Reviewerly/exHarmony huggingface dataset:
Simple but Effective In-the-wild Micro-Expression Spotting Based on Head Pose Segmentation

Micro-expressions may occur in high-stake situations when people attempt to conceal or suppress their true feelings. Nowadays, intelligent micro-expression analysis has long been focused on videos captured under constrained laboratory conditions. This is due to the relatively small number of publicly available datasets. Moreover, micro-expression characteristics are subtle and brief, and thus very susceptible to interference from external factors and difficult to capture. In particular, head movement is unavoidable in unconstrained scenarios, making micro-expression spotting highly challenging. This paper proposes a simple yet effective method for avoiding the interference of head movement on micro-expression spotting in natural scenarios by considering three-dimensional space. In particular, based on the head pose, which can be mapped to two-dimensional vectors (translations and rotations) for representation, long and complex videos could be divided into short video segments that basically exclude head movement interference. Following that, segmented micro-expression spotting is realized based on an effective short-segment-based micro-expression spotting algorithm. Experimental results on in-the-wild databases demonstrate the effectiveness of our proposed method in avoiding head movement interference. Additionally, due to the simplicity of this method, it creates opportunities for spotting micro-expressions in real-world scenarios, possibly even in real-time. Furthermore, it helps alleviate the small sample size problem in micro-expression analysis by boosting the spotting performance in massive unlabeled videos.
Scene-aware refinement network for unsupervised monocular depth estimation in ultra-low altitude oblique photography of UAV

Using depth estimation joint target detection networks to locate targets in the UAV field of view is a novel application in the depth estimation research field. The presence of more depth variations and low-texture regions in the ultra-low altitude oblique photographic images make them trickier to train for an excellent depth estimation network compared to autonomous driving scenarios. This presents a challenge in achieving optimal training. This study investigates the problem of unsupervised monocular depth estimation for ultra-low altitude oblique photography images. It aims to make subsequent advanced vision tasks better benefit from excellent depth estimation results in terms of overcoming complex scenes. The lack of effective back-projection directionality in training using adjacent frames is attributed to the extensive low-textured areas contained in the training data for complex ultra-low altitude oblique photography. We propose a self-supervised scene-aware refinement learning architecture from the perspective of enhancing feature perception to deal with such problems. The architecture consists of a multi-resolution feature fusion depth network and a perceptual refinement network (PRNet), together with a pose network to enhance regional differences in complex environments from a refined feature context perspective to obtain higher quality depth maps. We rethink the problem of depth information recovery and design the edge information aggregation (EIA) module, which is configured in the decoder section to refine the local region depth detail representation. We design several loss terms to constrain the training of the network in order to improve the quality of depth estimation. Our method is compared with six state-of-the-art self-supervised monocular depth estimation methods on three datasets (UAVid 2020, WildUAV, UAV ula). The experimental results demonstrate that our model achieves the best performance in most scenarios. The code and the private dataset (UAV ula) can be publicly available at https://github.com/takisu0916/MRFEDepth.
Postdigital Epistemology of Ignorance

Contrary to common assumptions, ignorance is not simply a state of not knowing or yet-to-be-known. Ignorance can involve substantive epistemic practices that are deliberately induced, cultivated, sustained, or purposefully designed, and which wilfully bring about forgetting, not knowing, or wanting to know what ought to be known. States of ignorance are often closely aligned with oppression, subordination, and exclusion, are socially situated, and arise from unequal and unfair differentials in power. Postdigital epistemic ignorance is concerned with how these practices manifest in the online environment along with digital devices and technologies.
Aquila-particle swarm based cooperative search optimizer with superpixel techniques for epithelial layer segmentation

The segmentation of epithelial layers from oral histopathology images plays a crucial role for early detection of oral cancer disease. As a result, more accurate segmentation of this layer is of the utmost importance for a Computer Aided Diagnosis (CAD) system. Therefore, this paper presents a superpixel image-based clustering technique using an improved Nature-Inspired Optimization Algorithm (NIOA), called the Cooperative Search (CS) algorithm. Here, superpixel image is utilized to construct a faster and noise-resistant oral histopathology image clustering method. The designed CS, on the other hand, is based on two popular NIOAs, the Aquila Optimizer (AO) and the Particle Swarm Optimizer (PSO), has better exploration-exploitation abilities. This CS has been used in the image clustering field to circumvent the problem of local optima trapping. On the other hand, a comparative investigation of three popular superpixel strategies with the proposed CS optimizer has been conducted to determine the optimal superpixel strategy for epithelial layer segmentation from clean as well as noisy oral histopathology images. Finally, the optimal superpixel strategy with CS has been tested with other state-of-the-art image segmentation techniques in the epithelial layer segmentation domain. The results obtained by the proposed segmentation technique are 98.82%, 96.70%, 97.60%, and 95.25%, in terms of average Accuracy, MCC, Dice, and Jaccard, respectively, which all are better than the cutting-edge segmentation methods. The proposed segmentation model is also evaluated over the leaf segmentation model and achieved better results visually and numerically. The optimization efficiency of the CS has also been measured over CEC2019 mathematical benchmark test functions and produced competitive results compared to other tested NIOAs.
T-depth-optimized quantum search with quantum data-access machine

