Influence of cost/loss functions on classification rate: A comparative study across diverse classifiers and domains

Abstract: The classification rate is the most significant factor affecting the selection of an appropriate classification approach for achieving the desired quality of decisions. Several researchers have evaluated the influence of different features on the performance of classification approaches, but cost/loss functions, despite their theoretical impact on the classification rate, have been less considered in the comparative literature review. In this paper, a comparative study has been presented on the influence of different cost/loss functions on the classification rate of different classifiers. For this purpose, five of the most well-known and widely used categories of cost/loss functions and three of the most popular main categories of classification approaches are considered. Also, 23 benchmark datasets from six different domains are selected. In this paper, all modeling of the statistical and intelligent classifiers are run in the Eviews and MATLAB package software, respectively. In addition, all mathematical modeling of models run in the GAMS package software. Empirical results indicate that cost/loss functions can significantly affect the classification rate. Numerical results show that the discrete cost/loss function, by average 3.91% and 8.31% improvement rather than semi-continuous and continuous cost/loss functions, respectively, is the most effective one. This clearly illustrates that the consistency of the cost/loss function with the goal function of the classification approach has a positive direct relationship with the classification rate. Furthermore, nonlinear versions of semi-continuous and continuous cost/loss functions are on average slightly more efficient than linear ones. Numerical results indicate that nonlinear forms can averagely yield an 88.99% classification rate that is overall 0.98% higher than linear ones, which achieved an 88.13% classification rate. Although the type of cost/loss function can affect the classification rate of different classifiers, the improvement amount varies based on the type of classifier used. In general, the improvement amount in statistical classifiers, i.e., 15.07% is significantly higher than others, and intelligent by 7.86% and deep learning by 5.20% are in the second and third places, respectively. This overall demonstrates that the complexity of the used classifiers has a negative relationship with the improvement of the classification rate in changing cost/loss function. Finally, numerical results indicate that the difference between improvement amounts achieved by varying cost/loss functions, in general, is not significant and is independent of the type of classifier, as well as the type and domain/area of data.


A sentiment analysis method for COVID-19 network comments integrated with semantic concept

Abstract: 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

Abstract: 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

Abstract: 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

Abstract: 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.


Abstract: 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

Abstract: 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

Abstract: 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

Abstract: 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

Abstract: 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

Abstract: 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

Abstract: 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

Abstract: 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. , 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 L framework with <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">a daptive data <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">s ampling and local <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">t 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

Abstract: 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

Abstract: 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

Abstract: 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.


Managing the changing understanding of benefits in software initiatives

Abstract: When deciding to develop new software, that decision should be based on a clear understanding of the intended benefits the system can lead to. But what if the understanding of those benefits fluctuates. In this article, we investigate the understanding that stakeholders have or build of the benefits of the system under development, and what role that understanding plays. We conducted four studies: an exploratory study based on 22 interviews on nine projects that had explicit incentives to conduct benefits management, a confirmatory study based on a survey (n=110) of concepts of benefits understanding developed in the first study, and two explanatory studies arranged as focus groups (five participants in each) on issues that arouse during analysis of the first two studies. We find that it is common for stakeholders' understanding of benefits to change during and after development (reported by 96% of respondents from their latest digitalization efforts). Predicting and managing the changes to understanding of benefits is not straightforward. This leads to uncertainty, both for investment decisions and how to manage benefits. We conclude that, rather than focusing on cookbook recipes for benefits management, more emphasis should be focused on helping practitioners embrace changes to understanding of benefits.


Evolutionary multi-objective design of autoencoders for compact representation of histopathology whole slide images

Abstract: The recent success of deep learning coincides with the surge of digital pathology as an effect of the pandemic. However, processing a massive volume of gigapixel histopathology images is daunting. Hence, establishing compact image representations for efficient computational pathology becomes paramount. Deep autoencoders have been frequently used to compress feature vectors extracted from pre-trained networks. However, the topology of autoencoders is generally set heuristically. In this paper, we propose a multi-objective evolutionary framework as a bi-level optimization scheme in which the outer level evolves autoencoders for the goal of dimension reduction while the inner level is to optimize their weights for training. Throughout the evolution process, multiple minimization objectives such as complexity, classification error, and code size are considered. These objectives can obtain compressed models to generate a very small set of deep features with the highest classification accuracy. We use images from The Cancer Genome Atlas (TCGA) repository provided by the National Cancer Institute to train and validate our optimization framework. We increased the classification accuracy by 8% while the image representation is 46,000 times shorter. We apply the framework to demonstrate higher accuracy, substantial memory reduction and faster processing for image retrieval compared to SOTA.


Dual-DIANet: A sharing-learnable multi-task network based on dense information aggregation

Abstract: Designing neural networks that balance shared features and task-specific ones is a major challenge in multi-task learning. To solve this issue, we aggregate information from both multi-scale and multi-level perspectives for a more comprehensive understanding of the multi-task features, which enables the network to learn a better sharing way. Specifically, we first introduce a basic Multi-scale-focused Dense Information Aggregation Network, where learnable fusion modules are used to connect two task-biased subnets and control the feature sharing. The fusion modules, consisting of densely cascaded dilated convolutions and scale-wise attention blocks, adaptively aggregate multi-scale information for each task. To further exploit multi-level information, the modules at different levels are mutually connected in a dense manner and guided by auxiliary supervision. By combining these two aspects of information aggregation, a Dual Dense Information Aggregation Network with a strong ability to learn appropriate sharing is finally proposed. Comprehensive experiments are reported on NYUDv2, SUN RGB-D, and Mini-Taskonomy to show the effectiveness of our method.


A markless 3D human motion data acquisition method based on the binocular stereo vision and lightweight open pose algorithm

Abstract: This study aimed to achieve precise 3D human motion data acquisition without the need for markers. Specifically, the proposed system utilized two affordable cameras to capture human motion from diverse angles. It developed a lightweight Open Pose algorithm to extract 2D human body joints from recorded videos. Subsequently, a stereo-matching method was applied to derive accurate 3D human body joint data. Comprehensive experiments with 10 healthy participants showcased the system's exceptional performance, demonstrating superior measurement accuracy compared to established standards like the NOKOV system (RMSE values consistently below 9°) and commonly used Microsoft Kinect devices (V2 and Azure). This innovative binocular vision system presents a new approach to motion capture, potentially offering a cost-effective and precise tool for practical applications across various scenarios (e.g., motor functional assessment, virtual avatar streaming), contributing significantly to both scientific and academic communities.


