Reviewerly/exHarmony huggingface dataset:
Parallel swarm-based algorithms for scheduling independent tasks

Task scheduling is crucial for achieving high performance in parallel computing. Since task scheduling is NP-hard, the efficient assignment of tasks to compute resources remains an issue. Across the literature, several algorithms have been proposed to solve different scheduling problems. One group of promising approaches in this field is formed by swarm-based algorithms which have a potential to benefit from a parallel execution. Common swarm-based algorithms are Ant Colony Optimization (ACO) and Particle Swarm Optimization (PSO). In this article, we propose two new scheduling methods based on parallel ACO, PSO and, Hill Climbing, respectively. These algorithms are used to solve the problem of scheduling independent tasks onto heterogeneous multicore platforms. The results of performance measuements demonstrate the improvements on the makespan and the scheduling time achieved by the parallel variants.
Deep learning based graphical password authentication approach against shoulder-surfing attacks

The password used to authenticate users is vulnerable to shoulder-surfing assaults, in which attackers directly observe users and steal their passwords without using any other technical upkeep. The graphical password system is regarded as a likely backup plan to the alphanumeric password system. Additionally, for system privacy and security, a number of programs make considerable use of the graphical password-based authentication method. The user chooses the image for the authentication procedure when using a graphical password. Furthermore, graphical password approaches are more secure than text-based password methods. In this paper, the effective graphical password authentication model, named as Deep Residual Network based Graphical Password is introduced. Generally, the graphical password authentication process includes three phases, namely registration, login, and authentication. The secret pass image selection and challenge set generation process is employed in the two-step registration process. The challenge set generation is mainly carried out based on the generation of decoy and pass images by performing an edge detection process. In addition, edge detection is performed using the Deep Residual Network classifier. The developed Deep Residual Network based Graphical Password algorithm outperformance than other existing graphical password authentication methods in terms of Information Retention Rate and Password Diversity Score of 0.1716 and 0.1643, respectively.
A hybrid approach combining data hiding with visual cryptography for secure extraction of data hiding

Visual cryptography scheme (VCS) naturally provides a solution for protecting a secret image in a group environment. However, most of the existing VCSs proposed so far only protect one secret image. In this work, we design a hybrid scheme combining VCS and data hiding to simultaneously safeguard the secret image and some related information (e.g., the description of the secret image). First of all, a framework for the hybrid scheme is introduced. Algorithms for building the proposed framework are given. Generally, a secret image is partitioned into k shadows in which the additional messages are embedded. By stacking k shadows, the secret image is visually revealed, and meanwhile, the embedded messages can be securely extracted from the stacked result. Moreover, the performance of the proposed technique would suffer as a result when the threshold k becomes larger. At this time, we have larger pixel expansion, smaller contrast, and reduced data hiding efficiency. To make the scheme more practical, an improved approach is presented for reducing the pixel expansion, increasing the visual quality of recovered image, and enhancing the data hiding efficiency. Comprehensive theoretical analysis and experiments are demonstrated to clarify the effectiveness and advantages of the proposed methods.
Intangible cultural heritage image classification with multimodal attention and hierarchical fusion

Designing an efficient Intangible Cultural Heritage (ICH) image classification model is beneficial for the public to recognize the ICH and fostering the preservation and spread of ICH. Currently, related ICH image classification researches mainly focus on the visual features of ICH images, ignoring attached textual descriptions. However, attached textual descriptions can provide crucial clues for ICH images classification. Therefore, in this study, we propose to combine attached textual descriptions to perform ICH image classification in a multimodal way. Additionally, to capture intra- and inter-interactions between ICH images and attached textual descriptions, we propose a novel model named MICMLF, mainly consisted of multimodal attention and hierarchical fusion. Multimodal attention is employed to make the model focus on “important regions” and “important words” in ICH image and attached textual descriptions respectively. Hierarchical fusion is utilized to capture inter-modal dynamics interactions. Extensive experiments are conducted on datasets of two Chinese nation-level ICH projects, New Year Print (年画) and Clay Figurine (泥塑). Experimental results demonstrate the superiority of MICMLF, compared with several state-of-the-art methods. Also, the proposed model can handle the situation where ICH images and textual descriptions are incomplete.
Passive vibration suppression method based on frequency domain analysis in white light vertical scanning interferometer

