Reviewerly/exHarmony huggingface dataset:
A Fine-Grained Visual Attention Approach for Fingerspelling Recognition in the Wild

Fingerspelling in sign language has been the means of communicating technical terms and proper nouns when they do not have dedicated sign language gestures. Automatic recognition of fingerspelling can help resolve communication barriers when interacting with deaf people. The main challenges prevalent in fingerspelling recognition are the ambiguity in the gestures and strong articulation of the hands. The automatic recognition model should address high inter-class visual similarity and high intra-class variation in the gestures. Most of the existing research in fingerspelling recognition has focused on the dataset collected in a controlled environment. The recent collection of a large-scale annotated fingerspelling dataset in the wild, from social media and online platforms, captures the challenges in a real-world scenario. In this work, we propose a fine-grained visual attention mechanism using the Transformer model for the sequence-to-sequence prediction task in the wild dataset. The fine-grained attention is achieved by utilizing the change in motion of the video frames (optical flow) in sequential context-based attention along with a Transformer encoder model. The unsegmented continuous video dataset is jointly trained by balancing the Connectionist Temporal Classification (CTC) loss and the maximum-entropy loss. The proposed approach can capture better fine-grained attention in a single iteration. Experiment evaluations show that it outperforms the state-of-the-art approaches.
A cognitive category-learning model of rule abstraction, attention learning, and contextual modulation.

We introduce the Category Abstraction Learning (CAL) model, a cognitive framework formally describing category learning built on similarity-based generalization, dissimilarity-based abstraction, two attention learning mechanisms, error-driven knowledge structuring, and stimulus memorization. Our hypotheses draw on an array of empirical and theoretical insights connecting reinforcement and category learning. The key novelty of the model is its explanation of how rules are learned from scratch based on three central assumptions. (a) Category rules emerge from two processes of stimulus generalization (similarity) and its direct inverse (category contrast) on independent dimensions. (b) Two attention mechanisms guide learning by focusing on rules, or on the contexts in which they produce errors. (c) Knowing about these contexts inhibits executing the rule, without correcting it, and consequently leads to applying partial rules in different situations. The model is designed to capture both systematic and individual differences in a broad range of learning paradigms. We illustrate the model's explanatory scope by simulating several benchmarks, including the classic Six Problems, the 5-4 problem, and linear separability. Beyond the common approach of predicting average response probabilities, we also propose explanations for more recently studied phenomena that challenge existing learning accounts, regarding task instructions, individual differences in rule extrapolation in three different tasks, individual attention shifts to stimulus features during learning, and other phenomena. We discuss CAL's relation to different models, and its potential to measure the cognitive processes regarding attention, abstraction, error detection, and memorization from multiple psychological perspectives. (PsycInfo Database Record (c) 2023 APA, all rights reserved).
An Electronic training program to treat errors of reading aloud in the English language among students with learning difficulties during the emerging Covid-19

This study aims at identifying, analysing, and classifying the mistakes that students make in grades (second, third, and fourth) in the elementary stage in Aseer in reading aloud and developing a proposed program to treat these errors. The study used the experimental approach. The study sample consisted of (72) male and female students from the study population, representing the three grade levels. 24 students (12 males and 12 females) were taken from each grade, and the study sample was taken from seven schools. The results of the study showed that there are no statistically significant differences in students ’reading errors due to the gender variable. The prevalence of reading errors in students’ readings is not related to males or females or vice versa. Despite the difference between the reader's gender and the teacher’s gender, the differences came free of any clear statistical significance. The study revealed statistically significant differences in students’ mastery of some reading skills due to the gender variable. The skills that showed differences are observing punctuation, meaning skills, and reading speed. Females were better than males in these skills, and there were no statistically significant differences in the remaining reading skills between the genders. The study recommended preparing training courses to familiarize male and female teachers with the latest findings of studies related to reading to employ them in educational situations and providing teachers with methods to diagnose students' weakness in reading.
Evaluation of College English Teaching Quality Based on Particle Swarm Optimization Algorithm

Teaching quality evaluation is an important part of teaching management. It is an important tool to improve teaching quality and school operation efficiency. Carrying out quality evaluation with teachers' classroom teaching as the object and students, experts and peers as the main body is the key link in the implementation of quality evaluation. This paper aims to study the evaluation of college English teaching quality based on particle swarm first algorithm. Based on the particle optimization algorithm, this paper divides the factors that affect the quality of university teaching into "causal" two types. Based on the evaluation of the entire system, combined with the application of analytic hierarchy process, various factors are revised layer by layer according to the status and degree of influence on the system. The weight can reduce the subjective level of the statistical average method; at the same time, the quality of teaching is a Comprehensive indicators, introducing the concept of evaluation indicators to provide new ideas for college teaching quality evaluation. The experimental results of this paper show that this paper introduces the particle swarm optimization algorithm to improve it. Through experiments and comparisons, it proves that the constructed model is accurate for teaching quality evaluation and has good practical significance in improving teaching management.
Multiple Level Action Embedding for Penetration Testing

