception.
Invariant Representations Learning with Future Dynamics

High-dimension inputs limit the sample efficiency of deep reinforcement learning, increasing the difficulty of applying it to real-world continuous control tasks, especially in uncertain environments. A good state embedding is crucial for the performance of agents on downstream tasks. The bisimulation metric is an excellent representation learning method that can abstract task-relevant invariant latent embeddings of states based on behavior similarity. However, since only one time-step transition is considered, the features captured by this method we call short-term dynamics. We think long-term dynamics are also important for state representation learning. In this paper, we present Invariant Representations Learning with Future Dynamics (RLF), which uses graph neural networks to learn long-term dynamics and trains the representation network based on a new state metric inspired by bisimulation relation. We experimented with our method on continuous control tasks from DeepMind Control Suite and showed that the RLF can mine more stable embeddings than the state-of-the-art representation learning methods for both state and pixel inputs. The learned policy on top of the embeddings has higher sample efficiency and performance and generalizes well in different tasks.
Feature-wise scaling and shifting: Improving the generalization capability of neural networks through capturing independent information of features

From the perspective of input features, information can be divided into independent information and correlation information. Current neural networks mainly concentrate on the capturing of correlation information through connection weight parameters supplemented by bias parameters. This paper introduces feature-wise scaling and shifting (FwSS) into neural networks for capturing independent information of features, and proposes a new neural network FwSSNet. In the network, a pair of scale and shift parameters is added before each input of each network layer, and bias is removed. The parameters are initialized as 1 and 0, respectively, and trained at separate learning rates, to guarantee the fully capturing of independence and correlation information. The learning rates of FwSS parameters depend on input data and the training speed ratios of adjacent FwSS and connection sublayers, meanwhile those of weight parameters remain unchanged as plain networks. Further, FwSS unifies the scaling and shifting operations in batch normalization (BN), and FwSSNet with BN is established through introducing a preprocessing layer. FwSS parameters except those in the last layer of the network can be simply trained at the same learning rate as weight parameters. Experiments show that FwSS is generally helpful in improving the generalization capability of both fully connected neural networks and deep convolutional neural networks, and FWSSNets achieve higher accuracies on UCI repository and CIFAR-10.
Hierarchical waste detection with weakly supervised segmentation in images from recycling plants

Reducing environmental pollution with household waste and emissions from the computing clusters is an urgent technological problem. In our work, we explore both of these aspects: the deep learning application to improve the efficiency of waste recognition on recycling plant's conveyor, as well as carbon dioxide emission from the computing devices used in this process. To conduct research, we developed an unique open WaRP dataset that demonstrates the best diversity among similar industrial datasets and contains more than 10,000 images with 28 different types of recyclable goods (bottles, glasses, card boards, cans, detergents, and canisters). Objects can overlap, be in poor lighting conditions, or significantly distorted. On the WaRP dataset, we study training and evaluation of cutting-edge deep neural networks for detection, classification and segmentation tasks. Additionally, we developed a hierarchical neural network approach called H-YC with weakly supervised waste segmentation. It provided a notable increase in the detection quality and made it possible to segment images, learning only having class labels, not their masks. Both the suggested hierarchical approach and the WaRP dataset have shown great industrial application potential.
When explainability turns into a threat - using xAI to fool a fake news detection method

The inclusion of Explainability of Artificial Intelligence (xAI) has become a mandatory requirement for designing and implementing reliable, interpretable and ethical AI solutions in numerous domains. xAI is now the subject of extensive research, from both the technical and social science perspectives. It is being received enthusiastically by legislative bodies and regular users of machine-learning-boosted applications alike. However, opening the black box of AI comes at a cost. This paper presents the results of the first study proving that xAI can enable successful adversarial attacks in the domain of fake news detection and lead to a decrease in AI security. We postulate the novel concept that xAI and security should strike a balance, especially in critical applications, such as fake news detection. An attack scheme against fake news detection methods is presented that employs an explainable solution. The described experiment demonstrates that the well-established SHAP explainer can be used to reshape the structure of the original message in such a way that the value of the model's prediction could be arbitrarily forced, whilst the meaning of the message stays the same. The paper presents various examples for which the SHAP values are used to point the adversary to the words and phrases that have to be changed to flip the label on the model prediction. To the best of the authors' knowledge, it has been the first research work to experimentally demonstrate the sinister side of xAI. As the generation and spreading of fake news has become a tool of modern warfare and a grave threat to democracy, the potential impact of explainable AI should be addressed as soon as possible.
RCPM_CFI: A regional core pattern mining method based on core feature influence