Attention driven CWT-deep learning approach for discrimination of Radar PRI modulation

Abstract: With the proliferation of radio frequency (RF) systems and radar applications, Electronic Warfare (EW) is receiving increasing importance. The analysis of the radar signals is a critical EW task that decides the nature of counter employments. In an Electronic Support (ES) system, the challenge is to detect hostile radiation sources efficiently and trigger a counter response. Detection of types of Pulse Repetition Interval (PRI) modulation of radar signal significantly facilitates the manifestation of RF emitters during recognition which is difficult in a dense EW environment. Recent developments in artificial intelligence (AI) methods suggest that this emerging technology can be effective for such purposes. In this direction, an automatic approach for recognizing several kinds of complex PRI modulation based on Continuous Wavelet Transform (CWT) and a combination of the vanilla Convolutional Neural Network (CNN), a multi-head self-attention (MHSA) mechanism and the popular Long Short-Term Memory (LSTM) is proposed. The CWT is used to decompose the PRI modulation sequence and obtain different time–frequency components. Further, aided by the proposed CNN-MHSA-LSTM combination, the features extracted from the CWT 2D-scalograms are used to execute PRI modulation discrimination. In this method, the vanilla CNN is employed for the extraction of deep features to figure out the class details while capturing the spatial attributes. Thereafter, to improve the discriminative power of the entire framework a MHSA mechanism is used. The temporal attributes are acquired by the LSTM which works in concert with the CNN for executing the detection of the PRI classes based on the extracted features. Also to assess the effectiveness of the proposed method, three models based on ResNet, popular CNN and SqueezeNet are implemented for benchmark comparison in terms of overall performance and complexity. The simulation results show that the proposed method enhances performance and achieves robustness in the noise-filled and imperfect channel knowledge environment. The best recognition accuracy is 98.3% with 50% spurious pulses in the environment which fluctuates with imperfect channel knowledge cases.


Enhanced radial base deep learning algorithm for predicting multimedia security issues

Abstract: From online social networks to life-or-death security systems, multimedia files (photos, movies, and audio recordings) have grown common in today’s digital culture. Protecting people, businesses and infrastructure requires strict adherence to the encryption and decryption of multimedia data. We suggested an Ensemble Whale Optimized Recurrent Neural Network (EWO-RNN)used in this study to overcome these issues. With the help of this study, multimedia security will be evaluated in more accurate and comprehensive manner. Smarter decisions and proactive security measures may follow as a result of this. To increase the system quality and the overall performance, the collected data is pre-processed for normalized data by using Min-Max Normalization. Pre-processed data is extracted by using Kernel Principle Component Analysis (K-PCA). The EWO-RNN evaluates the effectiveness and efficiency of an approach by analyzing the performance of Accuracy (97.85%), Precision (92.2%), F1-score (96.1%), Mean Square Error (MSE) (0.086), Root Mean Square (RMSE) (0.12%) and Sensitivity (95%). The Enhanced Radial Base Deep Learning Algorithm for Predicting Multimedia Security Issues proposes a solution with improved resilience, accuracy, generalization, and decision-making capabilities. In a dynamic and evolving digital environment, this makes the algorithm a viable tool for multimedia security assessments.


Automated code-based test case reuse for software product line testing

Abstract: A software product line (SPL) grows in size as a new product is developed. A new product in an SPL should be tested extensively for quality assurance. For the efficient testing, previous studies suggested reusing the existing test cases of a product family. However, either their methods were not efficient because interventions from human experts, specifications, architecture and/or traceabilities for test cases were required. To address these limitations, we propose an Automated Code-based Test case reuse for SPLs (ActSPL). ActSPL automatically identifies reusable test cases for new products of a product family using source code and test cases. ActSPL automatically constructs a hash-based traceability links between test cases and source code of a product family. Using the traceability links, ActSPL selects reusable test cases for a given new product from existing test cases of the product family. We evaluated ActSPL in terms of the effectiveness and cost reduction of reusing test cases with five open-source SPLs. The evaluation results showed that ActSPL, on average, achieved 100 % precision and 62 % recall. In addition, ActSPL, on average, saved 47.5 % of time required for testing a new product from scratch. Our study shows the feasibility of ActSPL reusing SPL test cases based on source code and test cases. Our results can be a basis for successive studies for automated code-based SPL testing.


Pixel-wise annotated and high-quality synthesized image datasets for semi-supervised truck segmentation with limited raw images

Abstract: Deep-learning-based segmentation methods have emerged as effective paradigms for intelligent monitoring, such as the loading status and locations of trucks at construction sites. However, the time-consuming and labor-intensive data preparation hinders the implementation of these methods. We propose a novel semi-supervised method to automatically synthesize high-quality and pixelwise annotated datasets for trucks using limited raw images. Ada-StyleGAN2 is initially employed to synthesize high-quality images from limited raw images. Subsequently, a label generator derived from DatasetGAN is integrated with atrous spatial pyramid pooling (ASPP) and a positional embedding module to generate pixel-level masks. Finally, a "crop and paste" module is proposed to address the scale variations. With only 5000 raw images and 18 annotated samples, the method can synthesize high-quality and pixelwise annotated images with a Fréchet inception distance (FID) of 5.40 in 30 min. Other annotations, such as pose estimation and activity recognition, have potential for future research.


Differential privacy scheme using Laplace mechanism and statistical method computation in deep neural network for privacy preservation

Abstract: Mountainous amounts of information are now available in hospitals, finance, counter-terrorism, education and many other sectors. Those information can offer a rich source for analysis and decision making. Such information contains user's sensitive and personal data as well. This emanates direct conflict with the user's privacy. Individual's privacy is their right. The existing privacy preserving algorithms works mainly on the numerical data and doesn't care about the categorical data. In addition, there is a heavy trade-off between privacy preservation and data utility. To overcome these issues, a deep neural network - statistical differential privacy (DNN−SDP) algorithm is proposed as the solution to disguise the individual's private and sensitive data. Both the numerical and categorical based human-specific data are considered and fed to the input layer of the neural network. The statistical methods weight of evidence and information value is applied in the hidden layer along with the random weight (wi) to get the initial perturbed data. This initially perturbed data is taken by Laplace computation based differential privacy mechanism as the input and provides the final perturbed data. Census income, bank marketing and heart disease datasets are used for experimentation. While comparing with the state-of-the-art methods, DNN−SDP algorithm provides 97.4% of accuracy with 98.2% of precision, 99% of recall rate and 98.7% of F-measure value. In addition, the fall-out rate, miss rate and false omission rate of the proposed algorithm are less than 4.1%. The DNN−SDP algorithm guarantees the privacy preservation along with data utility.


Highly Efficient Self-Checking Matrix Multiplication on Tiled AMX Accelerators

Abstract: General Matrix Multiplication (GEMM) is a computationally expensive operation that is used in many applications such as machine learning. Hardware accelerators are increasingly popular for speeding up GEMM computation, with Tiled Matrix Multiplication (TMUL) in recent Intel processors being an example. Unfortunately, the TMUL hardware is susceptible to errors, necessitating online error detection. The Algorithm-based Error Detection (ABED) technique is a powerful technique to detect errors in matrix multiplications. In this article, we consider implementation of an ABED technique that integrates seamlessly with the TMUL hardware to minimize performance overhead. Unfortunately, rounding errors introduced by floating-point operations do not allow a straightforward implementation of ABED in TMUL. Previously an error bound was considered for addressing rounding errors in ABED. If the error detection threshold is set too low, it will a trigger false alarm, while a loose bound will allow errors to escape detection. In this article, we propose an adaptive error threshold that takes into account the TMUL input values to address the problem of false triggers and error escapes and provide a taxonomy of various error classes. This threshold is obtained from theoretical error analysis but is not easy to implement in hardware. Consequently, we relax the threshold such that it can be easily computed in hardware. While ABED ensures error-free computation, it does not guarantee full coverage of all hardware faults. To address this problem, we propose an algorithmic pattern generation technique to ensure full coverage for all hardware faults. To evaluate the benefits of our proposed solution, we conducted fault injection experiments and show that our approach does not produce any false alarms or detection escapes for observable errors. We conducted additional fault injection experiments on a Deep Neural Network (DNN) model and find that if a fault is not detected, it does not cause any misclassification.