Vibration in manufacturing environment can significantly affects the accuracy of surface measurements, especially for the white light vertical scanning interferometry (VSI), which requires mechanical scanning for full-field measurements. This paper proposes a passive vibration suppression technique that integrates a second common-path interferometer and uses frequency domain analysis to measure vibration and reconstruct surface topography. The method is feasible for suppressing vibrations with different frequencies and amplitudes, as verified by simulation and signal recovery analysis. An in-house developed interferometer with a common optical path is used to demonstrate the measurement performance experimentally. The mathematical formula relating the vibration amplitude, the vibration frequency, the frame rate of the camera and the scanning interval is presented. The experimental results show that the precision of plane measurement decreases with increasing vibration amplitude and frequency. Moreover, the step sample signals under a maximum vibration condition of 400 nm/5 Hz are recovered, with a standard deviation of 26.07 nm for the step height measurement. As the scanning interval increases, the system can suppress higher-amplitude vibrations.
Multivariate mix-GEE models for longitudinal data with multiple outcomes

Multivariate longitudinal studies often involve two or more outcomes of interest measured repeatedly across time for each subject. A main challenge in the analysis of such data is the complex correlation structure. Appropriate modeling of the covariance matrix can provide more efficient parameter estimators. In this paper, multivariate finite mixture models are built for the working correlation matrix of the generalized estimating equations (GEE). A new procedure is proposed to estimate the parameters while ensuring the positive definiteness of the estimated working correlation matrix. Moreover, the consistency and the asymptotic normality of the parameter estimates are derived theoretically. Furthermore, if data are from a Gaussian mixture model, the estimators can be proved to be asymptotically efficient. In addition, the proposed method is illustrated through several simulation studies and a real data example of transportation safety.
Trains, games, and complexity: 0/1/2-player motion planning through input/output gadgets

We analyze the computational complexity of motion planning through local "input/output" gadgets with separate entrances and exits, and a subset of allowed traversals from entrances to exits, each of which changes the state of the gadget and thereby the allowed traversals. We study such gadgets in the zero-, one-, and two-player settings, in particular extending past motion-planning-through-gadgets work [7], [8] to zero-player games for the first time, by considering "branchless" connections between gadgets that route every gadget's exit to a unique gadget's entrance. Our complexity results include containment in L, NL, P, NP, and PSPACE; as well as hardness for NL, P, NP, and PSPACE. We apply these results to show PSPACE-completeness for certain mechanics in the video games Factorio, [the Sequence], and a restricted version of Trainyard, improving the result of [3]. This work strengthens prior results on switching graphs, ARRIVAL [6], and reachability switching games [10].
MEMORIA: A Memory Enhancement and MOment RetrIeval Application for LSC 2023

The continuous collection and storage of personal data, denoted Lifelogging, has gained popularity in recent years as a means of monitoring and improving personal health. One important aspect of lifelogging is the collection and analysis of image data, which can provide valuable insights into an individual’s lifestyle, dietary habits, and physical activity. The Lifelog Search Challenge provides a unique opportunity to explore the state-of-the-art in lifelogging research, particularly in the area of egocentric image retrieval and analysis. Researchers can propose their approaches and compete to solve lifelog retrieval challenges and evaluate the effectiveness of their systems on a rich multimodal dataset generated by an active lifelogger with 18 months of continuous capture of lifelogging data. This paper presents the second version of MEMORIA, a computational tool developed to participate in the Lifelog Search Challenge 2023. In this new version, the information retrieval is based on the use of natural language search with the possibility to filter the results based on keywords and time periods. The system applies image analysis algorithms to process visual lifelogs, from pre-processing algorithms to feature extraction methods, in order to enrich the annotation of the lifelogs. This new version explores the use of a graph database, more detailed image annotation, and event segmentation, in order to improve the performance and user interaction. Experimental results of the user interaction with our retrieval module are presented, confirming the effectiveness of the proposed approach and showing the most relevant functionalities of the system.
MODE: Monocular omnidirectional depth estimation via consistent depth fusion

