Reviewerly/exHarmony huggingface dataset:
Fine-grained Traffic Classification Based on Improved Residual Convolutional Network in Software Defined Networks

As Internet traffic becomes increasingly complex, a growing number of applications are carrying different services. To provides service-aware traffic classification, allowing network operators to better analyze network composition as well as manage and schedule efficiently network resources to facilitate sustainable development network, fine-grained traffic classification has become a crucial and challenging problem. Software defined networks has become the most promising new network architecture in the future with two major advantages of centralized control and programmability. Deep learning has also become a mainstream technology by its excellent feature extraction ability for large datasets. Thus the combination of software defined networks and deep learning can effectively solve the challenge of data set collection and feature extraction in the field of traffic classification. Inspired by research in computer vision, in this paper we propose an improved residual convolutional network approach to network traffic classification, which addresses the network degradation problem that occurs with increasing network depth in traditional deep learning methods for fine-grained network traffic identification, and can effectively learn deeper network features to achieve fine-grained network traffic classification. Experimental results show that the overall accuracy of the proposed method can reach 99.93%, which is about 4%, 13%, 7% and 2% higher than other state-of-the-art models such as classical residual networks ResNet-18, One-dimensional Convolutional Neural Networks (1D-CNN), Long Short-Term Memory (LSTM) and combined model CNN-LSTM respectively, thus verify the effectiveness of our method.
A Quantum-Safe Public-Key-Algorithms Approach with Lattice-Based Scheme

The most challenging application of post-quantum cryptography (PQC) is the distribution of provably secure cryptographic keys and quantum-safe algorithms. Public-key Encryption (PKE), Key-establishment Mechanisms (KEM), and Digital Signature Algorithms (DSA) are necessary cryptographic mechanisms to use common encryption communication through insecure or untrusted networks. This paper proposes a provably quantum-resilient secure key agreement mechanism based on the most promising lattice-based cryptographic scheme and the Module Learning-with-Rounding (MLWR) problem. The main contribution of this paper is to construct a modified framework on classical computers for study and test-bed purposes.
Large Deviation Principle for Random Permutations

Abstract We derive a large deviation principle for random permutations induced by probability measures of the unit square, called permutons. These permutations are called $\mu $-random permutations. We also introduce and study a new general class of models of random permutations, called Gibbs permutation models, which combines and generalizes $\mu $-random permutations and the celebrated Mallows model for permutations. Most of our results hold in the general setting of Gibbs permutation models. We apply the tools that we develop to the case of $\mu $-random permutations conditioned to have an atypical proportion of patterns. Several results are made more concrete in the specific case of inversions. For instance, we prove the existence of at least one phase transition for a generalized version of the Mallows model where the base measure is non-uniform. This is in contrast with the results of Starr (2009, 2018) on the (standard) Mallows model, where the absence of phase transition, that is, phase uniqueness, was proven. Our results naturally lead us to investigate a new notion of permutons, called conditionally constant permutons, which generalizes both pattern-avoiding and pattern-packing permutons. We describe some properties of conditionally constant permutons with respect to inversions. The study of conditionally constant permutons for general patterns seems to be a new challenging problem.
A Minimal “Functionally Sentient” Organism Trained With Backpropagation Through Time

This article presents a scenario where a simple simulated organism must explore and exploit an environment containing a food pile. The organism learns to make observations of the environment, use memory to record those observations, and thus plan and navigate to the regions with the strongest food density. We compare different reinforcement learning algorithms with an adaptive dynamic programming algorithm and conclude that backpropagation through time can convincingly solve this recurrent neural-network challenge. Furthermore, we argue that this algorithm successfully mimics a minimal ‘functionally sentient’ organism’s fundamental objectives and mental environmental-mapping skills while seeking a food pile distributed statically or randomly in an environment.
Active learning with feature matching for clinical named entity recognition

Active learning (AL) methods for Named Entity Recognition (NER) perform best when train and test data are drawn from the same distribution. However, only limited research in active learning has considered how to leverage the similarity between train and test data distributions. This is especially critical for clinical NER due to the rare concept (e.g., Symptoms) issue. Therefore, in this paper we present a novel AL method for clinical NER that selects the most beneficial instances for training by comparing train and test data distributions via low computational cost similarity metrics. When using GloVe embeddings, our method outperforms baseline AL methods by up to 11% in terms of reduction of training data required to reach the best performance of a target NER model. In addition, our method outperforms the baselines by a high margin in the first 20 iterations. The average margin is 12.15% and 12.75% on ShARe/CLEF 2013 and i2b2/VA 2010 respectively. When using BioBERT embeddings, our method outperforms baseline AL methods by up to 16% in terms of reduction of training data required to reach the target NER model performance.
Parametric Symmetries in Architectures Involving Indefinite Causal Order and Path Superposition for Quantum Parameter Estimation of Pauli Channels

