Reviewerly/exHarmony huggingface dataset:
Bilevel optimal parameter learning for a high-order nonlocal multiframe super-resolution problem

Abstract This work elaborated an improved method to multiframe super-resolution (SR), which involves a nonlocal first-order regularization combined with a nonlocal p-Laplacian term. The nonlocal TV term excels at edge preserving, whilst the nonlocal p-Laplacian is commonly used to perfectly reconstruct image textures. Firstly, we discuss the existence and uniqueness of a solution to our new model in a well posed framework. Then, we derive a modified Primal-dual iteration to compute the super-resolved solution. Furthermore, we introduce a new bilevel optimization approach to learn two regularization parameters. The included tests validate that the introduced optimization procedure performs favorably compared to numerous SR approaches in terms of efficiency and accuracy.
Leveraging Fuzzy Logic Towards More Explainable Reinforcement Learning-Induced Pedagogical Policies on Intelligent Tutoring Systems

Deep Reinforcement Learning (Deep RL) has revolutionized the field of Intelligent Tutoring Systems by providing effective pedagogical policies. However, the “black box” nature of Deep RL models makes it challenging to understand these policies. This study tackles this challenge by applying fuzzy logic to distill knowledge from Deep RL-induced policies into interpretable IF-THEN Fuzzy Logic Controller (FLC) rules. Our experiments show that these FLC policies significantly outperform expert policy and student decisions, demonstrating the effectiveness of our approach. We propose a Temporal Granule Pattern (TGP) mining algorithm to increase the FLC rules' interpretability further. This work highlights the potential of fuzzy logic and TGP analysis to enhance understanding of Deep RL-induced pedagogical policies.
Latent Space Encoding for Interpretable Fuzzy Logic Rules in Continuous and Noisy High-Dimensional Spaces

This study introduces a general approach for generating fuzzy logic rules in regression tasks with complex, high-dimensional input spaces. The method leverages the power of encoding data into a latent space, where its uniqueness is analyzed to determine whether it merits the distinction of becoming a noteworthy exemplar. The efficacy of the proposed method is showcased through its application in predicting the acceleration of one of the links for the Unimation Puma 560 robot arm, effectively overcoming the challenges posed by non-linearity and noise in the dataset.
NoiseCAM: Explainable AI for the Boundary Between Noise and Adversarial Attacks

Deep Learning (DL) and Deep Neural Networks (DNNs) are widely used in various domains. However, adversarial attacks can easily mislead a neural network and lead to wrong decisions. Defense mechanisms are highly preferred in safety- critical applications. In this paper, firstly, we use the gradient class activation map (GradCAM) to analyze the behavior deviation of the VGG-16 network when its inputs are mixed with adversarial perturbation or Gaussian noise. In particular, our method can locate vulnerable layers that are sensitive to adversarial perturbation and Gaussian noise. We also show that the behavior deviation of vulnerable layers can be used to detect adversarial examples. Secondly, we propose a novel NoiseCAM algorithm that integrates information from globally and pixel- level weighted class activation maps. Our algorithm is highly sensitive to adversarial perturbations and will not respond to Gaussian random noise mixed in the inputs. Third, we compare detecting adversarial examples using both behavior deviation and NoiseCAM, and we show that NoiseCAM outperforms behavior deviation modeling in its overall performance. Our work could provide a useful tool to defend against certain types of adversarial attacks on deep neural networks.
Decomposing Conventional Fuzzy Logic Systems to Hierarchical Fuzzy Systems

Hierarchical Fuzzy Systems (HFSs) have been viewed as a promising option to overcoming a fundamental problem in Fuzzy Logic Systems (FLSs), namely the rule explosion associated with an increase in input variables. In HFSs, the original FLS is decomposed into a number of low-dimensional fuzzy logic subsystems. As a result, rules in HFSs typically have antecedents with fewer variables than rules in FLSs which compute similar function mappings, given that the number of input variables of each subsystem is smaller. Consequently, HFSs tend to limit rule explosion, lowering complexity and enhancing model interpretability. However, developing the HFSs is difficult due to the added issue of designing suitable architecture (i.e., various subsystems, levels, topologies, and subsystem interactions) and rules for each subsystem. In fact, decomposing conventional fuzzy system is a challenging task. The difficulties include: “How to select the input variable for each subsystem”, “How to improve the meaning of intermediate variable?”, “How to link all the subsystems in HFSs?”, and “How to design the rules for each subsystem?” Hence, this paper presents a method to convert conventional FLSs to hierarchical fuzzy systems using two key steps. This method contributes to the process or guidelines in overcoming the difficulties in the decomposition of FLS to HFS.
Scale-Aware Graph Convolutional Network for Fine-Grained Image Classification

