Reviewerly/exHarmony huggingface dataset:
Indistinguishability Obfuscation via Mathematical Proofs of Equivalence

Over the last decade, indistinguishability obfuscation (iO) has emerged as a seemingly omnipotent primitive with numerous applications to cryptography and beyond. Moreover, recent breakthrough work has demonstrated that iO can be realized from well-founded assumptions. A thorn to all this remarkable progress is a limitation of all known constructions of general-purpose iO: the security reduction incurs a loss that is exponential in the input length of the function. This “input-length barrier” to iO stems from the non-falsifiability of the iO definition and is discussed in folklore as being possibly inherent. It has many negative consequences; notably, constructing iO for programs with inputs of unbounded length remains elusive due to this barrier. We present a new framework aimed towards overcoming the input-length barrier. Our approach relies on short mathematical proofs of functional equivalence of circuits (and Turing machines) to avoid the brute-force “input-by-input” check employed in prior works.– We show how to obfuscate circuits that have efficient proofs of equivalence in Propositional Logic with a security loss independent of input length.– Next, we show how to obfuscate Turing machines with unbounded length inputs, whose functional equivalence can be proven in Cook’s Theory PV.– Finally, we demonstrate applications of our results to succinct non-interactive arguments and witness encryption, and provide guidance on using our techniques for building new applications.To realize our approach, we depart from prior work and develop a new gate-by-gate obfuscation template that preserves the topology of the input circuit.
ISO/IEC 29110 and Software Engineering Education: A Systematic Mapping Study

This article presents a study of the publications made on the ISO/IEC 29110 standard in the university context, especially from the perspective of software engineering education. ISO 29110 is a life cycle profiles for very small entities on systems and software engineering standard, published in many parts. ISO 29110, since its publication in 2011 and its continuous evolution to these days, is the subject of study in different contexts, with education being a relevant axis. Considering, that software engineering education has implications in the software industry in emerging countries, it is necessary to identify and consolidate the work done in this context. In this study, the main research question was what researches have been done at ISO 29110 in the training of software engineers? To answer this question, a systematic mapping study (SMS) was performed. In the SMS, 241 articles were obtained with search string and 17 of them became as primary study after a process selection. Based on these studies, it was possible to determine that the software engineering Basic profile of ISO 29110 and its processes (Project Management and Software Implementation) have been the most studied. Besides, it was identified that project-oriented learning and gamification techniques have been the most used ISO 29110 learning strategies in the training of future software industry professionals.
Enhanced Frequency Fusion Network with Dynamic Hash Attention for image denoising

Recently, Transformer-based image denoising methods have achieved great progress in the image denoising task. However, these methods also lead to two problems: (a) Since noise can destroy texture or details in the image, the resulting tokens with low weight values may have a negative impact on the reconstructed denoised image (i.e., there are artifacts in the reconstructed image, etc); (b) Frequencies in different domains are ignored, leading to the missing of textural details in the reconstructed image. To this end, we propose an Enhanced Frequency Fusion Network (EFF-Net) with dynamic hash attention for image denoising, called EFF-Net. Specifically, to alleviate the impact of problem (a), we present the Dynamic Hash Attention (DHA) module, which aims to effectively mitigate the negative impact of tokens with low weight values on image denoising performance; Furthermore, we start from the frequency perspective and design the Enhanced Frequency Fusion (EFF) module with Decomposition Frequency (DF) as the core component, which aims to achieve the separation and fusion of noisy image content in the frequency domain, and appropriately reconstruct the image content of different frequency components at different locations. The DHA and EFF modules are integrated into plug-and-play Adaptive Frequency Enhancement (AFE) transformer blocks to selectively recover different frequencies based on long-range pixel dependency. The extensive experiments endorse the effective, and superior performance of our EFF-Net for image denoising.
Construction of a fluvial facies knowledge graph and its application in sedimentary facies identification