Parameter estimation for devices containing or supporting quantum systems is a field of quantum metrology using quantum probe states to reach their characterization. Pauli channels are ideal structures where qubits are transmitted or contained, commonly altering them with specific fingerprints. The ultimate limit imposed on such estimation is addressed using the quantum Fisher information, stating a lower bound for it. Although the most simple scheme suggests performing such an estimation directly using the individual channel, other approaches have shown improved outcomes by repeating identical copies of the channel for the characterization, or otherwise those connected inside of specific circuit arrangements. These connections commonly include path superposition or causal indefinite architectures. In addition, other improvements have been observed in concrete channels when complementary unitary controls are included. The current research analyses the complete set of Pauli channels under some of those architectures in a comparative approach to reach a better estimation, thus stating hierarchies. It is observed that the use of those unitary controls notably improves previous outcomes by several orders of magnitude.
Real-Time Multi-Vehicle Multi-Camera Tracking with Graph-Based Tracklet Features

An essential application in intelligent transportation systems is multi-target multi-camera tracking (MTMCT), where the target’s activity is tracked from different cameras. Although the tracking-by-detection scheme is the primary paradigm in MTMCT, the object association information from the video frames is lost. This is mainly because the multi-camera multi-object matching uses the information from the video frames separately. To solve this problem and leverage this association information, we propose an MTMCT framework, where features are built in the form of a graph and a graph similarity algorithm is used to match multi-camera objects. In this paper, we focus on the real-time scenario, where only the past images are used to match an object. Our method achieves an IDF1 score (the ratio of the number of correctly identified objects to the number of ground truth and average objects) of 0.75 with a rate of 14 frames per second (fps).
An empirical hybridized Siamese network using hypercube natural aggregation algorithm for handling imbalance data learning

Abstract Dealing with imbalanced data is a common challenge in machine learning, where one class has significantly fewer examples than another. Successfully addressing this challenge requires careful consideration of the data, algorithm, and evaluation metrics to ensure that the model accurately predicts the minority class. In this study, we present a hybrid approach called Siamese‐HYNAA, which combines a Siamese network and a population‐based optimizer hypercube natural aggregation algorithm (HYNAA) to generate candidate solutions for augmenting the minority class. We collected 10 imbalanced datasets ranging from 1.81 to 8.78 imbalanced ratios and built solution pairs based on correctly predicted candidate solutions using support vector machine (SVM). We then fed these solutions to the Siamese network, which employs a one‐shot learning approach to improve predictions with fewer candidate solutions. However, we found that SVM predicted only a small number of minority class samples accurately, prompting us to optimize the number of candidate solution pairs using HYNAA to generate more synthetic samples for the Siamese network. We evaluated our proposed strategy against basic SMOTE and our previous work, SMOTE‐PSOEV, using various performance measures, including ROC‐AUC learning curves, sensitivity, specificity, accuracy, Characteristic stability index, balanced accuracy, F1‐score, informedness, markedness, and execution time. Our results indicate that Siamese‐HYNAA generates promising results for imbalanced data.
DEEP LEARNING FOR SEGMENTATION AND COUNTING OF WHITE BLOOD CELLS IN CLINICAL DIAGNOSIS

In the field of cytopathology, the accurate identification and counting of white blood cells (WBCs) in blood smears is crucial for diagnosing various types of cancer. The process of manually detecting and segmenting these structures, however, can be challenging due to their variable morphologies and the presence of overlapping objects in the images. This makes manual detection time-consuming, labor-intensive, and prone to error, particularly for individuals without extensive experience in cytopathology. In this paper, a deep learning algorithm is developed based on a Mask R-CNN model and driven by a sub-algorithm called KOWN (Keep Only White Blood Cells with Nuclei) for WBC segmentation and counting. The proposed algorithm improves the accuracy of measurements compared to other rapidly growing deep learning works, providing maximum precision in detecting and counting WBCs in both low- and high-blood-cell-density images.
A Simple Logic of the Hide and Seek Game

We discuss a simple logic to describe one of our favourite games from childhood, hide and seek, and show how a simple addition of an equality constant to describe the winning condition of the seeker makes our logic undecidable. There are certain decidable fragments of first-order logic which behave in a similar fashion with respect to such a language extension, and we add a new modal variant to that class. We discuss the relative expressive power of the proposed logic in comparison to the standard modal counterparts. We prove that the model checking problem for the resulting logic is $$\textsf{P}$$ -complete. In addition, by exploring the connection with related product logics, we gain more insight towards having a better understanding of the subtleties of the proposed framework.
Non-linear processing with a surface acoustic wave reservoir computer