Monocular depth estimation has seen significant progress in recent years, especially in outdoor scenes. However, depth estimation results are not satisfying in omnidirectional images. As compared to perspective images, estimating the depth map from an omnidirectional image captured in the outdoor scene, using neural networks, has two additional challenges: (i) the depth range of outdoor images varies a lot across different scenes, making it difficult for the depth network to predict accurate depth results for training with an indoor dataset, besides the maximum distance in outdoor scenes mostly stays the same as the camera sees the sky, but depth labels in this region are entirely missing in existing datasets; (ii) a standard representation of omnidirectional images introduces spherical distortion, which causes difficulties for the vanilla network to predict accurate relative structural depth details. In this paper, we propose a novel network-MODE by giving special considerations to those challenges and designing a set of flexible modules for improving the performance of omnidirectional depth estimation. First, a consistent depth structure module is proposed to estimate a consistent depth structure map, and the predicted structural map can improve depth details. Second, to suit the characteristics of spherical sampling, we propose a strip convolution fusion module to enhance long-range dependencies. Third, rather than using a single depth decoder branch as in previous methods, we propose a semantics decoder branch to estimate sky regions in the omnidirectional image. The proposed method is validated on three widely used datasets, demonstrating the state-of-the-art performance. Moreover, the effectiveness of each module is shown through an ablation study on real-world datasets. Our code is available at https://github.com/lkku1/MODE. © 2017 Elsevier Inc. All rights reserved.
Rethinking Economics Education for Sustainable Development: A Posthumanist Practice Approach

This conceptual paper proposes a posthumanist practice approach as an alternative onto-epistemological framework for economics education. It applies a critical literature review to examine the positivist and neoliberal foundations of mainstream economics education. We find that the prevailing economics education promotes a decontextualized and hyperrational perspective on economic phenomena and human behavior, which impedes sustainable development goals by pushing pressing socio-ecological challenges to the periphery. In response, we propose a posthumanist practice approach grounded in social practice theory, which aims to provide a more holistic, contextualized, nature-immanent, and materially mediated understanding of human behavior and economic realities. By emphasizing practical learning through knowledgeable doing, relational entanglement, and multisensory interactions, the posthumanist practice approach recognizes economic knowledge to be situated, pluralistic, and shaped by interdependent human/nonhuman relations. This opens up a more ethical and relational way of understanding, learning, and acting that helps to reconnect the social with the natural and to align economics education with the goals of sustainable development. In order to apply a posthumanist onto-epistemological foundation for economics education, we provide guidance by outlining appropriate pedagogical methods, such as diversifying learning environments, embracing community and nature engagement as well as service learning, and revising the role of educators.
Regulation of Digital Agriculture—A Law and Economics Perspective

The digital transformation and the sustainability transition are the most important current challenges for European agricultural regulation. Innovation emerges as a key issue in both processes. This paper proposes an expansion of the methodological toolbox of Law and Economics to analyze how regulation can catalyze the development and diffusion of sustainable innovation.
Quantum walk on orbit spaces

Inspired by the covering-space method in path integral on multiply connected spaces, we here present a universal formula of time-evolution kernels for continuous- and discrete-time quantum walks on orbit spaces. In this note, we focus on the case in which walkers' configuration space is the orbit space $\Lambda/\Gamma$, where $\Lambda$ is an arbitrary lattice and $\Gamma$ is a discrete group whose action on $\Lambda$ has no fixed points. We show that the time-evolution kernel on $\Lambda/\Gamma$ can be written as a weighted sum of time-evolution kernels on $\Lambda$, where the summation is over the orbit of initial point in $\Lambda$ and weight factors are given by a one-dimensional unitary representation of $\Gamma$. Focusing on one dimension, we present a number of examples of the formula. We also present universal formulas of resolvent kernels, canonical density matrices, and unitary representations of arbitrary groups in quantum walks on $\Lambda/\Gamma$, all of which are constructed in exactly the same way as for the time-evolution kernel.
Deep Learning-Based Vehicle Type Detection and Classification

Modern intelligent transportation systems heavily rely on vehicle-type classification technology. Deep learning-based vehicle-type categorization technology has sparked growing concern as image processing, pattern recognition, and deep learning have all advanced. Over the past few years, convolutional neural work, in particular You Only Look Once (YOLO), has shown to have considerable advantages in object detection and image classification. This method speeds up detection because it can predict objects in real time. High accuracy: The YOLO prediction method produces accurate results with low background errors. YOLO also understands generalized object representation. This approach, which is among the best at object detection, outperforms R-CNN approaches by a wide margin. In this paper, YOLOv5 is used to demonstrate vehicle type detection; the YOLOv5m model was chosen since it suits mobile deployments, the model was trained with a dataset of 3000 images, where 500 images were allocated for each class with a variety of vehicles. Hyperparameter tuning was applied to optimize the model for better prediction and accuracy. Experimental results for a batch size of 32 trained for 300 epochs show a precision of 98.2%, recall of 94.9%, mAP@.5 of 97.9%, mAP@.5:.95 of 92.8%, and overall accuracy of 95.3% trained and tested on four vehicle classes.
A Blockchain-Based CCP Data Integrity Auditing Protocol for Smart HACCP