Abstract Quantum search algorithms offer a remarkable advantage of quadratic reduction in query complexity using quantum superposition principle. However, how an actual architecture may access and handle the database in a quantum superposed state has been largely unexplored so far; the quantum state of data was simply assumed to be prepared and accessed by a black-box operation—so-called oracle, even though this process, if not appropriately designed, may adversely diminish the quantum query advantage. Here, we introduce an efficient quantum data-access process, dubbed as quantum data-access machine (QDAM), and present a general architecture for quantum search algorithm. We analyze the runtime of our algorithm in view of the fault-tolerant quantum computation (FTQC) consisting of logical qubits within an effective quantum error correction code. Specifically, we introduce a measure involving two computational complexities, i.e. quantum query and T -depth complexities, which can be critical to assess performance since the logical non-Clifford gates, such as the T (i.e. <?CDATA $\pi/8$?> <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:mi>π</mml:mi> <mml:mrow> <mml:mo>/</mml:mo> </mml:mrow> <mml:mn>8</mml:mn> </mml:math> rotation) gate, are known to be costliest to implement in FTQC. Our analysis shows that for N searching data, a QDAM model exhibiting a logarithmic, i.e. <?CDATA $O(\log{N})$?> <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:mrow> <mml:mi>O</mml:mi> <mml:mo stretchy="false">(</mml:mo> <mml:mi>log</mml:mi> <mml:mi>N</mml:mi> <mml:mo stretchy="false">)</mml:mo> </mml:mrow> </mml:math> , growth of the T -depth complexity can be constructed. Further analysis reveals that our QDAM-embedded quantum search requires <?CDATA $O(\sqrt{N} \times \log{N})$?> <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:mi>O</mml:mi> <mml:mo stretchy="false">(</mml:mo> <mml:msqrt> <mml:mi>N</mml:mi> </mml:msqrt> <mml:mo>×</mml:mo> <mml:mo form="prefix">log</mml:mo> <mml:mrow> <mml:mi>N</mml:mi> </mml:mrow> <mml:mo stretchy="false">)</mml:mo> </mml:math> runtime cost. Our study thus demonstrates that the quantum data search algorithm can truly speed up over classical approaches with the logarithmic T -depth QDAM as a key component.
A novel WGF-LN based edge driven intelligence for wearable devices in human activity recognition

Human activity recognition (HAR) is one of the key applications of health monitoring that requires continuous use of wearable devices to track daily activities. The most efficient supervised machine learning (ML)-based approaches for predicting human activity are based on a continuous stream of sensor data. Sensor data analysis for human activity recognition using conventional algorithms and deep learning (DL) models shows promising results, but evaluating their ambiguity in decision-making is still challenging. In order to solve these issues, the paper proposes a novel Wasserstein gradient flow legonet WGF-LN-based human activity recognition system. At first, the input data is pre-processed. From the pre-processed data, the features are extracted using Haar Wavelet mother- Symlet wavelet coefficient scattering feature extraction (HS-WSFE). After that, the interest features are selected from the extracted features using (Binomial Distribution integrated-Golden Eagle Optimization) BD-GEO. The important features are then post-processed using the scatter plot matrix method. Obtained post-processing features are finally given into the WGF-LN for classifying human activities. From these experiments, the results can be obtained and showed the efficacy of the proposed model.
Inclusive education in higher education institution: Are Ukrainian faculty members’ ready for it?

Quality higher education involves making it accessible to students with special needs and disabilities. Therefore, the implementation of inclusive education is a certain indicator of quality among higher education institutions (HEIs). At the same time, faculty members play a fundamental role in promoting inclusive learning environments working with students with disabilities. The aim of research is to determine the readiness of faculty members to implement an inclusive education in Ukrainian HEIs, because their willingness to work defines the practical implementation of legislative and regulatory initiatives regarding the organization of inclusive education in the actual educational practice. For this, we conducted a survey among 186 faculty members with different age, teaching experience, and professional category. The results confirm that the faculty of HEI are primarily focused on working with students with normative development and almost do not take into account the characteristics of educational difficulties of students with disabilities. In addition, their level of knowledge about basic legal and regulatory documents, elements of an inclusive learning environment, and typical problems of implementing an inclusive approach in the educational process of HEI are not uniform. But faculty members showed their readiness to master the practices of implementing an inclusive learning environment in higher education institutions. The results obtained can be useful for the development of institutional policies for the implementation of inclusive education in HEIs.
Demonstration of Snow Removal Work by Wheel Loader in an Environment Surrounded by Obstacles