Reservoir computing is a neural network algorithm that reduces the training needed for a neural network to be function. Recently, reservoir computing has been implemented using MEMs devices with prevalent non-linear dynamics to perform non-linear processing tasks. While partially explored in the past, there has been renewed interest in using Surface Acoustic Wave devices as low energy radio-frequency processors. However they have yet to be explored in the reservoir computing framework. In this work, a 39.16 MHz two-port SAW resonator on chemically reduced YZ Lithium Niobate is design and measured. The quality factor, insertion loss, linear transmission, and non-linear transmission of the devices is measured, and the relationship of these properties to reservoir computing is discussed. The SAW resonator is then configured as a time-multiplexed reservoir, and it’s non-linear processing capabilities are discussed using the time-delayed parity benchmark.
A Bayesian belief network-based analytics methodology for early-stage risk detection of novel diseases

During a pandemic, medical specialists have substantial challenges in discovering and validating new disease risk factors and designing effective treatment strategies. Traditionally, this approach entails several clinical studies and trials that might last several years, during which strict preventive measures are enforced to manage the outbreak and limit the death toll. Advanced data analytics technologies, on the other hand, could be utilized to monitor and expedite the procedure. This research integrates evolutionary search algorithms, Bayesian belief networks, and innovative interpretation techniques to provide a comprehensive exploratory-descriptive-explanatory machine learning methodology to assist clinical decision-makers in responding promptly to pandemic scenarios. The proposed approach is illustrated through a case study in which the survival of COVID-19 patients is determined using inpatient and emergency department (ED) encounters from a real-world electronic health record database. Following an exploratory phase in which genetic algorithms are used to identify a set of the most critical chronic risk factors and their validation using descriptive tools based on the concept of Bayesian Belief Nets, the framework develops and trains a probabilistic graphical model to explain and predict patient survival (with an AUC of 0.92). Finally, a publicly available online, probabilistic decision support inference simulator was constructed to facilitate what-if analysis and aid general users and healthcare professionals in interpreting model findings. The results widely corroborate intensive and expensive clinical trial research assessments.
Improving the planarity and sharpness of monocularly estimated depth images using the Phong reflection model

Depth estimation based on a single RGB image (a.k.a. monocular depth estimation) is a challenging task, with applications in robotics, autonomous vehicles, and other areas. With the advance of deep learning, several monocular depth estimation approaches have emerged with remarkable results. However, it is still possible to observe deficiencies in the depth maps generated by current techniques. We present a deep neural network that takes the result produced by some existing monocular depth estimation approach and enhances it by adding the details that the depth map requires to be sharp. We train the proposed Depth Enhancer Neural Network (DENN) using a new loss function that compares the input color image of the scene to the color image produced by rendering it using the Phong reflection model. Our experiments show a clear visual improvement in the sharpness of the depth image produced by the DENN, leading to edge enhancement and regularity of planar surfaces without compromising non-planar objects. On average, the standard metrics show a 5% reduction in error when considering the quantitative results. This reduction is dependent on the initial monocular depth estimation technique used by DENN to train the model.
The induction into the profession of teaching. From an appraisal of the training requirements of teachers to the guiding concepts and strategies

This article provides an analysis of the distinct training requirements for teachers during their first year of teaching and the origins of professional fulfilment at the outset of their teaching profession. The objective is to derive fundamental principles and actionable guidelines that can be incorporated into an induction mentoring model. The provision of mentorship by an experienced and knowledgeable teacher with expertise in professional counselling is crucial for beginning teachers in order to facilitate quality instruction, efficient classroom management, and successful integration into the educational community. The chosen approach for this study is qualitative research methodology. The process of conducting a needs analysis involved the selection of first-year teachers who were the recipients of an induction programme, as well as induction mentor teachers from three counties, namely Argeș, Sibiu, and Alba. The research was conducted at the onset of the programme, serving as a pilot study aimed at providing assistance to beginning teachers during their first year of teaching through a remote induction mentoring programme. The study adopted a survey methodology utilising a questionnaire to investigate the requirements of beginning teachers in their first year of teaching.
Digital identity performance through emoji on the social media platform Instagram

