Reviewerly/exHarmony huggingface dataset:
SaMDE: A Self Adaptive Choice of DNDE and SPIDE Algorithms with MRLDE

Differential evolution (DE) is a proficient optimizer and has been broadly implemented in real life applications of various fields. Several mutation based adaptive approaches have been suggested to improve the algorithm efficiency in recent years. In this paper, a novel self-adaptive method called SaMDE has been designed and implemented on the mutation-based modified DE variants such as modified randomized localization-based DE (MRLDE), donor mutation based DE (DNDE), and sequential parabolic interpolation based DE (SPIDE), which were proposed by the authors in previous research. Using the proposed adaptive technique, an appropriate mutation strategy from DNDE and SPIDE can be selected automatically for the MRLDE algorithm. The experimental results on 50 benchmark problems taken of various test suits and a real-world application of minimization of the potential molecular energy problem validate the superiority of SaMDE over other DE variations.
Simulation of Robot Automatic Control Model Based on Artificial Intelligence Algorithm

Since the inception of robots, they have gained immense popularity, covering almost all walks of life. The application of industrial robots has made great contributions to the improvement of enterprises' competitiveness and technological progress. In the context of the rapid development of AI (Artificial Intelligence) technology, robots have made great improvements in both technical level and function. Based on the theory of AI algorithm, this paper constructs the robot automatic control model, and designs the action and path planning of the bionic robot. Mobile robots can carry and transport goods through precise navigation and obstacle avoidance algorithms. Finally, the simulation results show that the proposed algorithm can obtain the optimal path of the whole motion at a faster convergence rate. The actual point stabilization motion curve of the robot at each point shows that the trajectory of the robot at each point is smooth, the convergence speed is fast, and the deviation converges quickly, and the robot has good control effect.
Non-Recursive Cluster-Scale Graph Interacted Model for Click-Through Rate Prediction

Extracting users' interests from their behavior, particularly their 1-hop neighbors, has been shown to enhance Click-Through Rate (CTR) prediction performance. However, online recommender systems impose strict constraints on the inference time of CTR models, which necessitates pruning or filtering users' 1-hop neighbors to reduce computational complexity. Furthermore, while the graph information of users and items has been proven effective in collaborative filtering models, recursive graph convolution can be computationally costly and expensive to implement. To address these challenges, we propose the Non-Recursive Cluster-scale Graph Interacted (NRCGI) model, which reorganizes graph convolutional networks in a non-recursive and cluster-scale view to enable CTR models to consider deep graph information with low computational cost. NRCGI employs non-recursive cluster-scale graph aggregation, which allows the online recommendation computational complexity to shrink from tens of thousands of items to tens to hundreds of clusters. Additionally, since NRCGI aggregates neighbors in a non-recursive view, each hop of neighbors has a clear physical meaning. NRCGI explicitly constructs meaningful interactions between the hops of neighbors of users and items to fully model users' intent towards the given item. Experimental results demonstrate that NRCGI outperforms state-of-the-art baselines in three public datasets and one industrial dataset while maintaining efficient inference.
Bit-Plane Based Reversible Data Hiding in Encrypted Images Using Multi-Level Blocking With Quad-Tree

Reversible data hiding in encrypted images (RDHEI) has gained significant popularity among security and privacy researchers as well as users, because of its features such as reversibility, embedding capacity (EC), and security. To enlarge the EC while ensuring the complete reversibility and security, we propose a bit-plane based RDHEI method based on multi-level blocking with quad-tree. The proposed method uses median edge detector (MED) as well as difference predictor to transform the original input image into a low-magnitude difference matrix. The difference matrix is then encoded by first employing a novel quad-tree based bit-plane representation strategy to exploit the intra-bit plane correlation and subsequently by inter bit-plane redundancy mitigation strategy to exploit inter bit-plane level correlation, for significantly condensing their size. Thus, a bigger room is reserved inside the cover image for embedding, so that a large amount of secret data can be hidden while ensuring the complete reversibility of the image. Experimental results validate the superiority of the proposed method over the state-of-the-art methods.
A low delay highly dynamic range infrared imaging system for complex scenes based on FPGA

We proposed a hardware-oriented framework for infrared images compression and enhancement. The aim is to achieve high-quality imaging with minimal delay. Specifically, we introduce several key improvements: Firstly, we adopt the anisotropic guided filtering for layering, which effectively addresses the issue of detail halo. Secondly, we proposed a dynamic range compression algorithm (LIHS&HRC), which is based on local information histogram statistics and thus highlights the gray scales with abundant details. Thirdly, we propose a low-complexity adaptive two-dimensional non-local mean algorithm, which preserves important details while reducing noise. Through extensive experimentation, it is demonstrated the proposed algorithm framework significantly reduces latency and Field Programmable Gate Array (FPGA) resource requirements. It generates high-quality infrared image sequences in a minimal delay of only 0.31 ms at a frame rate of 50fps, with consuming a mere 0.0362 MB of shift register and 0.0312 MB of BRAM.
Learnable Graph Filter for Multi-view Clustering