Fine-grained Image Classification (FGIC) is a hot research topic in computer vision. Currently, FGIC faces several challenges, such as similar appearances, cluttered backgrounds, and pose variations. To effectively address these challenges, we propose a framework called Scale-Aware Graph Convolutional Network (SAGCN) to capture subtle differences in images. Leveraging the characteristics of fine-grained images, we design two core modules, namely Scale-Aware Selection Module (SASM) and Spatial Semantic Correlation Module (SSCM). SASM aggregates multi-scale information of fine-grained images by fusing features from multiple layers. SSCM establishes semantic-spatial relationships by propagating information among different parts of the fine-grained image. Furthermore, we propose a Pairwise Appearance Similarity Loss (PAS-Loss) to distinguish easily confused categories. Extensive experiments demonstrate that our method achieves state-of-the-art results on benchmark datasets.
Autonomous Networks with Multi-Layer, Intent-Based Operation: A truly autonomous network has the ability to change requirementsdynamically without human involvement. Creeting a network that can dothis requires the use of intents between multiple operational domains of thenetwork architecture, including the business, service and resource layers

As consumer expectations continue to rise and networks grow ever more complex, communication service providers (CSPs) around the world are becoming increasingly aware of the need for intent-based autonomous networks that significantly reduce the need for human intervention.
Analog image processing with nonlinear nonlocal flat optics

Digital signal processing has revolutionized many fields of science and engineering, but it still shows critical limits, mainly related to the complexity, power consumption, and limited speed of analogue-to-digital converters. A long-sought solution to overcome these hurdles is optical analog computing. In this regard, flat optics has been recently unveiled as a powerful platform to perform data processing in real-time, with low power consumption and a small footprint. So far, these explorations have been mainly limited to linear optics. Arguably, significantly more impact may be garnered from pushing this operation towards nonlinear processing of the incoming signals. In this context, we demonstrate here that nonlinear phenomena combined with engineered nonlocality in flat optics devices can be leveraged to synthesize Volterra kernels able to outperform linear optical analog image processing.
An interpolation-based reversible data hiding scheme for internet of things applications

Abstract The exponential growth of the Internet of Things (IoT) has led to an increased demand for secure and efficient data transmission methods. However, there is a tradeoff in the image quality and hiding capacity in the data hiding methods. Therefore, the maximum amount of data that could be stored in the image media is a difficult challenge while maintaining the image quality. Thus, to make the balance between the quality of the images and the embedding capacity, a novel interpolation-based revisable data hiding (RDH) approach is developed for IoT applications. The proposed interpolation technique takes the average of the root value for the product of two neighboring original pixel values and the third original pixel value. And for the central pixel, it takes an average of two interpolated pixels. By doing so, most of the original pixels are considered and the calculated interpolated pixel is much enhanced as its average value. Furthermore, the data hiding is performed in two stages. In the first stage, RSA is performed on the secret message, and then embedding is done based on which intensity range group. The experimental results indicate that the proposed technique enhanced the embedding capacity by 17.58% and produced 7.80% higher PSNR values for the test images as compared to the baseline methods.
TRansPose: Large-scale multispectral dataset for transparent object

Transparent objects are encountered frequently in our daily lives, yet recognizing them poses challenges for conventional vision sensors due to their unique material properties, not being well perceived from RGB or depth cameras. Overcoming this limitation, thermal infrared cameras have emerged as a solution, offering improved visibility and shape information for transparent objects. In this paper, we present TRansPose, the first large-scale multispectral dataset that combines stereo RGB-D, thermal infrared (TIR) images, and object poses to promote transparent object research. The dataset includes 99 transparent objects, encompassing 43 household items, 27 recyclable trashes, 29 chemical laboratory equivalents, and 12 non-transparent objects. It comprises a vast collection of 333,819 images and 4,000,056 annotations, providing instance-level segmentation masks, ground-truth poses, and completed depth information. The data was acquired using an FLIR A65 thermal infrared camera, two Intel RealSense L515 RGB-D cameras, and a Franka Emika Panda robot manipulator. Spanning 87 sequences, TRansPose covers various challenging real-life scenarios, including objects filled with water, diverse lighting conditions, heavy clutter, non-transparent or translucent containers, objects in plastic bags, and multi-stacked objects. Supplementary material can be accessed from the following link: https://sites.google.com/view/transpose-dataset .
A Lightweight Human Fall Detection Network

