Reviewerly/exHarmony huggingface dataset:
Genetic Algorithm for Key Generation in Cryptosystems

In this chapter, the authors explore the application of genetic algorithms (GAs) for key generation in cryptosystems. Cryptography plays a crucial role in securing sensitive information, and the strength of a cryptosystem lies in the robustness of its encryption keys. Traditional methods of generating cryptographic keys face challenges in finding optimal key solutions, especially as computational power increases. Genetic algorithms offer a promising alternative, leveraging evolutionary principles to optimize key generation. This chapter provides an overview of genetic algorithms, their application in cryptography, and various techniques for using GAs in key generation for secure and efficient cryptosystems.
Cybersecurity challenges in AI-enabled smart transportation systems

As an essential component of sustainable cities, smart transport plays a pivotal role in moving people and commodities efficiently and enhancing the quality of services for the entire community. The rapid advancements of the Internet of Things (IoT), machine learning (ML), and artificial intelligence (AI) have catalyzed the development of smart transportation systems. Various applications, such as personalized route guidance and traffic control systems, have been extensively studied and widely deployed over the globe. By heavily relying on real-time, multi-source, and accurate information, ML and AI-based system solutions are smart and efficient. However, ML and AI can be a double-bladed sword, as many recent studies revealed the vulnerability issues of ML and AI models under falsified information or adversarial attacks. This presents cybersecurity challenges in smart transportation systems. Available research suggests that few studies have investigated this issue. In this chapter, cybersecurity challenges are discussed in the context of different smart mobility applications such as traffic prediction systems and intelligent traffic signals. The information and analysis in this chapter will assist stakeholders to improve the reliability and robustness of ML and AI-based applications and better protect smart transportation systems.
Comparison of key clusters of translated Korean laws and untranslated American and British laws

This chapter has explored the phraseological divergences of English translated Korean laws from non-translated American and British laws by using key cluster analysis in corpus linguistics. The top 20 key clusters of the translated law corpus compared to the nontranslation US and UK corpora, sorted by their keyness value, revealed the salient linguistic features of the translation corpus in terms of their predominant functions. The key clusters showed that the translated Korean laws are featured by abusive use of structuring bundles to signal text deixis and tabulation or topic elaboration bundles as well as bundles marking legal reference in a manner not convergent to TL conventions of common law jurisdictions. These phraseological absurdities and complexities might be the result of source language interference caused by ST-oriented translation norms, having implications for adopting more reader-oriented approach in non-authoritative legislative translation in Korea.
Image-Text Connection: Exploring the Expansion of the Diversity Within Joint Feature Space Similarity Scores

Cross-modal representation learning aims to learn a shared representation space where data from multiple modalities can be effectively compared, fused, and understood. This paper investigates the role of increased diversity in the similarity score matrix in enhancing the performance of the CLIP (Contrastive Language-Image Pretraining), a multi-modal learning model that establishes a connection between images and text within a joint embedding space. Two transforming approaches, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">sine</i> and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">sigmoid</i> (including two versions), are incorporated into the CLIP model to amplify larger values and diminish smaller values within the similarity matrix ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">logits</i> ). Hardware limitations are addressed using a more compact text encoder (DistilBERT) and a pre-trained ResNet50 image encoder. The proposed adaptations are evaluated on various benchmarks, including image classification and image/text retrieval tasks, using 10 benchmark datasets such as Food101, Flickr30k, and COCO. The performance of the adapted models is compared to the base CLIP model using Accuracy, mean per class, and Recall@k metrics. The results demonstrate improvements in Accuracy (up to 5.32% enhancement for the PatchCamelyon dataset), mean per class (up to 14.48% enhancement for the FGVCAircraft dataset), and retrieval precision (with an increase of up to 45.20% in Recall@1 for the COCO dataset), compared to the baseline algorithm (CLIP).
Sufficient Conditions for Stabilizability by Discrete-Time Controllers Possessing Monic Characteristic Polynomials With Integer Coefficients

Considering the desirable feature of possessing an infinite lifespan for discrete-time controllers having integer monic characteristic polynomials in partially homomorphic encrypted environments, this paper deals with obtaining sufficient conditions to guarantee that stabilizability by such controllers is possible. Firstly, a linear programming based stabilizability condition is derived for the special case that the controller is described by an FIR model. Then, benefiting from the Minkowski theorem in geometric number theory, a sufficient condition for stabilizability by non-FIR controllers with integer monic characteristic polynomials is obtained.
Transitioning to a Commercial Dashboarding System: Socio-technical Observations and Opportunities