Regional co-location pattern mining is a branch of spatial co-location pattern mining, which is used to discover co-location patterns that prevalently co-occur in local regions. The regional co-location patterns can reveal the association relationships among spatial features in the local regions. However, in practice, the association relationship between certain features is asymmetrical, and some features play a key (core) role in the relationship, as central position. To explore the regional co-location patterns with core feature (called regional core patterns (RCPs)) as well as their prevalent regions, this paper presents a regional partition method based on the influence of core feature. Additionally, a regional core pattern mining (RCPM) algorithm is proposed to mine the RCPs and unveil their spatial distribution resulting from the arrangement of core feature. In the regional partition stage, we propose a partition criterion that takes into account the influence of core feature. This criterion aims to comprehensively address the spatial distribution relationship between core instances and their neighboring non-core instances, ensuring the partition process is rational and complete. In the RCPM stage, we propose a core-based nearest affiliation measure method to assess the neighbor relationship between core and non-core instances, which can consider the fact that there is competition between spatial instances of the same spatial feature. And, to effectively calculate pattern prevalence and quickly identify RCPs, we present a data structure called Core_Hash to store the influence relationship between core and non-core instances. Extensive experimental evaluations and analyses are conducted on synthetic and real-world datasets. Compared to the existing algorithms, our proposed algorithms yield more reasonable and comprehensive RCPs and demonstrate good efficiency and scalability.
Survey: Automatic generation of attack trees and attack graphs

Graphical security models constitute a well-known, user-friendly way to represent the security of a system. These classes of models are used by security experts to identify vulnerabilities and assess the security of a system. The manual construction of these models can be tedious, especially for large enterprises. Consequently, the research community is trying to address this issue by proposing methods for the automatic generation of such models. In this work, we present a survey illustrating the current status of the automatic generation of two popular kinds of graphical security models: Attack Trees and Attack Graphs. The goal of this survey is to present the current methodologies used in the field, compare them, and present the challenges and future directions to the research community.
BCD-Net: Stain Separation of Histological Images using Deep Variational Bayesian Blind Color Deconvolution

Histological images are often tainted with two or more stains to reveal their underlying structures. Blind Color Deconvolution (BCD) techniques separate colors (stains) and structural information (concentrations), which is useful for the processing, data augmentation, and classification of such images. Classical analytical BCD methods are typically computationally expensive in two distinct ways. First, estimating the colors and concentrations corresponding to a given image is a time-consuming process. Second, the entire estimation procedure must be performed independently for each image. In contrast, Deep Learning (DL) methods involve high training costs, but once trained, they are able to directly process unseen images. The application of DL to BCD has been limited by the absence of extensive databases containing ground truth color and concentrations. In this work, we propose BCD-Net, a deep variational Bayesian neural network for stain separation and concentration estimation. Under this framework, we address the challenge of lacking ground truth data by leveraging Bayesian modeling and inference techniques. We propose to use a prior distribution on the stain colors, and a simple flat prior on the concentrations. BCD-Net is trained by maximizing the evidence lower bound of the observed images. The loss function comprises two essential components: fidelity to the observed images and the Kullback-Leibler divergence between the estimated posterior distribution of colors and the selected prior. The model is trained, validated, and tested on two multicenter databases: Camelyon-17 and Warwick stain separation benchmark. The proposed approach is tested on image reconstruction, stain separation, and cancer classification. It performs well when contrasted with classical non-amortized methods and offers a substantial computational time advantage. This marks a significant step forward in the application of DL techniques to address BCD and paves the way for new approaches.
FAST: Enhancing Federated Learning Through Adaptive Data Sampling and Local Training