CCP data integrity auditing in South Korea's smart HACCP system is currently only enabled through centralized access control, so its reliability cannot be assured. Since simply logging the history of data access does not provide a way to effectively verify the integrity of the data, so reliable HACCP systems must include the appropriate data integrity auditing schemes on their system. Many researches have been proposed to verify the integrity of data stored on semi-honest cloud servers, but to the best of our knowledge, no research has taken a smart HACCP environment into account. Therefore, in this paper, we propose a blockchain-based data integrity auditing protocol appropriate for contexts with a smart HACCP system. Moreover, we conduct qualitative analysis to show that the computational overhead in our protocol is reasonable. The analysis results show that our protocol will satisfy the requirements of the smart HACCP system regardless of the blockchain platform on which it is implemented.
Korean-Vietnamese Neural Machine Translation at Sub-word Level

Recently, the explosion of deep learning has brought development opportunities to various fields such as automatic speech recognition, image recognition, financial fraud detection, etc. For the field of natural language processing (NLP) and more specifically for the field of machine translation (MT), advanced MT methods are also continuously being published based on the development of deep learning. However, most of the research focuses on popular languages such as English, French, and Chinese. Research related to low-level languages such as Korean or Vietnamese is still quite limited. We try to enrich the library of Vietnamese-Korean MT studies by applying Byte Pair Encoding (BPE) into MT systems. The results are evaluated by the method of Bi-Lingual Evaluation Understudy (BLEU) and Translation Error Rate (TER).
Verified ALL(*) Parsing with Semantic Actions and Dynamic Input Validation

Formally verified parsers are powerful tools for preventing the kinds of errors that result from ad hoc parsing and validation of program input. However, verified parsers are often based on formalisms that are not expressive enough to capture the full definition of valid input for a given application. Specifications of many real-world data formats include both a syntactic component and one or more non-context-free semantic properties that a well-formed instance of the format must exhibit. A parser for context-free grammars (CFGs) cannot determine on its own whether an input is valid according to such a specification; it must be supplemented with additional validation checks. In this work, we present CoStar++, a verified parser interpreter with semantic features that make it highly expressive in terms of both the language specifications it accepts and its output type. CoStar++ provides support for semantic predicates, enabling the user to write semantically rich grammars that include non-context-free properties. The interpreter also supports semantic actions that convert sequential inputs to structured outputs in a principled way. CoStar++ is implemented and verified with the Coq Proof Assistant, and it is based on the ALL(*) parsing algorithm. For all CFGs without left recursion, the interpreter is provably sound, complete, and terminating with respect to a semantic specification that takes predicates and actions into account. CoStar++ runs in linear time on benchmarks for four real-world data formats, three of which have non-context-free specifications.
A Framework for Policy Based Negotiation

Semantic remote attestation is a process for gathering and appraising evidence to determine the state of a remote system. Remote attestation occurs at the request of an appraiser or relying party and proceeds with a target system executing an attestation protocol that invokes attestation services in a specified order to generate and bundle evidence. The appraiser may then reason about the evidence to establish trust in the target’s state. Attestation Protocol Negotiation is the process of establishing a mutually agreed upon protocol that satisfies the appraiser’s desire for comprehensive information and the target’s desire for constrained disclosure. Here we explore formalization of negotiation focusing on a definition of system specifications through manifests, protocol sufficiency and soundness, policy representation, and negotiation structure. By using our formal models to represent and verify negotiation’s properties we can statically determine that a provably sound, sufficient, and executable protocol is produced.
HandDGCL: Two‐hand 3D reconstruction based disturbing graph contrastive learning

Abstract Virtual reality (VR) and augmented reality (AR) applications are becoming increasingly prevalent. However, constructing realistic 3D hands, especially when two hands are interacting, from a single RGB image remains a major challenge due to severe mutual occlusion and the enormous diversity of hand poses. In this article, we propose a disturbing graph contrastive learning strategy for two‐hand 3D reconstruction. This involves a graph disturbance network designed to generate graph feature pairs to enhance the consistency of the two‐hand pose features. A contrastive learning module leverages high‐quality generative features for a strong feature expression. We further propose a similarity distinguish method to divide positive and negative features for accelerating the model convergence. Additionally, a multi‐term loss is designed to balance the relation among the hand pose, the visual scale and the viewpoint position. Our model has achieved state‐of‐the‐art results in the InterHand2.6M benchmark. Ablation studies show the model's great ability to correct unreasonable hand movements. In subjective assessments, our graph disturbance learning method significantly improves the construction of realistic 3D hands, especially when two hands are interacting.