Penetration Testing (PT) is one of the most effective and widely used methods to increase the defence of a system by looking for potential vulnerabilities. Reinforcement learning (RL), a powerful type of machine learning in self-decision making, is demonstrated to be applicable in PT to increase automation as well as reduce implementation costs. However, RL algorithms are still having difficulty on PT problems which have large network size and high complexity. This paper proposes a multiple level action embedding applied with Wolpertinger architect (WA) to enhance the accuracy and performance of the RL, especially in large and complicated environments. The main purpose of the action embedding is to be able to represent the elements in the RL action space as an n-dimensional vector while preserving their properties and accurately representing the relationship between them. Experiments are conducted to evaluate the logical accuracy of the action embedding. The deep Q-network algorithm is also used as a baseline for comparing with WA using the multiple level action embedding.
On the Relationship between Generalization and Robustness to Adversarial Examples

One of the most intriguing phenomenons related to deep learning is the so-called adversarial examples. These samples are visually equivalent to normal inputs, undetectable for humans, yet they cause the networks to output wrong results. The phenomenon can be framed as a symmetry/asymmetry problem, whereby inputs to a neural network with a similar/symmetric appearance to regular images, produce an opposite/asymmetric output. Some researchers are focused on developing methods for generating adversarial examples, while others propose defense methods. In parallel, there is a growing interest in characterizing the phenomenon, which is also the focus of this paper. From some well known datasets of common images, like CIFAR-10 and STL-10, a neural network architecture is first trained in a normal regime, where training and validation performances increase, reaching generalization. Additionally, the same architectures and datasets are trained in an overfitting regime, where there is a growing disparity in training and validation performances. The behaviour of these two regimes against adversarial examples is then compared. From the results, we observe greater robustness to adversarial examples in the overfitting regime. We explain this simultaneous loss of generalization and gain in robustness to adversarial examples as another manifestation of the well-known fitting-generalization trade-off.
Dynamic epistemic logics for abstract argumentation

Abstract This paper introduces a multi-agent dynamic epistemic logic for abstract argumentation. Its main motivation is to build a general framework for modelling the dynamics of a debate, which entails reasoning about goals, beliefs, as well as policies of communication and information update by the participants. After locating our proposal and introducing the relevant tools from abstract argumentation, we proceed to build a three-tiered logical approach. At the first level, we use the language of propositional logic to encode states of a multi-agent debate. This language allows to specify which arguments any agent is aware of, as well as their subjective justification status. We then extend our language and semantics to that of epistemic logic, in order to model individuals’ beliefs about the state of the debate, which includes uncertainty about the information available to others. As a third step, we introduce a framework of dynamic epistemic logic and its semantics, which is essentially based on so-called event models with factual change. We provide completeness results for a number of systems and show how existing formalisms for argumentation dynamics and unquantified uncertainty can be reduced to their semantics. The resulting framework allows reasoning about subtle epistemic and argumentative updates—such as the effects of different levels of trust in a source—and more in general about the epistemic dimensions of strategic communication.
Revocable attribute-based proxy re-encryption

Abstract Attribute-based proxy re-encryption (ABPRE), which combines the notions of proxy re-encryption (PRE) and attribute-based encryption (ABE), allows a semi-trusted proxy with re-encryption key to transform a ciphertext under a particular access policy into a ciphertext under another access policy, without revealing any information about the underlying plaintext. This primitive is very useful in applications where encrypted data need to be stored in untrusted environments, such as cloud storage. In many practical applications, and in order to address scenarios where users misbehave or the re-encryption keys are compromised, an efficient revocation mechanism is necessary for ABPRE. Previously, revocation mechanism was considered in the settings of identity-based encryption (IBE), ABE, predicate encryption (PE), and broadcast PRE, but not ABPRE, which is what we set to do in this paper. We first formalize the concept of revocable ABPRE and its security model. Then, we propose a lattice-based instantiation of revocable ABPRE. Our scheme not only supports an efficient revocation mechanism but also supports polynomial-depth policy circuits and has short private keys, where the size of the keys is dependent only on the depth of the supported policy circuits. In addition, we prove that our scheme is selectively chosen-plaintext attack (CPA) secure in the standard model, based on the learning with errors assumption.
Assembler3: 3D Reconstruction of Laser-Cut Models