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Abstract: This note studies the problem of estimating frequencies of items over data streams. We propose a simple streaming algorithm for the problem in small space complexity. Our algorithm is a counter-based algorithm with the aid of probabilistic counting. We show that our algorithm with k counters computes, with probability at least 1−δ, the estimation with relative error at most (1+ε)N/k, taking O(kloglogNk+klog(ε−1δ−1k)+klogℓ) space in expectation, where N is the total number of items and ℓ is the number of different items.


OneSpace: Detecting cross-language clones by learning a common embedding space

Abstract: Identifying clone code fragments across different languages can enhance the productivity of software developers in several ways. However, the clone detection task is often studied in the context of a single language and less explored for code snippets spanning different languages. In this paper, we present OneSpace, a new cross-language clone detection approach. OneSpace projects different programming languages to the same embedding space using both code and API data. OneSpace, hence, leverages a Siamese Network to infer the similarity of the embedded programs. We evaluate OneSpace by detecting clones across three language pairs; JAVA-Python, Java-C++ and Java-C. We compared OneSpace with the other state-of-art techniques, SupLearn and CLCDSA. In our evaluation, OneSpace provided higher effectiveness than the state of the art. Our ablation study validated some of our intuitions in designing OneSpace, particularly that using a single embedding space (as opposed to separate ones) provides higher effectiveness. Additionally, we designed a variant of OneSpace that uses Word-Mover-Distance Algorithm and provides lower effectiveness, but is much more efficient. We also found that OneSpace provides higher effectiveness than the state of the art, even for: complex implementations, single-method implementations, varying ratios of positive to negative clones in training, varying amounts of training data, and for additional programming languages.


Harmonizing DevOps taxonomies — A grounded theory study

Abstract: DevOps responds to the growing need of companies to streamline the software development process and thus has experienced widespread adoption in the past few years. However, the successful adoption of DevOps requires companies to address important cultural and organizational changes. Nevertheless, it is crucial to recognize that various DevOps taxonomies exist, both from academic and practitioner perspectives, which may lead to misleading or failed adoption of DevOps. This paper presents empirical research on the structure of DevOps teams in software-producing organizations. The goal is to better understand the organizational structure and characteristics of teams adopting DevOps by harmonizing the existing knowledge. To achieve this, we employed a grounded theory approach with collaborative coding, involving two research groups. Inter-Coder Agreement (ICA) was utilized to guide the discussion rounds. We conducted a comprehensive analysis of existing studies on DevOps teams and taxonomies to gain a deeper understanding of the subject. From the analysis, we built a substantive and analytic theory of DevOps taxonomies. The theory is substantive in that the scope of validity refers to the ten secondary studies processed and analytic in that it analyzes “what is” rather than explaining causality or attempting predictive generalizations. A public repository with all the data related to the products resulting from the analysis and generation of the theory is available. We built a theory on DevOps taxonomies and tested whether it harmonizes the existing taxonomies, i.e., whether our theory can instantiate the others. This is the first step to define which taxonomies are best suited to approach DevOps culture and practices according to the companies’ objectives and capabilities. Editor’s note: Open Science material was validated by the Journal of Systems and Software Open Science Board.


Improved generative adversarial network with deep metric learning for missing data imputation

Abstract: Incomplete data are ubiquitous in real-world computer vision tasks. Imputing missing data is crucial for modeling machine learning algorithms. Although existing methods, such as MisGAN, have achieved considerable success, two problems remain: (1) generating a complete image from random noise weakens the model's imputation capability; (2) owing to the influence of random noise, the training of the generator is unstable. Therefore, this study proposes a novel approach that uses deep metric learning to enable MisGAN to perform multi-tasking missing data imputation. First, an image feature extraction network is applied to extract the semantic representation of the images. Then, deep metric learning is performed to learn good features embedding by maximizing inter-class variation and minimizing intra-class variation. Such an embedding replaces the random noise and is fed into MisGAN to obtain an improved generated complete image. Several experiments are conducted on datasets, demonstrating that the proposed method can significantly outperform baseline methods.


Features based adaptive augmentation for graph contrastive learning

Abstract: Self-supervised learning aims to eliminate the need for expensive annotation in graph representation learning, where graph contrastive learning (GCL) is trained with the self-supervision signals containing data-data pairs. These data-data pairs in GCL are generated with augmentation employing stochastic functions on the original graph. We argue that some features can be more critical than others depending on the downstream task, and applying stochastic function uniformly vandalizes the influential features, leading to diminished accuracy. To fix this issue, we introduce a Feature Based Adaptive Augmentation (FebAA) approach, which identifies and preserves potentially influential features and corrupts the remaining ones. We implement FebAA as a plug-and-play layer and use it with state-of-the-art Deep Graph Contrastive Representation Learning (GRACE) and Large-Scale Representation Learning on Graphs via Bootstrapping (BGRL). We successfully improved the accuracy of GRACE and BGRL on eight graph representation learning benchmark datasets.


Multi-layered self-attention mechanism for weakly supervised semantic segmentation

Abstract: Weakly Supervised Semantic Segmentation (WSSS) provides efficient solutions for semantic image segmentation using image-level annotations. WSSS requires no pixel-level labeling that Fully Supervised Semantic Segmentation (FSSS) does, which is time-consuming and label-intensive. Most WSSS approaches have leveraged Class Activation Maps (CAM) or Self-Attention (SA) to generate pseudo pixel-level annotations to perform semantic segmentation tasks coupled with fully supervised approaches (e.g., Fully Convolutional Network). However, those approaches often provides incomplete supervision that mainly includes discriminative regions from the last convolutional layer. They may fail to capture regions of low- or intermediate-level features that may not be present in the last convolutional layer. To address the issue, we proposed a novel Multi-layered Self-Attention (Multi-SA) method that applies a self-attention module to multiple convolutional layers, and then stack feature maps from the self-attention layers to generate pseudo pixel-level annotations. We demonstrated that integrated feature maps from multiple self-attention layers produce higher coverage in semantic segmentation than using only the last convolutional layer through intensive experiments using standard benchmark datasets.


Revealing code change propagation channels by evolution history mining

Abstract: Changes on source code may propagate to distant code entities through various kinds of relationships, which may form up change propagation channels. It is however difficult for developers to reveal code change propagate channels due to sophisticated interrelationships among code entities. In this work, we propose a novel graph representation for the changed code entities and related code entities changed within a range of space and time so that the types of relationships along which the changes are propagated can be explicitly presented. Then a subgraph mining technique is used to find the frequent change propagation channels. We finally reveal 40 types of frequent change propagation channels that cover over 98% cases of code change propagation in five well-known open-source Java projects. We find evidence that the code changes propagated through an unchanged intermediate code entity consume more time than those through a changed one, indicating the difficulties in maintaining code entities that related through indirect relationships. We find that a small proportion of code entities frequently appear in the FCPCs, and confirm the semantic relationships between code entities covered by 50 instances of FCPCs, indicating potential usefulness for developers to explain the range of change impact from given source code changes.