Lithofacies paleogeography is a data-intensive discipline that involves the interpretation and compilation of sedimentary facies. Traditional sedimentary facies analysis is a labor-intensive task with the added complexity of using unstructured knowledge and unstandardized terminology. Therefore, it is very difficult for beginners or non-geology scholars who lack a systematic knowledge and experience in sedimentary facies analysis. These hurdles could be partly alleviated by having a standardized, structured, and systematic knowledge base coupled with an efficient automatic machine-assisted sedimentary facies identification system. To this end, this study constructed a knowledge system for fluvial facies and carried out knowledge representation. Components include a domain knowledge graph for types of fluvial facies (meandering, braided and other fluvial depositional environments) and their characteristic features (bedforms, grain size distribution, etc.) with visualization, a method for query and retrieval on a graph database platform, a hierarchical knowledge tree-structure, a data-mining clustering algorithm for machine-analysis of publication texts, and an algorithm model for this area of sedimentary facies reasoning. The underlying sedimentary facies identification and knowledge reasoning system is based on expert experience and synthesis of publications. For testing, 17 sets literature publications data that included details of sedimentary facies data (bedforms, grain sizes, etc.) were submitted to the artificial intelligence model, then compared and validated. This testing set of automated reasoning results yielded an interpretation accuracy of about 90% relative to the published interpretations in those papers. Therefore, the model and algorithm provide an efficient and automated reasoning technology, which provides a new approach and route for the rapid and intelligent identification of other types of sedimentary facies from literature data or direct use in the field.
Traffic classification using distributions of latent space in software-defined networks: An experimental evaluation

With the emergence of new Internet services and the drastic increase in Internet traffic, traffic classification has become increasingly important to effectively satisfy the quality of service to users. The traffic classification system should be resilient and operate smoothly regardless of network conditions or performance and should be capable of handling various classes of Internet services. This paper proposes a traffic classification method in a software-defined network environment that employs a variational autoencoder (VAE) to accomplish this. The proposed method trains the VAE using six statistical features and extracts the distributions of latent features for the flows in each service class. Furthermore, it classifies the query traffic by comparing the distributions of latent features for the query traffic with the learned distributions of the service classes. For the experiment, the statistical features of network flows were collected from real-world domestic and overseas Internet services for training and testing. According to the experimental results, the proposed method has an average accuracy of 89%. This accuracy was 52%, 47%, 39%, 59%, and 26% higher than conventional statistics-based classification methods, MLP, AE+MLP, VAE+MLP, and SVM, respectively. This result clearly suggests that probability distributions of latent features, rather than specific values for latent features, can be used as more stable features.
Impact of Dataset and Model Parameters on Machine Learning Performance for the Detection of GPS Spoofing Attacks on Unmanned Aerial Vehicles

GPS spoofing attacks are a severe threat to unmanned aerial vehicles. These attacks manipulate the true state of the unmanned aerial vehicles, potentially misleading the system without raising alarms. Several techniques, including machine learning, have been proposed to detect these attacks. Most of the studies applied machine learning models without identifying the best hyperparameters, using feature selection and importance techniques, and ensuring that the used dataset is unbiased and balanced. However, no current studies have discussed the impact of model parameters and dataset characteristics on the performance of machine learning models; therefore, this paper fills this gap by evaluating the impact of hyperparameters, regularization parameters, dataset size, correlated features, and imbalanced datasets on the performance of six most commonly known machine learning techniques. These models are Classification and Regression Decision Tree, Artificial Neural Network, Random Forest, Logistic Regression, Gaussian Naïve Bayes, and Support Vector Machine. Thirteen features extracted from legitimate and simulated GPS attack signals are used to perform this investigation. The evaluation was performed in terms of four metrics: accuracy, probability of misdetection, probability of false alarm, and probability of detection. The results indicate that hyperparameters, regularization parameters, correlated features, dataset size, and imbalanced datasets adversely affect a machine learning model’s performance. The results also show that the Classification and Regression Decision Tree classifier has an accuracy of 99.99%, a probability of detection of 99.98%, a probability of misdetection of 0.2%, and a probability of false alarm of 1.005%, after removing correlated features and using tuned parameters in a balanced dataset. Random Forest can achieve an accuracy of 99.94%, a probability of detection of 99.6%, a probability of misdetection of 0.4%, and a probability of false alarm of 1.01% in similar conditions.
Punctured Low-Bias Codes Behave Like Random Linear Codes