Many long-established, traditional manufacturing businesses are becoming more digital and data-driven to improve their production. These companies are embracing visual analytics in these transitions through their adoption of commercial dashboarding systems. Although a number of studies have looked at the technical challenges of adopting these systems, very few have focused on the socio-technical issues that arise. In this paper, we report on the results of an interview study with 17 participants working in a range of roles at a long-established, traditional manufacturing company as they adopted Microsoft Power BI. The results highlight a number of socio-technical challenges the employees faced, including difficulties in training, using and creating dashboards, and transitioning to a modern digital company. Based on these results, we propose a number of opportunities for both companies and visualization researchers to improve these difficult transitions, as well as opportunities for rethinking how we design dashboarding systems for real-world use.
PROTAMP-RRT: A Probabilistic Integrated Task and Motion Planner Based on RRT

Solving complex robot manipulation tasks requires a Task and Motion Planner (TAMP) that searches for a sequence of symbolic actions, i.e. a task plan, and also computes collision-free motion paths. As the task planner and the motion planner are closely interconnected TAMP is considered a challenging problem. In this paper, a Probabilistic Integrated Task and Motion Planner (PROTAMP-RRT) is presented. The proposed method is based on a unified Rapidly-exploring Random Tree (RRT) that operates on both the geometric space and the symbolic space. The RRT is guided by the task plan and it is enhanced with a probabilistic model that estimates the probability of sampling a new robot configuration towards the next sub-goal of the task plan. When the RRT is extended, the probabilistic model is updated alongside. The probabilistic model is used to generate a new task plan if the feasibility of the previous one is unlikely. The performance of PROTAMP-RRT was assessed in simulated pick-and-place tasks, and it was compared against state-of-the-art approaches TM-RRT and Planet, showing better performance.
SDDS-Net: Space and Depth Encoder-Decoder Convolutional Neural Networks for Real-Time Semantic Segmentation

In this paper, we propose novel convolutional encoder-decoder architectures for real-time semantic segmentation based on an image-to-image translation approach via the space-to-depth and depth-to-space modules. We present architectures that compress the spatial information of the image using the space-to-depth (SD) instead of the commonly used pooling methods (Max-pooling and Average-pooling) or strided convolution approaches. The SD module can reduce the image size while preserving the spatial information of the image in the form of extra depth information, this approach is much better than the pooling approaches which introduce a loss in the information and the details of the image. We also propose a lightweight and simple decoder stage using the depth-to-space (DS) module which constructs a high-resolution dense prediction map from a large number of low-resolution feature maps. The proposed architectures are efficient in learning image classification and semantic segmentation with high accuracy and average processing speed. We trained and tested our proposed architectures on image classification (i.e. CIFAR10 and Tiny ImageNet), and indoor and outdoor benchmarks for semantic segmentation specifically NYU-depthV2 and CITYSCAPES. The proposed architectures could attain high accuracy in classification (94.28% on CIFAR10 and 72.25% on Tiny ImageNet) and high mean average precision and pixel accuracy values in semantic segmentation (pixel accuracy of 78.55% on NYU-depthV2 and 87.9% on CITYSCAPES) while maintaining a real-time speed of frame processing outperforming recent state-of-the-art methods in semantic segmentation.
Reversible Logic-Based Hexel Value Differencing—A Spatial Domain Steganography Method for Hexagonal Image Processing

The field of steganography has witnessed considerable advancements in square-pixel-based image processing (SIP). However, the application of steganography in Hexel (Hexagonal pixel)-based Image Processing (HIP) is still underexplored. This study introduces a pioneering spatial steganography method in the HIP domain called Reversible Logic-Based Hexel Value Differencing (RLBHVD). Our approach draws inspiration from Pixel-Value-Differencing (PVD), a fundamental spatial-domain (S-D) steganography method in SIP. Initially, the image is transformed into the HIP domain using the custom software infrastructure developed for this project. Due to the absence of commercial equipment capable of producing HIP-domain images, traditional digital imaging systems, with their sensor components, analog-to-digital conversion units, and square-pixel-based displays, are employed. Once the image is converted, it is partitioned into standardized heptads, each comprising seven hexels. Simultaneously, the secret message is segmented for embedding into the hexels within each heptad. Unlike SIP-domain PVD, which embeds segments into independent pixel pairs, our method performs iterative embedding within each heptad. Additionally, we leverage Feynman gates, a core element of reversible logic, to achieve retrieval of both the cover image and the secret message. Unlike PVD in SIP, our approach enables reversibility in the recovery process. Experimental results demonstrate that our proposed method, RLBHVD, outperforms its SIP counterpart, PVD, by achieving a low Mean Squared Error (MSE), high Peak Signal-to-Noise Ratio (PSNR), and significant similarity between the stego-image and cover image histograms. These findings highlight the efficacy and superiority of our HIP-based steganography approach in comparison to existing SIP methods.
Architecture Alternative Deep Multi-View Clustering