Powerful-IoU: More straightforward and faster bounding box regression loss with a nonmonotonic focusing mechanism

Abstract: Bounding box regression (BBR) is one of the core tasks in object detection, and the BBR loss function significantly impacts its performance. However, we have observed that existing IoU-based loss functions suffer from unreasonable penalty factors, leading to anchor boxes expanding during regression and significantly slowing down convergence. To address this issue, we intensively analyzed the reasons for anchor box enlargement. In response, we propose a Powerful-IoU (PIoU) loss function, which combines a target size-adaptive penalty factor and a gradient-adjusting function based on anchor box quality. The PIoU loss guides anchor boxes to regress along efficient paths, resulting in faster convergence than existing IoU-based losses. Additionally, we investigate the focusing mechanism and introduce a non-monotonic attention layer that was combined with PIoU to obtain a new loss function PIoU v2. PIoU v2 loss enhances the capability to focus on anchor boxes of medium quality. By incorporating PIoU v2 into popular object detectors such as YOLOv8 and DINO, we achieved an increase in average precision (AP) and improved performance compared to their original loss functions on the MS COCO and PASCAL VOC datasets, thus validating the effectiveness of our proposed improvement strategies.


AMAD: Active learning-based multivariate time series anomaly detection for large-scale IT systems

Abstract: Multivariate time series anomaly detection on key performance indicators helps mitigate the impact of large-scale IT system anomalies. Due to the large volume and the abstract nature of multivariate time series, previous works have tended to make overly strict or optimistic hypotheses on labeling costs and resulted in unsatisfactory results. Thus, it remains a challenge to make an appropriate trade-off between labeling costs and model performance. This research proposes AMAD, an active learning-based approach that works to address this problem. Its core idea is to provide the learner model high-value label queries via an ensemble query strategy, which dynamically adapts to the estimated anomaly ratio and the ever-changing model performance. Moreover, it is the first to discuss in detail the margin effect of query strategies on model performance, to our knowledge, this has not been investigated in previous works on time series anomaly detection. Extensive experiments on five public datasets demonstrate that AMAD works well and robustly on various real-world scenarios, which outperforms state-of-the-art baseline methods by 16% and 11% in terms of recall and F1, with only 3% of data being labeled.


Masked autoencoders with handcrafted feature predictions: Transformer for weakly supervised esophageal cancer classification

Abstract: Esophageal cancer is a serious disease with a high prevalence in Eastern Asia. Histopathology tissue analysis stands as the gold standard in diagnosing esophageal cancer. In recent years, there has been a shift towards digitizing histopathological images into whole slide images (WSIs), progressively integrating them into cancer diagnostics. However, the gigapixel sizes of WSIs present significant storage and processing challenges, and they often lack localized annotations. To address this issue, multi-instance learning (MIL) has been introduced for WSI classification, utilizing weakly supervised learning for diagnosis analysis. By applying the principles of MIL to WSI analysis, it is possible to reduce the workload of pathologists by facilitating the generation of localized annotations. Nevertheless, the approach's effectiveness is hindered by the traditional simple aggregation operation and the domain shift resulting from the prevalent use of convolutional feature extractors pretrained on ImageNet.We propose a MIL-based framework for WSI analysis and cancer classification. Concurrently, we introduce employing self-supervised learning, which obviates the need for manual annotation and demonstrates versatility in various tasks, to pretrain feature extractors. This method enhances the extraction of representative features from esophageal WSI for MIL, ensuring more robust and accurate performance.We build a comprehensive dataset of whole esophageal slide images and conduct extensive experiments utilizing this dataset. The performance on our dataset demonstrates the efficiency of our proposed MIL framework and the pretraining process, with our framework outperforming existing methods, achieving an accuracy of 93.07% and AUC (area under the curve) of 95.31%.This work proposes an effective MIL method to classify WSI of esophageal cancer. The promising results indicate that our cancer classification framework holds great potential in promoting the automatic whole esophageal slide image analysis.


Hedging inflation expectations in the cryptocurrency futures market

Abstract: This paper finds the first evidence of time variation in the relationship between inflation expectations and the price of cryptocurrency futures. Daily data on the futures markets of Bitcoin—starting in December 2017—and Ethereum—available since February 2021—reveal responses to inflation expectations that are consistently positive across both measures in a full sample encompassing 2022. These results hold for a sample that precedes the Luna crash in May 13, 2022. However, the response turns negative in the period between the failures of the Luna and FTX crypto exchanges. We find cryptocurrency futures provide an effective hedge against inflation expectations and may provide a hedge against idiosyncratic market risk if the ensuing uncertainty is embraced by traders leading them to search-for-yield behavior. Risk that is more systemic—and not properly digested by financial markets—may lead futures contract holders to exit their positions ahead of expiration, leading to a bid down of futures prices and an erosion of their hedging ability. This may have contributed to the turbulence in cryptocurrencies experienced during the latter part of 2022.


Dendrogram-based Heterogeneous Learners for Automatic Modulation Classification in DSTBC-OFDM Systems

Abstract: Software-Defined Radios (SDRs) have a crucial role in dynamic spectrum sensing and sharing as they can quickly adapt to changes in their environment. Digital Modulation Recognition (DMR) enables this process by giving SDR the ability to recognize the received signal modulation format to make intelligent and autonomous decisions to switch on the optimal modulation scheme. However, with the deployment of the Distributed Space-Time Block Coding Orthogonal Frequency Division Multiplexing (DSTBC-OFDM) signals that support MIMO systems, that provides spatio-temporel multiplexing, new and challenging signal identification problems have emerged. This paper proposes a robust DMR method for relaying Space-Time Block Coding (STBC) Orthogonal Frequency Division Multiplexing (OFDM) systems. The proposed classifier is built using heterogeneous base classifiers namely, Support Vector Machine (SVM), K-Nearest Neighbors (KNN), and Random Forest (RFC), based on a Dendrogram or decision tree structure. The Dendrogram-based Heterogeneous Learners (DHL) classifier were obtained via Ascendant Hierarchical Clustering (AHC) and by employing Higher-Order Moment (HOMs) and Higher-Order Cumulants (HOCs) as features. Moreover, we propose a grid search-based hyperparameter tuning for DHL to produce the best structure and optimize the performance.


Neighborhood preserving embedding with autoencoder

Abstract: Neighborhood preserving embedding (NPE) is a classical method for dimensionality reduction (DR), and it is a linear version of the locally linear embedding method. However, NPE and all its variants only consider the one-way mapping from high-dimensional space to low-dimensional space. The projected low-dimensional data may not accurately and effectively "represent" the original samples. To address this problem, we improve NPE based on linear autoencoder. The conventional projection of NPE is considered as the encoding stage, and the decoder stage is a reconstruction from the low-dimensional space to the original high-dimensional space, which is the key to maintaining more significant information. Based on this, we propose a new NPE method called NPEAE (neighborhood preserving embedding with autoencoder) in this paper. NPEAE performs excellently in face recognition, handwritten character categorization, object classification, etc. The experiments on MNIST, COIL-20, the Extended Yale B, Olivetti Research Laboratory (ORL), and FERET show that NPEAE has a higher recognition accuracy than other comparative methods.