The rising issue of an aging population has intensified the focus on the health concerns of the elderly. Among these concerns, falls have emerged as a predominant health threat for this demographic. The YOLOv5 family represents the forefront of techniques for human fall detection. However, this algorithm, although advanced, grapples with issues such as computational demands, challenges in hardware integration, and vulnerability to occlusions in the designated target group. To address these limitations, we introduce a pioneering lightweight approach named CGNS-YOLO for human fall detection. Our method incorporates both the GSConv module and the GDCN module to reconfigure the neck network of YOLOv5s. The objective behind this modification is to diminish the model size, curtail floating-point computations during feature channel fusion, and bolster feature extraction efficacy, thereby enhancing hardware adaptability. We also integrate a normalization-based attention module (NAM) into the framework, which concentrates on salient fall-related data and deemphasizes less pertinent information. This strategic refinement augments the algorithm's precision. By embedding the SCYLLA Intersection over Union (SIoU) loss function, our model benefits from faster convergence and heightened detection precision. We evaluated our model using the Multicam dataset and the Le2i Fall Detection dataset. Our findings indicate a 1.2% enhancement in detection accuracy compared with the conventional YOLOv5s framework. Notably, our model realized a 20.3% decrease in parameter tally and a 29.6% drop in floating-point operations. A comprehensive instance analysis and comparative assessments underscore the method's superiority and efficacy.
Measurement-free fault-tolerant logical-zero-state encoding of the distance-three nine-qubit surface code in a one-dimensional qubit array

Generation of logical zero states encoded with a quantum error-correcting code is the first step for fault-tolerant quantum computation but requires considerably large resource overheads in general. To reduce such overheads, we propose an efficient encoding method for the distance-three, nine-qubit surface code and show its fault tolerance. This method needs no measurement, unlike other fault-tolerant encoding methods. Moreover, this is applicable to a one-dimensional qubit array. Observing these facts, we experimentally demonstrate the logical-zero-state encoding of the surface code using a superconducting quantum computer on the cloud. We also experimentally demonstrate the suppression of fast dephasing due to intrinsic residual interactions in this machine by a dynamical decoupling technique dedicated to the qubit array. To extend this method to larger codes, we also investigate the concatenation of the surface code with itself, resulting in a distance-nine, 81-qubit code. We numerically show that fault-tolerant encoding of this large code can be achieved by appropriate error detection.
Generating Synthetic Resume Data with Large Language Models for Enhanced Job Description Classification

In this article, we investigate the potential of synthetic resumes as a means for the rapid generation of training data and their effectiveness in data augmentation, especially in categories marked by sparse samples. The widespread implementation of machine learning algorithms in natural language processing (NLP) has notably streamlined the resume classification process, delivering time and cost efficiencies for hiring organizations. However, the performance of these algorithms depends on the abundance of training data. While selecting the right model architecture is essential, it is also crucial to ensure the availability of a robust, well-curated dataset. For many categories in the job market, data sparsity remains a challenge. To deal with this challenge, we employed the OpenAI API to generate both structured and unstructured resumes tailored to specific criteria. These synthetically generated resumes were cleaned, preprocessed and then utilized to train two distinct models: a transformer model (BERT) and a feedforward neural network (FFNN) that incorporated Universal Sentence Encoder 4 (USE4) embeddings. While both models were evaluated on the multiclass classification task of resumes, when trained on an augmented dataset containing 60 percent real data (from Indeed website) and 40 percent synthetic data from ChatGPT, the transformer model presented exceptional accuracy. The FFNN, albeit predictably, achieved lower accuracy. These findings highlight the value of augmented real-world data with ChatGPT-generated synthetic resumes, especially in the context of limited training data. The suitability of the BERT model for such classification tasks further reinforces this narrative.
Studying negative evidence in Finnish language corpora

This study explores the relationship between lower-than-expected frequencies of word forms and inherent gaps in Finnish inflectional paradigms. The research aims to determine whether it is possible to predict paradigmatic gaps from lower-than-expected frequencies of word forms. We examined Finnish nouns inflected in a marginal case (the instructive) and hypothesized that some of these nouns may potentially have gaps in their inflectional paradigms. However, we found that such gaps are contingent and do not cause uncertainty when filled. We find that the correlation between inherent gaps and lower frequencies is one-directional: predicting inherent gaps from lower-than-expected frequencies is problematic. The results suggest that any paradigmatic gap suggested by corpus frequency is more likely to be contingent than inherent, and that the less semantic need there is for a particular word form, the more likely it will be unattested even in a large corpus. The research highlights the importance of considering semantic profiles when analyzing the grammaticality of word forms and suggests that statistical tests like Fisher’s exact are not necessarily the right approach to tackle the problem of negative evidence in corpus studies.
Realised overabundance in Estonian noun paradigms: A corpus study