Introduction Emoji are omnipresent features of digital discourses and have become almost inherent features of online interactions. Given the large and ever-increasing variety of emoji, it is not farfetched to assume that users will have preferences for particular emoji or emoji combinations, and that their preferences are indexically related to their identities, for example in terms of skin-tone and/or gender. With a case study, the present paper aims to address a particular gap in the extant literature, namely the role of emoji in the presentation of different identities of one individual, namely a US American female in her early 20s. Methods The data for this study was drawn from Instagram and comprises a total of 625 posts made by one user to three of her own accounts with the platform. Each account represents a different aspect of her identity, and the analysis demonstrates that while the use of some emoji remains stable across all of her accounts, other emoji are very account-specific in that they are employed to highlight one particular aspect of her identity. Results The results thus indicate the different values that emoji might have in the contexts of authorship analyses. Additional studies are required to investigate this phenomenon further.
Role of Integrated Magnetics and Geology in Tracking and Exploring Complex Structures Controlling Gold Mineralization. Example from the Fawakheir-Atalla Gold Prospects, Eastern Desert, Egypt

Abstract Gold mineralisation is spatially and chronologically correlated with fault/shear zones in many of the world-class gold mines. Hence, the indirect exploration for Au mineralisation is linked with shearing and complex structures in many areas. Hence, in general, the need for a rapid, effective and new technology for gold exploration that reflects the structure set-up, shear zones, faults and related structural elements is crucial in the gold industry. In this article, we present an example of an integrated approach to gold exploration in the Fawakheir-Attala gold mining prospect in the eastern desert of Egypt. Remote sensing is used to test for rock differentiation; intensive field geological investigations were conducted along several traverses. Petrographic and geochemical analysis of selected samples confirmed Au content in some localities. Moreover, magnetic methods are used extensively (either aeromagnetic or measured land profiles) to investigate the magnetic signature of the different reported rock units and their relationship with gold occurrences and deposits. Normalised source strength transformation, magnitude magnetic transforms and subsurface modelling are used to explore the inherent relation between the surface and subsurface magnetic susceptibilities. The magnetic signature of the talc-carbonate rocks is determined. The gradational contact against the serpentinite is explained. Because three current Au mines are associated with contacts/fractures, the clear relation between the contacts/fractures and the magnetic data and the comparison with the contact occurrence density COD (heat) magnetic filtered map, the present analysis workflow can now be used to suggest new locations for Au occurrences.
Robust Feature-Guided Generative Adversarial Network for Aerial Image Semantic Segmentation against Backdoor Attacks

Profiting from the powerful feature extraction and representation capabilities of deep learning (DL), aerial image semantic segmentation based on deep neural networks (DNNs) has achieved remarkable success in recent years. Nevertheless, the security and robustness of DNNs deserve attention when dealing with safety-critical earth observation tasks. As a typical attack pattern in adversarial machine learning (AML), backdoor attacks intend to embed hidden triggers in DNNs by poisoning training data. The attacked DNNs behave normally on benign samples, but when the hidden trigger is activated, its prediction is modified to a specified target label. In this article, we systematically assess the threat of backdoor attacks to aerial image semantic segmentation tasks. To defend against backdoor attacks and maintain better semantic segmentation accuracy, we construct a novel robust generative adversarial network (RFGAN). Motivated by the sensitivity of human visual systems to global and edge information in images, RFGAN designs the robust global feature extractor (RobGF) and the robust edge feature extractor (RobEF) that force DNNs to learn global and edge features. Then, RFGAN uses robust global and edge features as guidance to obtain benign samples by the constructed generator, and the discriminator to obtain semantic segmentation results. Our method is the first attempt to address the backdoor threat to aerial image semantic segmentation by constructing the robust DNNs model architecture. Extensive experiments on real-world scenes aerial image benchmark datasets demonstrate that the constructed RFGAN can effectively defend against backdoor attacks and achieve better semantic segmentation results compared with the existing state-of-the-art methods.
Path planning of a mobile robot using an improved mixed-method of potential field and wall following

The existing Bug algorithms, which are the same as wall-following algorithms, offer good performance in solving local minimum problems caused by potential fields. However, because of the odometer drift that occurs in actual environments, the performance of the paths planned by these algorithms is significantly worse in actual environments than in simulated environments. To address this issue, this article proposes a new Bug algorithm. The proposed algorithm contains a potential field function that is based on the relative velocity, which enables the potential field method to be extended to dynamic scenarios. Using the cumulative changes in the internal and external angles and the reset point of the robot during the wall-following process, the condition for state switching has been redesigned. This improvement not only solves the problem of position estimation deviation caused by odometer noise but also enhances the decision-making ability of the robot. The simulation results demonstrate that the proposed algorithm is simpler and more efficient than existing wall-following algorithms and can realise path planning in an unknown dynamic environment. The experimental results for the Kobuki robot further validate the effectiveness of the proposed algorithm.