Reviewerly/exHarmony huggingface dataset:
Dendrogram-based Heterogeneous Learners for Automatic Modulation Classification in DSTBC-OFDM Systems

Authors:
raw_author_name: H. MOULAY
raw_affiliation_string: Telecommunications and Digital Signal Laboratory, Djillali Liabes University, Sidi Bel Abbes, Algeria
raw_author_name: A.B. DJEBBAR
raw_affiliation_string: Telecommunications and Digital Signal Laboratory, Djillali Liabes University, Sidi Bel Abbes, Algeria
raw_author_name: B. DEHRI
raw_affiliation_string: Independent researcher, Hasnaoui Group, Sidi Bel Abbes, Algeria
raw_author_name: M. BESSEGHIER
raw_affiliation_string: Mustapha Stambouli University, Mascara, Algeria; Telecommunications and Digital Signal Laboratory, Djillali Liabes University, Sidi Bel Abbes, Algeria

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

Authors:
raw_author_name: Ruisheng Ran
raw_affiliation_string: The College of Computer and Information Science, Chongqing Normal University, Chongqing 401331, China
raw_author_name: Jinping Wang
raw_affiliation_string: The College of Computer and Information Science, Chongqing Normal University, Chongqing 401331, China
raw_author_name: Bin Fang
raw_affiliation_string: The College of Computer Science, Chongqing University, Chongqing 400044, China
raw_author_name: Weiming Yang
raw_affiliation_string: The College of Computer and Information Science, Chongqing Normal University, Chongqing 401331, China

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

Authors:
raw_author_name: Muhammad Khurram Khan
raw_affiliation_string: Center of Excellence in Information Assurance, King Saud University Riyadh, Saudi Arabia

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

Authors:
raw_author_name: Gian Carlo Milanese
raw_affiliation_string: Università degli Studi di Milano-Bicocca, Italy
raw_author_name: Gabriella Pasi
raw_affiliation_string: Università degli Studi di Milano-Bicocca, Italy

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

Authors:
raw_author_name: Wenxu Wang
raw_affiliation_string: College of Information and Electrical Engineering, China Agricultural University, Beijing, 100083, China
raw_author_name: Zhenbo Li
raw_affiliation_string: Beijing Engineering and Technology Research Center for Internet of Things in Agriculture, Beijing, 100083, China; College of Information and Electrical Engineering, China Agricultural University, Beijing, 100083, China; Key Laboratory of Smart Farming Technologies for Aquatic Animal and Livestock, Ministry of Agriculture and Rural Affairs, Beijing, 100083, China
raw_author_name: Weiran Li
raw_affiliation_string: College of Information and Electrical Engineering, China Agricultural University, Beijing, 100083, China

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

Authors:
raw_author_name: Junghwan Kim
raw_affiliation_string: Department of Geography, Virginia Tech, USA
raw_author_name: Jinhyung Lee
raw_affiliation_string: Department of Geography and Environment, Western University, Canada
raw_author_name: Ismini Lourentzou
raw_affiliation_string: Department of Computer Science, Virginia Tech, USA

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

Authors:
raw_author_name: Dong Li
raw_affiliation_string: School of Computer and Software Engineering, Xihua University, Chengdu 610039, China
raw_author_name: Heng Zhang
raw_affiliation_string: School of Computer and Software Engineering, Xihua University, Chengdu 610039, China

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

Authors:
raw_author_name: Mohammed A. Shehab
raw_affiliation_string: Concordia University, 1455 De Maisonneuve Blvd. W., Montreal, H3G 1M8, Canada
raw_author_name: Wael Khreich
raw_affiliation_string: American University of Beirut, Beirut, Lebanon
raw_author_name: Abdelwahab Hamou-Lhadj
raw_affiliation_string: Concordia University, 1455 De Maisonneuve Blvd. W., Montreal, H3G 1M8, Canada

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

Authors:
raw_author_name: Giuseppe Sanfilippo
raw_affiliation_string: Department of Mathematics and Computer Science, University of Palermo, Palermo, Italy

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

Authors:
raw_author_name: Luca Incurvati
raw_author_name: Julian J. Schlöder

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

Authors:
raw_author_name: Emanuele Ogliari
raw_affiliation_string: Politecnico di Milano, via Lambruschini 4, Milan, 20156, Italy

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

Authors:
raw_author_name: Yang Sui
raw_affiliation_string: Department of Electrical and Computer Engineering, Rutgers University, 94 Brett Road, Piscataway, 08854, NJ, USA
raw_author_name: Ding Ding
raw_affiliation_string: Media Lab, Tencent America, 2747 Park Blvd, Palo Alto, 94306, CA, USA
raw_author_name: Xiang Pan
raw_affiliation_string: Media Lab, Tencent America, 2747 Park Blvd, Palo Alto, 94306, CA, USA
raw_author_name: Xiaozhong Xu
raw_affiliation_string: Media Lab, Tencent America, 2747 Park Blvd, Palo Alto, 94306, CA, USA
raw_author_name: Shan Liu
raw_affiliation_string: Media Lab, Tencent America, 2747 Park Blvd, Palo Alto, 94306, CA, USA
raw_author_name: Bo Yuan
raw_affiliation_string: Department of Electrical and Computer Engineering, Rutgers University, 94 Brett Road, Piscataway, 08854, NJ, USA
raw_author_name: Zhenzhong Chen
raw_affiliation_string: School of Remote Sensing and Information Engineering, Wuhan University, Hubei, 430072, Wuhan, China

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

Authors:
raw_author_name: Yan Liang
raw_affiliation_string: School of Automation, Northwestern Polytechnical University, Xi'an, China
raw_author_name: Yanwei Liu
raw_affiliation_string: School of Mechanical and Precision Instrument Engineering, Xi'an University of Technology, Xi'an, China
raw_author_name: Weifeng Liu
raw_affiliation_string: School of Electrical and Control Engineering, Shaanxi University of Science and Technology, Xi'an, China

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.