We present Assembler3 a software tool that allows users to perform 3D parametric manipulations on 2D laser cutting plans. Assembler3 achieves this by semi-automatically converting 2D laser cutting plans to 3D, where users modify their models using available 3D tools (kyub), before converting them back to 2D. In our user study, this workflow allowed users to modify models 10x faster than using the traditional approach of editing 2D cutting plans directly. Assembler3 converts models to 3D in 5 steps: (1) plate detection, (2) joint detection, (3) material thickness detection, (4) joint matching based on hashed joint "signatures", and (5) interactive reconstruction. In our technical evaluation, Assembler3 was able to reconstruct 100 of 105 models. Once 3D-reconstructed, we expect users to store and share their models in 3D, which can simplify collaboration and thereby empower the laser cutting community to create models of higher complexity.
Nod to Auth: Fluent AR/VR Authentication with User Head-Neck Modeling

With AR/VR devices becoming increasingly common around us, their user authentication has posed a critical challenge. While typing passwords is straightforward with a keyboard, it has been cumbersome with conventional AR/VR input techniques such as in-air gestures and hand-held controllers. In this work, we developed a fluent authentication technique that allows AR/VR users to unlock their profiles with simple head gestures (e.g., nodding). This resembles the powerful “Slide to Unlock” interaction on touch screen devices. Specifically, we extract bio-features such as neck length and head radius from IMU sensor readings of these head gestures for user identification with machine learning. Though our approach is less strict compared with conventional password-based methods, we believe its swiftness greatly facilitates scenarios with frequent user switching (e.g., device sharing across team and family members) which demand quick authentications. Through a 10-participant evaluation, we demonstrated that our system is robust and accurate with an average accuracy of 97.1% on groups of 5, simulating family and lab use.
Design of Multidimensional Classifiers using Fuzzy Brain Emotional Learning Model and Particle Swarm Optimization Algorithm

This study presents a multidimensional classifier design using a fuzzy brain emotional learning model, combined with a particle swarm optimization (PSO) algorithm that allows a network to automatically determine the optimum values for the weights of the reward signal.The multidimensional fuzzy brain emotional learning classifier(MFBELC) is first described with corresponding fuzzy inference rules; then the PSO algorithm is applied for the optimum parameter choice.This PSO-MFBELC is evaluated for the Wine dataset and Iris dataset, which are publicly available from the UCI machine learning database.A comparison of simulations using the proposed PSO-MFBELC shows that this classifier is superior to other algorithms in the recognition accuracy aspect.
FWB-Net: Front White Balance Network for Color Shift Correction in Single Image Dehazing Via Atmospheric Light Estimation

In recent years, single image dehazing deep models based on Atmospheric Scattering Model (ASM) have achieved remarkable results. But the dehazing outputs of those models suffer from color shift. Analyzing the ASM model shows that the atmospheric light factor (ALF) is set as a scalar which indicates ALF is constant for whole image. However, for images taken in real-world, the illumination is not uniformly distributed over whole image which brings model mismatch and possibly results in color shift of the deep models using ASM. Bearing this in mind, in this study, first, a new non-homogeneous atmospheric scattering model (NH-ASM) is proposed for improving image modeling of hazy images taken under complex illumination conditions. Second, a new U-Net based front white balance module (FWB-Module) is dedicatedly designed to correct color shift before generating dehazing result via atmospheric light estimation. Third, a new FWB loss is innovatively developed for training FWB-Module, which imposes penalty on color shift. In the end, based on NH-ASM and front white balance technology, an end-to-end CNN-based color-shift-restraining dehazing network is developed, termed as FWB-Net. Experimental results demonstrate the effectiveness and superiority of our proposed FWB-Net for dehazing on both synthetic and real-world images.
Blockchain informative infrastructure: a conceptual reflection on public administrative procedures and a citizen-centred view