A comprehensive examination of email spoofing: Issues and prospects for email security

Abstract: Attackers are becoming more skilled in recent years, using sophisticated technology to produce look-alike emails that make it difficult to distinguish between real and fake ones. Most false emails can be detected, but certain undiscovered ones can be dangerous and compromise security. The attacker compromises SMTP to launch an email spoofing attack. This is not difficult given that it was designed without any security safeguards. Spoofers typically exploit the various fields in email headers. By taking advantage of loopholes in email security systems, attackers can create an ideal spoofing mail. As a result, it appears as a reliable source and succeeds in phishing attempts. An in-depth analysis of the email process, its protocols, and authentication mechanisms along with the security measures and adoption rates that led to a variety of spoofing attacks has been examined in our work. Our experiments on renowned mail service suppliers observed that some of them are still vulnerable to associated flaws. Further, we analyzed how different aspects such as age and education, determine whether or not a message is spoofed, and how malware uses email as a command and control to compromise the victim's device and seize control of it. Further, it offers a multitude of mitigation strategies against spoofing attempts that aid aspirants in future research.


Fuzzy order-sorted feature logic

Abstract: Order-Sorted Feature (OSF) logic is a knowledge representation and reasoning language based on function-denoting feature symbols and set-denoting sort symbols ordered in a subsumption lattice. OSF logic allows the construction of record-like terms that represent classes of entities and that are themselves ordered in a subsumption relation. The unification algorithm for such structures provides an efficient calculus of type subsumption, which has been applied in computational linguistics and implemented in constraint logic programming languages such as LOGIN and LIFE and automated reasoners such as CEDAR. This work generalizes OSF logic to a fuzzy setting. We give a flexible definition of a fuzzy subsumption relation which generalizes Zadeh's inclusion between fuzzy sets. Based on this definition we define a fuzzy semantics of OSF logic where sort symbols and OSF terms denote fuzzy sets. We extend the subsumption relation to OSF terms and prove that it constitutes a fuzzy partial order with the property that two OSF terms are subsumed by one another in the crisp sense if and only if their subsumption degree is greater than 0. We show how to find the greatest lower bound of two OSF terms by unifying them and how to compute the subsumption degree between two OSF terms, and we provide the complexity of these operations.


Diagnosis of Osteosarcoma Based on Multimodal Microscopic Imaging and Deep Learning

Abstract: Osteosarcoma is the most common primary bone tumor with high malignancy. It is particularly necessary to achieve rapid and accurate diagnosis in its intraoperative examination and early diagnosis. Accordingly, the multimodal microscopic imaging diagnosis system constructed by bright field, spontaneous fluorescence and polarized light microscopic imaging was used to study the pathological mechanism of osteosarcoma from the tissue microenvironment level and achieve rapid and accurate diagnosis. First, the multimodal microscopic images of normal and osteosarcoma tissue slices were collected to characterize the overall morphology of the tissue microenvironment of the samples, the arrangement structure of collagen fibers and the content and distribution of endogenous fluorescent substances. Second, based on the correlation and complementarity of the feature information contained in the three single-mode images, combined with convolutional neural network (CNN) and image fusion methods, a multimodal intelligent diagnosis model was constructed to effectively improve the information utilization and diagnosis accuracy. The accuracy and true positivity of the multimodal diagnostic model were significantly improved to 0.8495 and 0.9412, respectively, compared to those of the single-modal models. Besides, the difference of tissue microenvironments before and after cancerization can be used as a basis for cancer diagnosis, and the information extraction and intelligent diagnosis of osteosarcoma tissue can be achieved by using multimodal microscopic imaging technology combined with deep learning, which significantly promoted the application of tissue microenvironment in pathological examination. This diagnostic system relies on its advantages of simple operation, high efficiency and accuracy and high cost-effectiveness, and has enormous clinical application potential and research significance.


Graph embedding-based heterogeneous domain adaptation with domain-invariant feature learning and distributional order preserving

Abstract: Heterogeneous domain adaptation (HDA) methods leverage prior knowledge from the source domain to train models for the target domain and address the differences in their feature spaces. However, incorrect alignment of categories and distribution structure disruption may be caused by unlabeled target samples during the domain alignment process for most existing methods, resulting in negative transfer. Additionally, the previous works rarely focus on the robustness and interpretability of the model. To address these issues, we propose a novel Graph embedding-based Heterogeneous domain-Invariant feature learning and Distributional order preserving framework (GHID). Specifically, a bidirectional robust cross-domain alignment graph embedding structure is proposed to globally align two domains, which learns the domain-invariant and discriminative features simultaneously. In addition, the interpretability of the proposed graph structures is demonstrated through two theoretical analyses, which can elucidate the correlation between important samples from a global perspective in heterogeneous domain alignment scenarios. Then, a heterogeneous discriminative distributional order preserving graph embedding structure is designed to preserve the original distribution relationship of each domain to prevent negative transfer. Moreover, the dynamic centroid strategy is incorporated into the graph structures to improve the robustness of the model. Comprehensive experimental results on four benchmarks demonstrate that the proposed method outperforms other state-of-the-art approaches in effectiveness.


Exploring the limitations in how ChatGPT introduces environmental justice issues in the United States: A case study of 3,108 counties

Abstract: The potential of Generative AI, such as ChatGPT, has sparked discussions among researchers and the public. This study empirically explores the capabilities and limitations of ChatGPT, specifically its portrayal of environmental justice issues. Using OpenAI's ChatGPT API, we asked ChatGPT (GPT-4) to answer questions about environmental justice issues in 3,108 counties in the contiguous United States. Our findings suggest that ChatGPT provides a general overview of environmental justice issues. Consistent with research, ChatGPT appears to acknowledge the disproportionate distribution of environmental pollutants and toxic materials in low-income communities and those inhabited by people of color. However, our results also highlighted ChatGPT's shortcomings in detailing specific local environmental justice issues, particularly in disadvantaged (e.g., rural and low-income) counties. For instance, ChatGPT could not provide information on local-specific environmental justice issues for 2,593 of 3,108 counties (83%). The results of the binary logistic regression model revealed that counties with lower population densities, higher percentages of white population, and lower incomes are less likely to receive local-specific responses from the ChatGPT. This could indicate a potential regional disparity in the volume and quality of training data, hinting at geographical biases. Our findings offer insights and implications for educators, researchers, and AI developers.


Multi-branch-feature fusion super-resolution network

Abstract: Deep network attracts extensive attention in the single image super-resolution field. Most of the deep network-based super-resolution methods usually employ a feature extraction stream to generate the input for the next extraction module during extracting deep features. Whereas, many experiments show that the feature maps yielded by different feature extraction streams generally contain different but complementary detailed information. Based on this observation, we propose a multi-branch feature fusion super-resolution network termed as MBFSR to solve the single image super-resolution task. MBFSR progressively deploys multiple feature extraction modules to extract the deep feature. Specifically, the deployed feature extraction modules first generate different feature maps through three well-designed feature extraction branches, and then fuse them by the developed three-branch fusion module to generate a high-resolution feature map. Finally, the output features of all modules are directly used to generate the desired high-resolution image. Experimental results demonstrate the availability and superiority of MBFSR over other popular methods.