In this paper we investigate the use of parallel forms, or morphological overabundance, in Estonian nouns, which are richly inflected in fourteen cases and two numbers. The complexity of the nominal inflection system derives from the multiplicity of declension classes, which allow for the availability of parallel forms in many morphological paradigm cells. Whereas previous studies of overabundance in nominal inflection have focussed on available overabundance in particular cells, this study focuses on the actual usage of overabundant forms, or ‘realised overabundance’, taking as a starting point those lexemes which are attested with two parallel forms in the corpus. Following the criteria proposed by Thornton (2019) , we investigate the canonicity of realised overabundance in Estonian, in terms of its extent across both lexemes and cells. In addition, frequency ratios of alternate variants and conditions on their usage are investigated by comparing lexemes in the most overabundant cells. Finally, we tackle the connections between morphological overabundance and syntactic argumenthood, exploring the hypothesis that parallel forms serve distinct functions. We find no contextual preference in terms of which of the two forms is used. In general, we find realised overabundance to be restricted and more canonical than potential overabundance.
Singly-excited resonant open quantum system Tavis-Cummings model with quantum circuit mapping

Abstract Tavis-Cummings (TC) cavity quantum electrodynamical effects, describing the interaction of N atoms with an optical resonator, are at the core of atomic, optical and solid state physics. The full numerical simulation of TC dynamics scales exponentially with the number of atoms. By restricting the open quantum system to a single excitation, typical of experimental realizations in quantum optics, we analytically solve the TC model with an arbitrary number of atoms with linear complexity. This solution allows us to devise the Quantum Mapping Algorithm of Resonator Interaction with N Atoms (Q-MARINA), an intuitive TC mapping to a quantum circuit with linear space and time scaling, whose N +1 qubits represent atoms and a lossy cavity, while the dynamics is encoded through 2 N entangling gates. Finally, we benchmark the robustness of the algorithm on a quantum simulator and superconducting quantum processors against the quantum master equation solution on a classical computer.
Unmanned Aerial Vehicle Path-Planning Method Based on Improved P-RRT* Algorithm

This paper proposed an improved potential rapidly exploring random tree star (P-RRT*) algorithm for unmanned aerial vehicles (UAV). The algorithm has faster expansion and convergence speeds and better path quality. Path planning is an important part of the UAV control system. Rapidly exploring random tree (RRT) is a path-planning algorithm that is widely used, including in UAV, and its altered body, P-RRT*, is an asymptotic optimal algorithm with bias sampling. The algorithm converges slowly and has a large random sampling area. To overcome the above drawbacks, we made the following improvements. First, the algorithm used the direction of the artificial potential field (APF) to determine whether to perform greedy expansion, increasing the search efficiency. Second, as the random tree obtained the initial path and updated the path cost, the algorithm rejected high-cost nodes and sampling points based on the heuristic cost and current path cost to speed up the convergence rate. Then, the random tree was pruned to remove the redundant nodes in the path. The simulation results demonstrated that the proposed algorithm could significantly decrease the path cost and inflection points, speed up initial path obtaining and convergence, and is suitable for the path planning of UAVs.
Performance and perception: machine translation post-editing in Chinese-English news translation by novice translators

Abstract Machine translation has become a popular option for news circulation, due to its speed, cost-effectiveness and improving quality. However, it still remains uncertain whether machine translation is effective in helping novice translators in news translation. To investigate the effectiveness of machine translation, this study conducted a Chinese-English news translation test to compare the performance and perception of translation learners in machine translation post-editing and manual translation. The findings suggest that it is challenging for machine translation to understand cultural and semantic nuances in the source language, and produce coherent structural translation in the target language. No significant quality difference was observed in post-editing and manual translation, though post-editing quality was found to be slightly better. Machine translation can help to reduce translation learners’ processing time and mental workload. Compared to manual translation, machine translation post-editing is considered as a preferred approach by translation learners in news translation. It is hoped that this study could cast light on the integration of machine translation into translator training programs.
Implementasi sistem pengelolaan dana Bantuan Operasional Sekolah (BOS) berdasarkan Permendikbud Nomor 6 Tahun 2021 di SD Inpres Nanas Kecamatan Wasile Selatan Kabupaten Halmahera Timur

School Operational Assistance Funds are funds provided by the government to schools. Financial management must be accounted for with transparency and accountability in accordance with BOS principles. This research aims to analyze in more depth the implementation of the School Operational Assistance (BOS) fund management system implemented by SD Inpres Nanas, South Wasile District, East Halmahera Regency Based on PERMENDIKBUD No. 6 of 2021 concerning Technical Guidelines for Managing School Operational Assistance Funds. The type of research used is qualitative with a descriptive approach. The results of research regarding the management of School Operational Assistance (BOS) funds at SD Inpres Nanas, South Wasile District regarding implementation and reporting have been carried out well in accordance with the Technical Instructions for PERMENDIKBUD No. 6 of 2021. However, planning has not been in accordance with the Technical Instructions for PERMENDIKBUD No. 6 of 2021. 2021 because the preparation of the school budget or RKAS has not been carried out in a transparent manner without involving parents and guardians of students, and the school has not published a joint decision regarding the preparation of the RKAS