Purpose This work highlights the potential of blockchain from a public management (PM) angle. Given the limited presence of blockchain's broad-spectrum, empirical-applicative evidence in the public sector, as well as theoretical systematisations that can exemplify its potential within this scope, the authors have concentrated their efforts on structuring a referring model. Design/methodology/approach After identifying a gap and conducting a preliminary literature review related to public administration (PA), the authors propose a paradigm focused on a conceptual synthesis methodology, starting from a longitudinal analysis and a coding activity that are able to structure a clear framework of theoretical parallelism regarding blockchain's main functionalities, as well as future perspectives in the public sector. Findings The main results yielded a specific longitudinal literature review and constitute a referring model of blockchain's systematised functionalities through a conceptualised matrix. The outcome of the conceptualisation process frames and systematises a rapidly growing controversial phenomenon, furnishing a specific referring paradigm for the issue as it relates to policymakers. Originality/value The study's originality resides in the two views the authors created, both from the literature review and from the conceptual synthesis for public sector operative practice via an anthropocentric lens, conveyed by a three-year range of analysis.
Machine vision-based relative-angle measurement system between circular holes

In this study, the machine-vision technique is employed to measure the angle between two circular holes for the derailleur parts of a bicycle. A delta robot equipped with a motorized zoom lens is constructed as an automatic measurement device. In this way, we can detect the normal vectors of circular holes with different positions and sizes by using only one camera. By calculating the plane normal vectors of two circular holes within the camera coordinate system and performing the cross-product operation, the angle between the two circular holes is obtained. Simulation experiments reveal that the measurement method is influenced by the projection eccentricity error, and the error can be reduced by increasing the focal length and employing the virtual-center alignment method. The results show that the average error for measuring the derailleur parts is approximately 0.061°, and the standard deviation is 0.112°. Experimental analysis indicates that while the magnification of the image is larger, the average error of the measurement result is reduced, and the standard deviation is reduced as well. When the magnification is 0.4×, the measurement average error is 0.04°, and standard deviation is 0.101°. When the distance between the center of the circle and the center of the image is increased, the error in the angle calculation is also increased. A significant improvement is obtained by setting the fitting center position to the optical central position, which omits the influence of the error amplification caused by the distance of the circular hole from the optical axis.
Is It Beneficial to Use AI Chatbots to Improve Learners’ Speaking Performance?

This study focused on analyzing how different interactions (face-to-face chatting and chatting with AI chatbots) may affect speaking skills.In order to examine whether using AI chatbots can be beneficial for improving speaking task performances, 110 participants were divided into three groups: a face-toface chatting, AI text-chatting, and AI voice-chatting group.They were assigned three speaking tasks that involved describing a picture, responding to questions, and expressing an opinion, which they did both before and after the experiments.Based on the analysis of quantitative data, the findings of the study indicated that both AI groups improved their speaking performance after the experiment while there were mixed results from the face-to-face interactions.It was found that the participants in the face-to-face group improved significantly better in responding to questions and expressing an opinion while no significant differences were revealed in the describing a picture task.With respect to the effects of using AI chatbots, there were no significant differences in the two speaking tasks (describe a picture and respond to questions) between the three groups.However, the comparative analysis of the three different interaction modes suggested that the AI voice-chatting pairs outperformed in terms of their speaking performance task (expressing an opinion) compared with the face-to-face and AI textchatting groups.Regarding students' perspectives, descriptive statistics and selected student interviews were reported.Based on the findings of the current study, pedagogical implications and directions for future research are also suggested.
Ego-GNNs: Exploiting Ego Structures in Graph Neural Networks

Graph neural networks (GNNs) have achieved remarkable success as a framework for deep learning on graph-structured data. However, GNNs are fundamentally limited by their tree-structured inductive bias: the WL-subtree kernel formulation bounds the representational capacity of GNNs, and polynomial-time GNNs are provably incapable of recognizing triangles in a graph. In this work, we propose to augment the GNN message-passing operations with information defined on ego graphs (i.e., the induced subgraph surrounding each node). We term these approaches Ego-GNNs and show that Ego-GNNs are provably more powerful than standard message-passing GNNs. In particular, we show that Ego-GNNs are capable of recognizing closed triangles, which is essential given the prominence of transitivity in real-world graphs. We also motivate our approach from the perspective of graph signal processing as a form of multiplex graph convolution. Experimental results on node classification using synthetic and real data highlight the achievable performance gains using this approach.
Key Issues of Organizational Structure and Processes with Feature‐based Product Line Engineering

ABSTRACT Feature‐Based Product Line Engineering (PLE) is a well‐known approach for efficiently engineering product lines, which numerous case studies have shown to yield substantial benefits in cost, quality, and time to market. This article presents an approach for a necessary ingredient of successful PLE: Handling the temporal dimension, which is concerned with managing artifacts as they change and evolve. The approach relies on a foundation of proven traditional change control techniques but shows how they apply in the context of Feature‐Based PLE.