Commit-time defect prediction using one-class classification

Abstract: Existing Just-In-Time Software Defect Prediction methods suffer from the data imbalance problem, where the majority class (normal commits) significantly outnumbers the minority class (buggy commits). This results in a higher probability of misclassification. Various data balancing techniques have been proposed to address this challenge with varying degrees of success. In this study, we propose an approach that rely on One-Class Classification (OCC) to train models using data from the majority class only. This eliminates the need for data balancing. We compare the accuracy of three OCC algorithms - One-class SVM, Isolation Forest, and One-class k-NN - to their binary counterparts - SVM, Random Forest, and k-NN - on 34 software projects. Our results show that the data imbalance ratio (the proportion of normal to buggy commits) plays a crucial role in determining the optimal classification approach. We found that for projects with medium to high imbalance ratio, OCC algorithms outperform binary classifiers with and without data balancing, using cross and time-sensitive validation approaches. Furthermore, we found that OCC methods require fewer features for projects with medium to high IR, reducing the computational overhead of training and response time while providing a better understanding of the data and algorithm behavior.


A probabilistic analysis of selected notions of iterated conditioning under coherence

Abstract: It is well known that basic conditionals satisfy some desirable basic logical and probabilistic properties, such as the compound probability theorem. However checking the validity of these becomes trickier when we switch to compound and iterated conditionals. Herein we consider de Finetti's notion of conditional both in terms of a three-valued object and as a conditional random quantity in the betting framework. We begin by recalling the notions of conjunction and disjunction among conditionals in selected trivalent logics. Then we analyze the notions of iterated conditioning in the frameworks of the specific three-valued logics introduced by Cooper-Calabrese, by de Finetti, and by Farrel. By computing some probability propagation rules we show that the compound probability theorem and other important properties are not always preserved by these formulations. Then, for each trivalent logic we introduce an iterated conditional as a suitable random quantity which satisfies the compound prevision theorem as well as some other desirable properties. We also check the validity of two generalized versions of Bayes' Rule for iterated conditionals. We study the p-validity of generalized versions of Modus Ponens and two-premise centering for iterated conditionals. Finally, we observe that all the basic properties are satisfied within the framework of iterated conditioning followed in recent papers by Gilio and Sanfilippo in the setting of conditional random quantities.


Inferentialism

Abstract: Abstract This chapter introduces inferentialism, focusing on its application to the logical domain. It highlights the main attraction of inferentialism, namely that it grounds meaning in our inferential practices and distinguishes between semantic and meta-semantic theses associated with inferentialism. The chapter goes on to present three problems inferentialism faces: the Problem of Defective Concepts, the Problem of Constitutive Rules, and the Problem of Limited Applicability. It is argued that while inferentialists already have in house the resources to address the first two problems, they must join forces with expressivists in order to address the third problem. Moreover, joining forces with expressivists enables inferentialists to solve the first problem whilst allowing them to hold on to a classical notion of inference.


Enhanced Convolutional Neural Network for solar radiation nowcasting: All-Sky camera infrared images embedded with exogeneous parameters

Abstract: Electrical power production by renewable energy sources is unpredictable in nature and this may cause imbalance between power generation and demand. Therefore, an accurate prediction of solar radiation is crucial for the stability and efficient management of electric grid. This study focuses on very short-term forecasts of solar radiation with a horizon in the range of 5-15 min. In this paper, a Convolutional Neural Network is proposed that uses sequences of infrared images captured by an All-Sky Imager to forecast the Global Horizontal Irradiance on different time horizon. A real case study, exploiting six months of high-resolution data, is analysed. Additionally, an innovative technique, the Enhanced Convolutional Neural Network (ECNN), is proposed in which exogenous data, as the solar radiation measurement, is encoded in terms of coloured pixels in the upper corner of the images. Considering the naïve persistence method as a baseline, a clear improvement across the key metrics has been noted with the proposed methodology. A deeper analysis of the results reveals that the proposed models are more accurate than persistence when high fluctuations of solar radiation are experienced. In that case, the ECNN achieves a forecast skill exceeding 19% for all the tested forecast horizons.


Corner-to-Center long-range context model for efficient learned image compression

Abstract: In the framework of learned image compression, the context model plays a pivotal role in capturing the dependencies among latent representations. To reduce the decoding time resulting from the serial autoregressive context model, the parallel context model has been proposed as an alternative that necessitates only two passes during the decoding phase, thus facilitating efficient image compression in real-world scenarios. However, performance degradation occurs due to its incomplete casual context. To tackle this issue, we conduct an in-depth analysis of the performance degradation observed in existing parallel context models, focusing on two aspects: the Quantity and Quality of information utilized for context prediction and decoding. Based on such analysis, we propose the Corner-to-Center transformer-based Context Model (C3M) designed to enhance context and latent predictions and improve rate–distortion performance. Specifically, we leverage the logarithmic-based prediction order to predict more context features from corner to center progressively. In addition, to enlarge the receptive field in the analysis and synthesis transformation, we use the Long-range Crossing Attention Module (LCAM) in the encoder/decoder to capture the long-range semantic information by assigning the different window shapes in different channels. Extensive experimental evaluations show that the proposed method is effective and outperforms the state-of-the-art parallel methods. Finally, according to the subjective analysis, we suggest that improving the detailed representation in transformer-based image compression is a promising direction to be explored.


Particle filtering for dynamic systems with future constraints

Abstract: This work focuses on a new state estimation problem for dynamic systems with future constraints. It is well known that constraints, as a priori, should always be satisfied by states of some dynamic systems, and thus sufficiently fusing the valuable information into filters will produce more accurate estimates. To this end, a particle smoothing framework is proposed by incorporating future constraints, instead of only current constraints. Next, we construct a criterion function by making the center value of dynamic model satisfy constraints to improve dynamic modeling accuracy, and investigate a cost function to compensate for the impact of the modified dynamic model on the original predicted distribution. Based on this, we present a new particle filter that future constraints are applied in importance sampling and updating particle weights. Especially, the modified dynamic model is chosen as the importance density function so that the sampled particles satisfy constraints at consecutive times as much as possible. Finally, two simulations verify the superiority of the proposed filter.


Scalable and Accelerated Self-healing Control Circuit Using Evolvable Hardware

Abstract: Controllers are mission-critical components of any electronic design. By sending control signals, they decide which and when other data path elements must operate. Faults, especially Single Event Upset (SEU) occurrence in these components, can lead to functional/mission failure of the system when deployed in harsh environments. Hence, competence to self-heal from SEU is highly required in the control path of the digital system. Reconfiguration is critical for recovering from a faulty state to a non-faulty state. Compared to native reconfiguration, the Virtual Reconfigurable Circuit (VRC) is an FPGA-generic reconfiguration mechanism. The non-partial reconfiguration in VRC and extensive architecture are considered hindrances in extending the VRC-based Evolvable Hardware (EHW) to real-time fault mitigation. To confront this challenge, we have proposed an intrinsic constrained evolution to improve the scalability and accelerate the evolution process for VRC-based fault mitigation in mission-critical applications. Experimentation is conducted on complex ACM/SIGDA benchmark circuits and real-time circuits used in space missions, which are not included in related works. In addition, a comparative study is made between existing and proposed methodologies for brushless DC motor control circuits. The hardware utilization in the multiplexer has been significantly reduced, resulting in up to a 77% reduction in the existing VRC architecture. The proposed methodology employs a fault localization approach to narrow the search space effectively. This approach has yielded an 87% improvement on average in convergence speed, as measured by the evolution time, compared to the existing work.