Random linear codes are a workhorse in coding theory, and are used to show the existence of codes with the best known or even near-optimal trade-offs in many noise models. However, they have little structure besides linearity, and are not amenable to tractable error-correction algorithms. In this work, we prove a general derandomization result applicable to random linear codes. Namely, in settings where the coding-theoretic property of interest is “local” (in the sense of forbidding certain bad configurations involving few vectors–code distance and list-decodability being notable examples), one can replace random linear codes (RLCs) with a significantly derandomized variant with essentially no loss in parameters. Specifically, instead of randomly sampling coordinates of the (long) Hadamard code (which is an equivalent way to describe RLCs), one can randomly sample coordinates of any code with low bias. Over large alphabets, the low bias requirement can be weakened to just large distance. Furthermore, large distance suffices even with a small alphabet in order to match the current best known bounds for RLC list-decodability. In particular, by virtue of our result, all current (and future) achievability bounds for list-decodability of random linear codes extend automatically to random puncturings of any low-bias (or large alphabet) “mother” code. We also show that our punctured codes emulate the behavior of RLCs on stochastic channels, thus giving a derandomization of RLCs in the context of achieving Shannon capacity as well. Thus, we have a randomness-efficient way to sample codes achieving capacity in both worst-case and stochastic settings that can further inherit algebraic or other algorithmically useful structural properties of the mother code. This is an extended abstract. The full version is available at https://arxiv.org/abs/2109.11725.
Imitation Learning-Based Implicit Semantic-Aware Communication Networks: Multi-Layer Representation and Collaborative Reasoning

Semantic communication has recently attracted significant interest from both industry and academia due to its potential to transform the existing data-focused communication architecture towards a more generally intelligent and goal-oriented semantic-aware networking system. Despite its promising potential, semantic communications and semantic-aware networking are still in their infancy. Most existing works focus on transporting and delivering the explicit semantic information, e.g., labels or features of objects, that can be directly identified from the source signal. The original definition of semantics as well as recent results in cognitive neuroscience suggest that it is the implicit semantic information, in particular the hidden relations connecting different concepts and feature items that play the fundamental role in recognizing, communicating, and delivering the real semantic meanings of messages. Motivated by this observation, we propose a novel reasoning-based implicit semantic-aware communication network architecture that allows destination users to directly learn a reasoning mechanism that can automatically generate complex implicit semantic information based on a limited clue information sent by the source users. Our proposed architecture can be implemented in a multi-tier cloud/edge computing networks in which multiple tiers of cloud data center (CDC) and edge servers can collaborate and support efficient semantic encoding, decoding, and implicit semantic interpretation for multiple end-users. We introduce a new multi-layer representation of semantic information taking into consideration both the hierarchical structure of implicit semantics as well as the personalized inference preference of individual users. We model the semantic reasoning process as a reinforcement learning process and then propose an imitation-based semantic reasoning mechanism learning (iRML) solution to learning a reasoning policy that imitates the inference behavior of the source user. A federated graph convolutional network (GCN)-based collaborative reasoning solution is proposed to allow multiple edge servers to jointly construct a shared semantic interpretation model based on decentralized semantic message samples. Extensive experiments have been conducted based on real-world datasets to evaluate the performance of our proposed architecture. Numerical results confirm that iRML offers up to 25.8 dB improvement on the semantic symbol error rate, compared to the semantic-irrelevant communication solutions.
Annealing Optimization for Progressive Learning With Stochastic Approximation

In this work, we introduce a learning model designed to meet the needs of applications in which computational resources are limited, and robustness and interpretability are prioritized. Learning problems can be formulated as constrained stochastic optimization problems, with the constraints originating mainly from model assumptions that define a tradeoff between complexity and performance. This tradeoff is closely related to overfitting, generalization capacity, and robustness to noise and adversarial attacks, and depends on both the structure and complexity of the model, as well as the properties of the optimization methods used. We develop an online prototype-based learning algorithm based on annealing optimization that is formulated as an online gradient-free stochastic approximation algorithm. The learning model can be viewed as an interpretable and progressively growing competitive-learning neural network model to be used for supervised, unsupervised, and reinforcement learning. The annealing nature of the algorithm contributes to minimal hyperparameter tuning requirements, poor local minima prevention, and robustness with respect to the initial conditions. At the same time, it provides online control over the performance–complexity tradeoff by progressively increasing the complexity of the learning model as needed, through an intuitive bifurcation phenomenon. Finally, the use of stochastic approximation enables the study of the convergence of the learning algorithm through mathematical tools from dynamical systems and control, and allows for its integration with reinforcement learning algorithms, constructing an adaptive state–action aggregation scheme.
Towards Adversarial Attacks for Clinical Document Classification