Due to the strong non-linear fitting ability of deep neural networks, deep multi-view clustering has become a popular topic in the fields of signal processing and machine learning. Multi-view clustering based on deep auto-encoder can effectively capture the nonlinear features of high-dimensional data. However, the existing methods still have the following problems: 1) most autoencoder-based deep multi-view clustering methods ignore the differences between cross-view data and lose view-diversity features by using the same encoder structure for different views, and 2) many current deep multi-view techniques rely on single-lane neural networks for extracting feature data from each view. The current approach has limitations in its capability to accurately analyze comprehensive complementary information and multilevel features. To address these issues, we introduce a new clustering method Architecture Alternative Deep Multi-view Clustering (AADMC). Specifically, AADMC proposes a dynamic encoder network to adapt the encoder structure of each view according to the diversities of different views. Subsequently, AADMC proposes utilizing the HilbertSchmidt Independent Criterion (HSIC) to analyze the diversity of information between each encoder output. Moreover, AADMC integrates high-order and low-order information of the data into a shared connection matrix. To be more specific, the lowrank constraint is employed in order to effectively investigate and utilize the consensus information derived from all available views. The effectiveness and superiority of AADMC are demonstrated through experimental results conducted on various public datasets.
Decoupling-Cooperative Framework for Referring Expression Comprehension

Referring Expression Comprehension (REC) aims to locate a specific object within an image by interpreting a referring expression articulated in natural language. This task comprises two essential branches: understanding and localizing. The former entails processing cognitive information from multimodal data, while the latter involves realizing the predictions in the perceptive visual space. Although various advanced approaches have been developed for each of these branches separately, existing REC approaches are unable to effectively leverage them due to the specific designs of architectures or objectives for REC, which bind understanding and localizing inseparably. To overcome this challenge, we propose the Decoupling-Cooperative Framework (DCF). The decoupling scheme in DCF enables us to utilize upto-date methods for understanding and localizing with minimal constraints. Meanwhile, the proposed cooperative modules enable better integration of the strengths from both branches to achieve further enhancements. Extensive experiments demonstrate that DCF achieves state-of-the-art performance across four benchmarks, thus highlighting the generalizability of DCF.
Key Frame Mechanism for Efficient Conformer Based End-to-End Speech Recognition

Recently, Conformer as a backbone network for end-to-end automatic speech recognition achieved state-of-the-art performance.The Conformer block leverages a self-attention mechanism to capture global information, along with a convolutional neural network to capture local information, resulting in improved performance.However, the Conformer-based model encounters an issue with the self-attention mechanism, as computational complexity grows quadratically with the length of the input sequence.Inspired by previous Connectionist Temporal Classification (CTC) guided blank skipping during decoding, we introduce intermediate CTC outputs as guidance into the downsampling procedure of the Conformer encoder.We define the frame with non-blank output as key frame.Specifically, we introduce the key framebased self-attention (KFSA) mechanism, a novel method to reduce the computation of the self-attention mechanism using key frames.The structure of our proposed approach comprises two encoders.Following the initial encoder, we introduce an intermediate CTC loss function to compute the label frame, enabling us to extract the key frames and blank frames for KFSA.Furthermore, we introduce the key frame-based downsampling (KFDS) mechanism to operate on high-dimensional acoustic features directly and drop the frames corresponding to blank labels, which results in new acoustic feature sequences as input to the second encoder.By using the proposed method, which achieves comparable or higher performance than vanilla Conformer and other similar work such as Efficient Conformer.Meantime, our proposed method can discard more than 60% useless frames during model training and inference, which will accelerate the inference speed significantly.This work code is available in (https://github.com/scufan1990/Key-Frame-Mechanism-For
A Data-Related Patch Proposal for Semantic Segmentation of Aerial Images

Large-size images cannot be directly put into GPU for training and need to be cropped to patches due to GPU memory limitation. The commonly used cropping methods before are random cropping and sequential cropping, which are crude and fatally inefficient. Firstly, categories of datasets are often imbalanced, and just simple cropping misses an excellent opportunity to make the data distribution balanced. Secondly, the training needs to crop a large number of patches to cover all patterns, which greatly increases the training time. This problem is of great practical hazards but is often overlooked by previous works. The optimal solution is to generate valuable patches. Valuable patches refer to the value to network training, i.e., the value of this patch for the convergence of the network, and the improvement of the accuracy. To this end, we propose a data-related patch proposal strategy to sample high valuable patches. The core idea is to score each patch according to the accuracy of each category, so as to perform balanced sampling. Compared with random cropping or sequential cropping, our method can improve the segmentation accuracy and accelerate the training vastly. Moreover, our method also shows great advantages over the loss-based balanced approaches. Experiments on Deepglobe and Potsdam show the excellent effect of our method.
GDiPAYOLO: A Fault Detection Algorithm for UAV Power Inspection Scenarios