Generalised synchronisations, embeddings, and approximations for continuous time reservoir computers

Abstract: We establish conditions under which a continuous time reservoir computer, such as a leaky integrator echo state network, admits a generalised synchronisation f between the source dynamics and reservoir dynamics. We show that multiple generalised synchronisations can exist simultaneously, and connect this to the multi-Echo-State-Property (multi-ESP). In the special case of a linear reservoir computer, we derive a closed form expression for the generalised synchronisation f. Furthermore, we establish conditions under which f is of class C1, and conditions under which f is a topological embedding on the fixed points of the source system. This embedding result is closely related to Takens' embedding Theorem. We also prove that the embedding of fixed points occurs almost surely for randomly generated linear reservoir systems. With an embedding achieved, we discuss how the universal approximation theorem makes it possible to forecast the future dynamics of the source system and replicate its topological properties. We illustrate the theory by embedding a fixed point of the Lorenz-63 system into the reservoir space using numerical methods. Finally, we show that if the observations are perturbed by white noise, the GS is preserved up to a perturbation by an Ornstein–Uhlenbeck process.


An anti-steganalysis adaptive steganography for HEVC video based on PU partition modes

Abstract: Calibration-based steganalysis methods are a fatal threat to current HEVC steganography algorithms. The current steganography algorithms based on prediction unit (PU) still lack resistance to this type of steganalysis. The reason is that the improvements in HEVC codec provide more potential steganographic space as well as introduce more detectable distortion. Therefore, a HEVC video steganography method that has resistance to PU shift-based steganalysis is proposed in this paper. First, the PU shift phenomenon that exists in PU-based steganography is introduced. Then the existing calibration-based steganalysis method is enhanced. According to the encoding rules of HEVC, the distortion cost function according to the difference of rate distortion cost (RD cost) during recompression is designed, which aims to obtain better resistance to calibration-based steganalysis. Experimental results demonstrate that the steganography algorithm designed in this paper provides excellent resistance ability for calibration-based steganalysis.


Traffic spills the beans: A robust video identification attack against YouTube

Abstract: Traffic-based video identification attack is a side-channel attack that allows an attacker to identify transmitted online videos from encrypted network traffic. Existing attacks face two types of challenges: 1) YouTube's improved streaming technology renders them invalid or impacted; 2) real-world diverse traffic makes them underperform. In this paper, we provide the first study of YouTube's technical improvements and reveal its three key features: i) the mechanism that replaces the MPD file, ii) variable combined segments, and iii) various buffering processes. Based on the above analysis, we propose a Robust and easily scalable Video Identification Attack against YouTube called RoVIA. RoVIA extracts video fingerprints from video file headers and reference sequences from video traffic. RoVIA uses the reference sequence to generate candidate sequences in the pre-built video fingerprint library and achieves video identification by measuring sequence similarity. Experimental results show that RoVIA achieves open-world accuracy of 0.967, 0.924, and 0.863 in open-world, fixed-quality, and poor network experiments. Furthermore, RoVIA's scalability allows the target classes to be adjusted by updating the fingerprint library without collecting new samples and retraining parameters. This study aims to demonstrate the vulnerabilities of YouTube streaming technology and the potential consequences of its malicious use. We also propose two YouTube-friendly countermeasures to help developers build effective defenses.


CTFCD: Channel transformer based on full convolutional decoder for single image deraining

Abstract: Although convolutional neural network and visual transformer have been successfully applied in various field of computer vision, there is little work combining them to construct an efficient network model to solve image deraining tasks. Convolutional neural network is utilized to extract the features from each region, while visual transformer is utilized to extract the context information between local features. Due to the limitations in computational resources and processing time, it is difficult for visual transformer to process high-resolution images, which hinders the application of visual transformer in devices with limited hardware resources. The purpose of this article is to utilize the advantages of to design a lightweight encoder-decoder network for real-time image deraining. Firstly, a novel channel Transformer module is designed to obtain global contextual information, where deep separable convolution is utilized to extract multi-scale local features and a Transformer encoder is constructed by stacking Transformer modules. Secondly, a decoder based on a fully convolution is designed to adopt mask attention and inverted bottleneck convolution to achieve progressive feature fusion and feature reconstruction, which significantly reduces computational complexity and memory requirement. A large number of experimental results have verified that the proposed method has superior performance compared with other state-of-the art methods, while the computational cost and parameter quantity are much smaller than those of similar methods.


Modeling of PID controlled 3DOF robotic manipulator using Lyapunov function for enhancing trajectory tracking and robustness exploiting Golden Jackal algorithm

Abstract: In this study, a three degrees of freedom (3 DOF) rigid-link robotic manipulator (RLM) has been simulated by using the Simscape model and the mathematical model derived by Lagrange method. The robot arm has been regulated by an Optimized PID Controller to achieve better tracking performance and reasonable robustness against disturbances and payload uncertainty. To optimize the controller, a novel nature-inspired Golden Jackal Optimization (GJO) algorithm has been used due to its efficient exploration that increases the diversity of the released solutions and its exploitation schemes which enhance the best-explored solutions. The tuning process has utilized a Lyapunov stability function as the objective function (OF) and the efficacy of the proposed algorithm is evaluated through a comprehensive comparison with various state-of-the-art metaheuristic techniques such as Particle Swarm Optimization (PSO), Artificial Bee Colony (ABC), Jellyfish Search Optimizer (JSO), Whale Optimization Algorithm (WOA), Arithmetic Optimization Algorithm (AOA) and Sine Cosine Algorithm (SCA). The assessment has been conducted on benchmark error-based functions, providing rigorous testing and validation of the algorithm's performance. Furthermore, the performance evaluation has focused on the system's robustness against disturbances, noise, and variations in the payload mass, particularly in the context of Pick and Place (PNP) industrial tasks. The results of simulation have demonstrated that the optimized system, employing the Lyapunov function, demonstrated superior performance in minimizing the objective function value compared to other benchmark functions.


YembaTones: A syllable-tone annotated dataset for speech recognition and prosodic analysis of the Yemba language

Abstract: Prosody is a key area of linguistics that explores tonal and rhythmic variations in speech. In tonal languages such as Yemba, prosody plays a crucial role in distinguishing between words with different meanings or different grammatical forms. However, despite the large number of native speakers of this language in Cameroon, there are few resources for the speech recognition and synthesis. In this article, we present YembaTones, a syllabic and tonal annotated dataset, created from a dictionary we designed of 344 Yemba/French words coming from the most common phrases of the language, grouped according to their spellings that only differ by the tone. The dataset was originally designed for training and evaluating tone detection models for tonal and low resource languages. The recordings of the pronunciation of these words were made with 11 native speakers of Yemba, mainly specialists in linguistics with a good command of the sounds of the language. The recordings were made with a dictaphone in different places such as the homes of the speakers, the campuses and their workplaces. Then they have been cleaned and segmented into individual audio files corresponding to the pronunciations of isolated words, using the software Audacity. After cleaning and segmentation, we selected 3420 good quality audio files for annotation. Annotations were made at the syllabic and tonal level using Praat software. YembaTones is a valuable resource not only for the training and evaluation of automatic tone detection models but also for automatic speech recognition, speech synthesis of tonal and poorly endowed languages, as well as for the study of prosody and Yemba phonetics, research in speech acoustics and phonetic linguistics.