Snow removal work using construction equipment faces problems such as a shortage of skilled operators owing to the declining birthrate and aging population, work in dangerous areas, and accidents caused by a lack of concentration during long work hours. To improve the working environment, research and development of automation of construction equipment are actively conducted. Therefore, in this study, we aim to generate a driving path for wheel loaders for snow removal work in a work environment surrounded by obstacles, such as walls and fences. Furthermore, the proposed method considers the changing shape of the snow piles during the removal. We experimentally verified that snow removal could be performed using an actual wheel loader on the route generated by the proposed simulation.
Large-Scale Anonymized Text-based Disability Discourse Dataset

The involvement of individuals with disabilities in online discussions related to disability and accessibility is a critical area of study. While previous research has qualitatively examined the participation of individuals with disabilities on social media platforms, large-scale analysis of social media content by people with disabilities has been an underexplored area. This paper presents a pioneering large-scale study of disability communities on Reddit. We developed an anonymized text-based dataset that consists of 1.5 million comments posted on three subreddits: r/disability, r/Blind, and r/ADHD. Using topic modeling, we analyzed the dataset and identified eight highly-coherent common categories and their associated keywords across the three subreddits. We contribute an Anonymized Disability Discourse Reddit Corpus (ADDReC) of 1.5 million comments that feature eight disability discourse categories.
Barriers and Benefits: The Path to Accessible Makerspaces

Motivated by the philosophical overlap between makerspace culture and the needs of assistive technology users, we investigated the ways that makerspaces can support the development of new technologies by and for disabled makers. Using eleven semi-structured interviews with makerspace operators and disabled makerspace users, we identified five categories of barriers to makerspace participation: recruitment/outreach, physical access, financial, access to information, and belonging. Based on these interviews, we highlight ways makerspaces can better welcome makers with disabilities: enabling members to create adaptive technologies for the space ("makerspacing the makerspace"), making the physical space and information within the space accessible, and fostering belonging by building relationships with the disability community. Overall, our work contributes to our understanding of the possibilities and challenges of connecting the disabled community with the maker community and suggests new directions for collaboration, especially towards building hybrid makerspaces that provide multiple modalities for connection and creativity.
Quantum–classical co-simulation for smart grids: a proof-of-concept study on feasibility and obstacles

Abstract With the rising complexity of our electricity infrastructure, smart grid simulations increasingly rely on co-simulation, which involves jointly executing independent subsystem simulations. However, in large-scale simulation scenarios, such as those involving costly power-flow analysis, co-simulation may experience computational-performance issues. Quantum computing offers a potential solution through quantum–classical co-simulation, in which one or more simulators of an otherwise classical co-simulation are executed on quantum hardware. However, there is no practical realization of this concept that establishes its feasibility. To address this gap, we integrate a quantum power flow simulator with a smart grid co-simulation and conduct an exploratory simulation study using a fictitious case-study scenario. The experiments demonstrate the feasibility of quantum–classical co-simulation; at the same time, they highlight four obstacles to the concept’s realization in practice: (1) To use quantum computing for co-simulation, session-based scheduling is required. (2) Distributed simulation limits possible applications and requires proximity of computing resources. (3) For the efficient extraction of classical information from the quantum states, we need carefully designed operators. (4) Current hardware limitations—such as noise susceptibility and the lack of quantum random access memory—limit practical near-term uses of quantum power flow; therefore, attention should be turned to alternative applications that are more promising in the near term. These findings pave the way for future research on quantum–classical co-simulation and its potential applications in smart grids.
From big data to smart data: a sample gradient descent approach for machine learning

Abstract This research paper presents an innovative approach to gradient descent known as ‘‘Sample Gradient Descent’’. This method is a modification of the conventional batch gradient descent algorithm, which is often associated with space and time complexity issues. The proposed approach involves the selection of a representative sample of data, which is subsequently subjected to batch gradient descent. The selection of this sample is a crucial task, as it must accurately represent the entire dataset. To achieve this, the study employs the use of Principle Component Analysis (PCA), which is applied to the training data, with a condition that only those rows and columns of data that explain 90% of the overall variance are retained. This approach results in a convex loss function, where a global minimum can be readily attained. Our results indicate that the proposed method offers faster convergence rates, with reduced computation times, when compared to the conventional batch gradient descent algorithm. These findings demonstrate the potential utility of the ‘‘Sample Gradient Descent’’ technique in various domains, ranging from machine learning to optimization problems. In our experiments, both approaches were run for 30 epochs, with each epoch taking approximately 3.41 s. Notably, our ‘‘Sample Gradient Descent’’ approach exhibited remarkable performance, converging in just 8 epochs, while the conventional batch gradient descent algorithm required 20 epochs to achieve convergence. This substantial difference in convergence rates, along with reduced computation times, highlights the superior efficiency of our proposed method. These findings underscore the potential utility of the ‘‘Sample Gradient Descent’’ technique across diverse domains, ranging from machine learning to optimization problems. The significant improvements in convergence rates and computation times make our algorithm particularly appealing to practitioners and researchers seeking enhanced efficiency in gradient descent optimization.
Revolutionizing Oral Cancer Detection: An Approach Using Aquila and Gorilla Algorithms Optimized Transfer Learning-Based CNNs