The emerging paradigm of federated learning (FL) strives to enable devices to cooperatively train models without exposing their raw data. In most cases, the data across devices are non-independently and identically distributed in FL. Thus, the local models trained over different data distributions will inevitably deviate from the global optima, which induces optimization inconsistency and even hurts global convergence. Moreover, the resource-constrained devices with heterogeneous training capacities ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">e.g.</i> , computing and communication) further slow down the convergence rate. To this end, we introduce an <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">F</u> L framework with <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">a</u> daptive data <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">s</u> ampling and local <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">t</u> raining, namely FAST. Specifically, even without devices' private data distributions, FAST enables each device to sample different rates of data points from each of its local classes to rebuild a dataset for training, thus adjusting the convergence direction of the aggregated global model to be closer to the global optima. The theoretical analysis shows that the convergence bound depends on the sampling rates as well as the number of local iterations executed on the sampled data. To achieve resource-effective and convergence-guaranteed FL, we then design an online learning algorithm that jointly optimizes the data sampling and local training strategies so as to encourage the decrease of global loss under the given time budget. Extensive experiments on physical and simulated environments show that, FAST improves the model accuracy by about 1.55%-6.78% given the same time budget, and accelerates training by about 1.39-5.89× with the same target accuracy, compared with the baselines.
Understanding the implementation issues when using deep learning frameworks

Deep Learning (DL) frameworks like TensorFlow can help developers implement DL applications (e.g., computer vision) faster and easier. When using DL frameworks, developers encountered a large number of questions and posted them on Stack Overflow (SO). The goal of this paper is to conduct a comprehensive empirical study on the SO questions, summarize the implementation issues, and suggest future opportunities. This paper focuses on three DL frameworks (i.e., TensorFlow, PyTorch, and Theano), groups 2,401 relevant SO questions into various implementation issues, and constructs a taxonomy. We also analyze the popularity and difficulty of these issues under the taxonomy. For the identified various implementation issues, we constructed a taxonomy consisting of seven major categories with 63 subcategories. Our analysis reveals that 91.7% of questions are related to the implementation categories of data processing, model setting, model training, and model prediction. Developers frequently address the remaining three categories (i.e., Model evaluation, runtime environment, and visualization), where runtime environment is the most difficult category. Based on empirical findings, we provide some suggestions for future research. In this paper, we summarized the issues of DL implementation and proposed corresponding opportunities for future study. We expect this paper to help developers and researchers understand these issues and design better tools to improve the productivity of DL implementation.
Collaborative software design and modeling in virtual reality

Context: Software engineering is becoming more and more distributed. Developers and other stakeholders are often located in different locations, departments, and countries and operating within different time zones. Most online software design and modeling tools are not adequate for distributed collaboration since they do not support awareness and lack features for effective communication. Objective: The aim of our research is to support distributed software design activities in Virtual Reality (VR). Method: Using design science research methodology, we design and evaluate a tool for collaborative design in VR. We evaluate the collaboration efficiency and recall of design information when using the VR software design environment compared to a non-VR software design environment. Moreover, we collect the perceptions and preferences of users to explore the opportunities and challenges that were incurred by using the VR software design environment. Results: We find that there is no significant difference in the efficiency and recall of design information when using the VR compared to the non-VR environment. Furthermore, we find that developers are more satisfied with collaboration in VR. Conclusion: The results of our research and similar studies show that working in VR is not yet faster or more efficient than working on standard desktops. It is very important to improve the interface in VR (gestures with haptics, keyboard and voice input), as confirmed by the difference in results between the first and second evaluation.
Universal adversarial backdoor attacks to fool vertical federated learning

Vertical federated learning (VFL) is a privacy-preserving distribution learning paradigm that enables participants, owning different features of the same sample space to train a machine learning model collaboratively while retaining their data locally. This paradigm facilitates improved efficiency and security for participants such as financial or medical fields, making VFL an essential component of data-driven Artificial Intelligence systems. Nevertheless, the partitioned structure of VFL can be exploited by adversaries to inject a backdoor, enabling them to manipulate the VFL predictions. In this paper, we aim to investigate the vulnerability of VFL in the context of binary classification tasks. To this end, we define a threat model for backdoor attacks in VFL and introduce a universal adversarial backdoor (UAB) attack to poison the predictions of VFL. The UAB attack, consisting of universal trigger generation and clean-label backdoor injection, is incorporated during the VFL training at specific iterations. This is achieved by alternately optimizing VFL sub-problems' universal trigger and model parameters. Our work distinguishes itself from existing studies on designing backdoor attacks for VFL, as those require the knowledge of auxiliary information that is not accessible within the split VFL architecture. In contrast, our approach does not require additional data to execute the attack. On the real-world datasets, our approach surpasses existing state-of-the-art methods, achieving up to 100% backdoor task performance while maintaining the main task performance. Our results in this paper make a major advance in revealing the hidden backdoor risks of VFL, hence paving the way for the future development of secure VFL. Our results in this paper make a major advance in revealing the hidden backdoor risks of VFL, hence paving the way for the future development of secure VFL applications such as finance.