Regardless of revolutionizing improvements in various domains thanks to recent advancements in the field of Deep Learning (DL), recent studies have demonstrated that DL networks are susceptible to adversarial attacks. Such attacks are crucial in sensitive environments to make critical and life-changing decisions, such as health decision-making. Research efforts on using textual adversaries to attack DL for natural language processing (NLP) have received increasing attention in recent years. Among the available textual adversarial studies, Electronic Health Records (EHR) have gained the least attention. This paper investigates the effectiveness of adversarial attacks on clinical document classification and proposes a defense mechanism to develop a robust convolutional neural network (CNN) model and counteract these attacks. Specifically, we apply various black-box attacks based on concatenation and editing adversaries on unstructured clinical text. Then, we propose a defense technique based on feature selection and filtering to improve the robustness of the models. Experimental results show that a small perturbation to the unstructured text in clinical documents causes a significant drop in performance. Performing the proposed defense mechanism under the same adversarial attacks, on the other hand, avoids such a drop in performance. Therefore, it enhances the robustness of the CNN model for clinical document classification.
Characterization of strain field distribution in carbon fiber reinforced resin matrix composites using electron beam moiré and geometric phase analysis

The meso and nanoscopic deformation behavior of the carbon-fiber reinforced resin matrix composite during tensile loading were investigated via in situ scanning electron microscopy combined with the electron beam moiré and geometric phase analysis (GPA) techniques. Electron beam lithography was used to fabricate a cross-grating with a pitch of 359 nm (2780 lines/mm) on the surface of the composite material to facilitate the direct measurement of the deformation. In situ observation of the composite subjected to tension were conducted to determine strain filed distribution under different loads. The global and local strain field were measured and analyzed at the meso and nanoscale. The strain field distribution around matrix transverse crack, cracks between fibers, interlaminar cracks, and broken fiber cracks were characterized via GPA under load. The strain concentration factor and the ineffective length of the broken fiber were also measured experimentally.
Bug detection in Java code: An extensive evaluation of static analysis tools using Juliet Test Suites

Abstract Previous studies have demonstrated the usefulness of employing automated static analysis tools (ASAT) and techniques to detect security bugs in software systems. However, these studies are usually focused on analyzing the effectiveness of the tools using open‐source tools based on C/C++ source code. The choice for making an appropriate decision on the most suitable tool for bug detection in Java code software remains a relatively unexplored domain. To address this deficiency, this study empirically evaluates eight widely used ASATs, namely, Findbug, PMD, YASCA, LAPSE+, JLint, Bandera, ESC/Java, and Java Pathfinder using the Juliet Test Suite (Test Suite v1.2). Additionally, we assessed the performance of the detection capabilities for the aforementioned bug detection tools using robust performance measures such as precision, recall, Youden index, and the OWASP web benchmark evaluation (WBE). The experimental results show that the tools obtain precision values ranging from 83% to 90.7% based on the studied datasets. Specifically, the Java Pathfinder achieves the best precision score of 90.7%, followed by YASCA and Bandera with a precision score of 88.7% and 83%, respectively. Similarly, Bandera, ESC/Java, and Java Pathfinder obtain a Youden index of 0.8, which indicates the effectiveness of the tools in detecting security bugs in Java source code.
NRN-RSSEG: A Deep Neural Network Model for Combating Label Noise in Semantic Segmentation of Remote Sensing Images

The performance of deep neural networks depends on the accuracy of labeled samples, as they usually contain label noise. This study examines the semantic segmentation of remote sensing images that include label noise and proposes an anti-label-noise network framework, termed Labeled Noise Robust Network in Remote Sensing Image Semantic Segmentation (NRN-RSSEG), to combat label noise. The algorithm combines three main components: network, attention mechanism, and a noise-robust loss function. Three different noise rates (containing both symmetric and asymmetric noise) were simulated to test the noise resistance of the network. Validation was performed in the Vaihingen region of the ISPRS Vaihingen 2D semantic labeling dataset, and the performance of the network was evaluated by comparing the NRN-RSSEG with the original U-Net model. The results show that NRN-RSSEG maintains a high accuracy on both clean and noisy datasets. Specifically, NRN-RSSEG outperforms UNET in terms of PA, MPA, Kappa, Mean_F1, and FWIoU in the presence of noisy datasets, and as the noise rate increases, each performance of UNET shows a decreasing trend while the performance of NRN-RSSEG decreases slowly and some performances show an increasing trend. At a noise rate of 0.5, the PA (−6.14%), MPA (−4.27%) Kappa (−8.55%), Mean_F1 (−5.11%), and FWIOU (−9.75%) of UNET degrade faster; while the PA (−2.51%), Kappa (−3.33%), and FWIoU of NRN-RSSEG (−3.26) degraded more slowly, MPA (+1.41) and Mean_F1 (+2.69%) showed an increasing trend. Furthermore, comparing the proposed model with the baseline method, the results demonstrate that the proposed NRN-RSSEG anti-noise framework can effectively help the current segmentation model to overcome the adverse effects of noisy label training.