The early detection of oral cancer is pivotal for improving patient survival rates. However, the high cost of manual initial screenings poses a challenge, especially in resource-limited settings. Deep learning offers an enticing solution by enabling automated and cost-effective screening. This study introduces a groundbreaking empirical framework designed to revolutionize the accurate and automatic classification of oral cancer using microscopic histopathology slide images. This innovative system capitalizes on the power of convolutional neural networks (CNNs), strengthened by the synergy of transfer learning (TL), and further fine-tuned using the novel Aquila Optimizer (AO) and Gorilla Troops Optimizer (GTO), two cutting-edge metaheuristic optimization algorithms. This integration is a novel approach, addressing bias and unpredictability issues commonly encountered in the preprocessing and optimization phases. In the experiments, the capabilities of well-established pre-trained TL models, including VGG19, VGG16, MobileNet, MobileNetV3Small, MobileNetV2, MobileNetV3Large, NASNetMobile, and DenseNet201, all initialized with 'ImageNet' weights, were harnessed. The experimental dataset consisted of the Histopathologic Oral Cancer Detection dataset, which includes a 'normal' class with 2494 images and an 'OSCC' (oral squamous cell carcinoma) class with 2698 images. The results reveal a remarkable performance distinction between the AO and GTO, with the AO consistently outperforming the GTO across all models except for the Xception model. The DenseNet201 model stands out as the most accurate, achieving an astounding average accuracy rate of 99.25% with the AO and 97.27% with the GTO. This innovative framework signifies a significant leap forward in automating oral cancer detection, showcasing the tremendous potential of applying optimized deep learning models in the realm of healthcare diagnostics. The integration of the AO and GTO in our CNN-based system not only pushes the boundaries of classification accuracy but also underscores the transformative impact of metaheuristic optimization techniques in the field of medical image analysis.
PAE: Towards More Efficient and BBB-Secure AE from a Single Public Permutation

Four observations can be made regarding recent trends that have emerged in the evolution of authenticated encryption schemes: (1) regarding simplicity, the adoption of public permutations as primitives has allowed for sparing a key schedule and the need for storing round keys; (2) using the sums of permutation outputs, inputs, or outputs and inputs has been a well-studied means to achieve higher security beyond the birthday bound; (3) concerning robustness, schemes can provide graceful security degradation if a limited amount of nonces repeats during the lifetime of a key; and (4) Andreeva et al.’s ForkCipher approach can increase the efficiency of a scheme since they can use fewer rounds per output branch compared to full-round primitives. In this work, we improve the state of the art by combining those aspects for efficient authenticated encryption. We propose PAE, an efficient nonce-based AE scheme that employs a public permutation and one call to an XOR-universal hash function. PAE provides O(2n/3)-bit security and high throughput by combining forked public-permutation-based variants of $$\textsf {nEHtM}$$ and Encrypted Davies-Meyer. Thus, it can use a single, in part round-reduced, public permutation for most operations, spare a key schedule, and guarantee security beyond the birthday bound even under limited nonce reuse.
Secure Multi-party Computation with Legally-Enforceable Fairness

Fairness is a security notion of secure computation and cannot always be achieved if an adversary corrupts a majority of parties in standard settings. Lindell (CT-RSA 2008) showed that imposing a monetary penalty on an adversary can circumvent the impossibility. He formalized such a security notion as “legally enforceable fairness" for the two-party setting based on the ideal trusted bank functionality and showed a protocol achieving the requirements. Based on the same framework, we introduce secure multi-party computation with legally enforceable fairness that is applicable for an arbitrary number of parties. Further, we propose two protocols that realize our introduced functionality. The first one achieves O(n) rounds, $$O(n^2)$$ communications, and $$O(n \alpha )$$ fees, where n is the number of parties, and $$\alpha $$ is a parameter for the penalty amount. The fee refers to the balance amount in the bank required at the beginning of the protocol, which evaluates the difficulty of participating in the protocol in a financial sense. The second one achieves O(1) rounds, O(n) communications, and $$O(n^2 \alpha )$$ fees.