Algorithms for 2-club cluster deletion problems using automated generation of branching rules

Abstract: In the 2-Club Cluster Vertex Deletion (resp., 2-Club Cluster Edge Deletion) problem the input is a graph G and an integer k, and the goal is to decide whether there is a set of at most k vertices (resp., edges) whose removal from G results in a graph in which the diameter of every connected component is at most 2. In this paper we give algorithms for 2-Club Cluster Vertex Deletion and 2-Club Cluster Edge Deletion whose running times are O⁎(3.104k) and O⁎(2.562k), respectively. Our algorithms were obtained using automated generation of branching rules. Our results improve the previous O⁎(3.303k)-time algorithm for 2-Club Cluster Vertex Deletion [Liu et al., FAW-AAIM 2012] and the O⁎(2.695k)-time algorithm for 2-Club Cluster Edge Deletion [Abu-Khzam et al., TCS 2023].


Efficient approach of high average utility pattern mining with indexed list-based structure in dynamic environments

Abstract: Various studies on high utility pattern mining have been conducted to satisfy the emerging need to consider the characteristics of real-world databases, such as the importance and quantity of items. In the traditional utility-based framework, the mining result is influenced by the number of items in a pattern, or in some cases, single utilities of items. In order to overcome this drawback, high average utility pattern mining has been proposed. It provides more interesting results since it takes into account the average utility of patterns by considering their lengths. Methods based on this concept have emerged in recent years, including ones that target incremental environments. However, existing algorithms create an enormous number of candidate patterns or require complex operations during the mining process. To address this degradation, we propose a new and more efficient approach for mining high average utility patterns in dynamic environments. The proposed algorithm utilizes a data structure more efficient than previous ones, which takes the form of an indexed list. It also incorporates efficient realigning and mining techniques for handling incremental data and accurately mining results. Experimental results show the superiority of the proposed approach in terms of runtime, memory usage, scalability, and accuracy.


Geochemical prospectivity mapping using compositional balance analysis and multifractal modeling: A case study in the Jinshuikou area, Qinghai, China

Abstract: To effectively map the prospecting targets related to deposit-type sought, local indicators of spatial association (LISA), compositional balance analysis (CoBA), and multifractal modeling were applied to process stream sediment geochemical data that are collected from the Jinshuikou area, Qinghai province, China. In this study, 14 elements, including Zn, Cu, Ni, Mo, Co, V, Ti, Sb, Ag, Pb, Sn, Hg, Cd, and W, were analyzed in 493 samples. The result of LISA shows that the ore-forming elements Zn and Cu have a close spatial autocorrelation, which supports the construction of balance b1 related to the Zn-Cu mineralization assemblage. A traditional contrast additive method was applied to compare with balance b1 for geochemical anomaly identification. The results clearly indicate that balance b1 significantly assisted with not only reduction of geochemical data internal imbalance caused by extreme value, but also intensification of Zn-Cu assemblage geochemical anomalies associated with Zn polymetallic mineralization. The high anomalies of the two balances, b5 (Co-V-Ti) and b8 (Sn-Pb-Ag-Sb), reflect well the distribution of basic and intermediate-acid rocks in the area, respectively. The geochemical distribution pattern of the balance b1 can be depicted by multifractal parameters such as singular index α, as well as asymmetry index R, which are useful for describing the spatial heterogeneity of the balance b1. The singularity indices of balance b1 were classified by the concentration-area (C-A) fractal model to produce the high potential areas of the Zn-Cu assemblage. The high potential areas were obviously correlated with all Zn polymetallic occurrences. So, combined with the metallogenic geological characteristics of the study area, five prospective prediction targets were mapped. The anomaly verification demonstrates that the Zn concentration has reached the lowest industrial grade and cut-off grade corresponding to the target area I-1 and I-2, respectively, and Cu also has certain metallogenic potential. There is good potential for Zn-Cu mineralization as represented by this integrated method.


Spatial distribution of molybdenum in catchment sediments in the Sino-Mongolian border region

Abstract: The Sino-Mongolian border region is an important exploration hotspot in the Asia-Pacific margin and hosts one of the three major porphyry Cu-Au-Mo mineralization belts in the world. Variations in the concentration and spatial distribution of Mo in the region can indirectly reflect the distribution of other minerals, such as copper; therefore, Mo has been widely used as a pathfinder element for geochemical prospecting for porphyry Cu deposits. However, comprehensive and integrated analytical studies of single elements (e.g., Mo) in the Sino-Mongolian border region are lacking. Therefore, this study collected 10,453 catchment sediment samples from the study region and analyzed 69 elements. The concentration of Mo in these samples ranged from 0.10 to 84.96 mg/kg, with a median and mean of 0.84 mg/kg (considered the geochemical background value) and 0.97 mg/kg, respectively. The factors influencing the Mo concentration and spatial distribution along the Sino-Mongolian border region were investigated in terms of geological background, climate, precipitation, mineral distribution, and exploitation history. Influenced by climate and precipitation, Mo in catchment sediments is solubilized and easily transported under the oxidizing effect of hypergenesis. Mo concentrations were low in forested areas along the Altai Mountains in the west and Da Hinggan Ling Mountains in the east. Sixteen high-concentration Mo areas (geochemical province/domain, > 1.07 mg/kg, > Q75%) that contained 46 Mo geochemical anomalies (> 1.24 mg/kg, > Q85%) were circumscribed in this study. These anomalous areas were mainly related to the widespread distribution of minerals, coal-forming strata, and black shale deposits in the region. Compared to previous studies, this study identified more concentrated geochemical anomaly areas with a wider distribution in the Sino-Mongolian border region and provided higher quality geochemical data to promote further mineral exploration and support environmental change studies in this area.


A filter-augmented auto-encoder with learnable normalization for robust multivariate time series anomaly detection

Abstract: While existing reconstruction-based multivariate time series (MTS) anomaly detection methods demonstrate advanced performance on many challenging real-world datasets, they generally assume the data only consists of normal samples when training models. However, real-world MTS data may contain significant noise and even be contaminated by anomalies. As a result, most existing approaches easily capture the pattern of the contaminated data, making identifying anomalies more difficult. Although a few studies have aimed to mitigate the interference of the noise and anomalies by introducing various regularizations, they still employ the objective of fully reconstructing the input data, impeding the model from learning an accurate profile of the MTS's normal pattern. Moreover, it is difficult for existing methods to apply the most appropriate normalization schemes for each dataset in various complex scenarios, particularly for mixed-feature MTS. This paper proposes a filter-augmented auto-encoder with learnable normalization (NormFAAE) for robust MTS anomaly detection. Firstly, NormFAAE designs a deep hybrid normalization module. It is trained with the backbone end-to-end in the current training task to perform the optimal normalization scheme. Meanwhile, it integrates two learnable normalization sub-modules to deal with the mixed-feature MTS effectively. Secondly, NormFAAE proposes a filter-augmented auto-encoder with a dual-phase task. It separates the noise and anomalies from the input data by a deep filter module, which facilitates the model to only reconstruct the normal data, achieving a more robust latent representation of MTS. Experimental results demonstrate that NormFAAE outperforms 17 typical baselines on five real-world industrial datasets from diverse fields.