Numerous real-time object detection frameworks have been evaluated using publicly available datasets, demonstrating commendable performance. However, there is a growing need for improved performance when these frameworks are applied to custom datasets. This manuscript aims to address the dual objectives of achieving high accuracy while maintaining lightweight efficiency in the context of UAV (Unmanned Aerial Vehicle) power inspection algorithm development. The newly introduced GDiBlock (Group Dimensionality-increase Convolution Block) effectively reduces channel redundancy, allowing for the extraction of rich, high-dimensional feature information with fewer parameters. Simultaneously, the PAM (Pooling Attention Mechanism) accurately distinguishes between background and target entities without introducing additional parameters, making it particularly advantageous for addressing complex background scenarios commonly encountered in UAV imagery. We meticulously explore a design paradigm guided by the receptive field principle to determine the optimal number of blocks within each processing stage. In conclusion, our proposed network architecture reduces the parameter count by 28% to 40%, while improving the mAP@.5 metric by a margin of 1.7 to 5.4 points when compared to YOLOv5. When compared to state-of-the-art real-time object detection frameworks, our study demonstrates superior performance in terms of both model accuracy and size
19. Imagination and justice: Teaching the future(s) of higher education through Africanfuturist speculative fiction

How might inclusion, equity, justice, care and sustainability be set to glimmer in higher education? This chapter draws on educational theory, critical research, and storytelling - in particular Africanfuturist science fiction - to dream new educational interfaces that are oriented for good. The chapter first maps out a 15 week course where students study the Africanfuturist science fiction novella Binti before writing their own storied futures for higher education in response. In the second half of the chapter another voice takes up the pen to imagine they are a student taking the course, and thus speculatively enact it. The chapter aims to open spaces for students and lecturers to: reflect on their (our) own positions in the academy, to critique the reproduction of classed, raced, gendered inequities in higher education through the encroachments of automation and platformisation, but then to generate futures that are oriented to justice.
4. Imagining higher education as infrastructures of care

This chapter is both retrospective and prospective. The authors contend that universities are constitutive of extractive infrastructures in the context of increased reliance on corporate controlled digital platforms, and processes that render faculty and students data objects rather than active agents of education. Using pertinent and powerful examples, the argument is made that universities are framed by extractive infrastructures that encode logics of ownership, competition, individualism and commodification to reproduce inequalities. This framework is extended to pervasive data collection practices inherent in learning management platforms, performance measurement systems, university rankings, and other technologies that inform higher education discourses, policies and practices. This prompts imagining otherwise by conceiving universities as infrastructures of care. The authors offer a remaking of the “good” university by creating material, epistemic and affective structures that operate on principles and values of reciprocity, reparation, gifting, sovereignty, hospitality, and epistemic pluralism.
A Critical Review of Predictive Modeling with 'Latent Shock' Variable

aInstitutional affiliation: Hospital 12 de Octubre, Madrid. España. Corresponding author: Marcos Valiente Fernández, Calle Francisco Navacerrada, n°57. 3° C. 28028. Phone: +34 616 807 458, e-mail: [email protected] Funds: None Conflict of interest: authors declare no conflicts of interest
Inclusive Learning through Immersive Virtual Reality and Semantic Embodied Conversational Agent: A case study in children with autism

Sustainable Development Goal 4 from the United Nations 2030 Agenda, focus on ensuring inclusive and equitable quality education and promoting lifelong learning opportunities for all. Inclusive education is a key component of sustainable development goal 4, and assistive technology is a critical factor in achieving it. In this context, this paper introduces the development of an immersive virtual reality system with conversational skills for inclusive learning processes. The idea of this work is to provide an assistive technology to motivate, educate, and train students with disabilities in a more inclusive way. In order to improve the user experience, the system was developed to work in a CAVE-like immersive environment using Natural Language Processing through a Semantic Embodied Conversational Agent. This work highlights that assistive technology can promote educational, psychological, and social benefits for students with disabilities. The use of assistive technology can enable academic engagement and social participation and be transformative from a psychological perspective. A case study was conducted on children with autism, which showed encouraging results of the system as an assistive technology for teaching and learning.