Multi-view clustering is an important machine learning task for multi-media data. Recently, graph filter based multi-view clustering achieves promising performance and attracts much attention. However, the conventional graph filter based methods only use a pre-defined graph filter for each view and the used graph filters ignore the rich information among all views. Different from the conventional methods, in this paper, we aim to tackle a new problem, i.e., instead of using the pre-defined graph filters, how to construct an appropriate consensus graph filter by considering the information in all views. To achieve this, we propose a novel multi-view clustering method with graph filter learning. In our method, we learn an appropriate consensus graph filter from all views of data with multiple graph learning rather than directly pre-defining it. Then, we provide an iterative algorithm to obtain the consensus graph filter and analyze why it can lead to better clustering results. The extensive experiments on benchmark data sets demonstrate the effectiveness and superiority of the proposed method. The codes of this article are released in http://Doctor-Nobody.github.io/codes/MCLGF.zip.
KeyPosS: Plug-and-Play Facial Landmark Detection through GPS-Inspired True-Range Multilateration

In the realm of facial analysis, accurate landmark detection is crucial for various applications, ranging from face recognition and expression analysis to animation. Conventional heatmap or coordinate regression-based techniques, however, often face challenges in terms of computational burden and quantization errors. To address these issues, we present the KeyPoint Positioning System (KeyPosS) - a groundbreaking facial landmark detection framework that stands out from existing methods. The framework utilizes a fully convolutional network to predict a distance map, which computes the distance between a Point of Interest (POI) and multiple anchor points. These anchor points are ingeniously harnessed to triangulate the POI's position through the True-range Multilateration algorithm. Notably, the plug-and-play nature of KeyPosS enables seamless integration into any decoding stage, ensuring a versatile and adaptable solution. We conducted a thorough evaluation of KeyPosS's performance by benchmarking it against state-of-the-art models on four different datasets. The results show that KeyPosS substantially outperforms leading methods in low-resolution settings while requiring a minimal time overhead.1 The code is available at https://github.com/zhiqic/KeyPosS.
Dropping Pathways Towards Deep Multi-View Graph Subspace Clustering Networks

Multi-view graph clustering aims to leverage different views to obtain consistent information and improve clustering performance by sharing the graph structure. Existing multi-view graph clustering algorithms generally adopt a single-pathway network reconstruction and consistent feature extraction, building on top of auto-encoders and graph convolutional networks (GCN). Despite their promising results, these single-pathway methods may ignore the significant complementary information between different layers and the rich multi-level context inside. On the other hand, GCN usually employs a shallow network structure (2-3 layers) due to the over-smoothing with the increase of network depth, while few multi-view graph clustering methods explore the performance of deep networks. In this work, we propose a novel Dropping Pathways strategy toward building a deep Multi-view Graph Subspace Clustering network, namely DPMGSC, to fully exploit the deep and multi-level graph network representations. The proposed method implements a multi-pathway self-expressive network to capture pairwise affinities of graph nodes among multiple views. Moreover, we empirically study the impact of a series of dropping methods on deep multi-pathway networks. Extensive experiments demonstrate the effectiveness of the proposed DPMGSC compared with its deep counterpart and state-of-the-art methods.
SegViT v2: Exploring Efficient and Continual Semantic Segmentation with Plain Vision Transformers

Abstract This paper investigates the capability of plain Vision Transformers (ViTs) for semantic segmentation using the encoder–decoder framework and introduce SegViTv2 . In this study, we introduce a novel Attention-to-Mask (ATM) module to design a lightweight decoder effective for plain ViT. The proposed ATM converts the global attention map into semantic masks for high-quality segmentation results. Our decoder outperforms popular decoder UPerNet using various ViT backbones while consuming only about $$5\%$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mn>5</mml:mn> <mml:mo>%</mml:mo> </mml:mrow> </mml:math> of the computational cost. For the encoder, we address the concern of the relatively high computational cost in the ViT-based encoders and propose a Shrunk ++ structure that incorporates edge-aware query-based down-sampling (EQD) and query-based up-sampling (QU) modules. The Shrunk++ structure reduces the computational cost of the encoder by up to $$50\%$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mn>50</mml:mn> <mml:mo>%</mml:mo> </mml:mrow> </mml:math> while maintaining competitive performance. Furthermore, we propose to adapt SegViT for continual semantic segmentation, demonstrating nearly zero forgetting of previously learned knowledge. Experiments show that our proposed SegViTv2 surpasses recent segmentation methods on three popular benchmarks including ADE20k, COCO-Stuff-10k and PASCAL-Context datasets. The code is available through the following link: https://github.com/zbwxp/SegVit .
Causality-Inspired Source-Free Domain Adaptation for Medical Image Classification

Domain adaptation can mitigate the domain shift problem of deep learning methods in medical image classification but is restricted in medical applications due to limited access to sensitive medical images and scarce labels caused by strong requirement of expertise in medicine. Source-free domain adaptation (SFDA) only requires unlabeled target domain data and a pre-trained source domain model without access to source domain data but usually suffers from degraded performance. In this paper, we propose a causality-inspired source-free domain adaptation (CSDA) approach for medical image classification to address this problem from a causal view. We achieve causal graph modeling for medical imaging and unify two causal modules with SFDA to alleviate the interference caused by acquisition process and prototypes of the pre-trained source model during self-training for adaptation. Specifically, data augmentation and source-prototype based causal interventions are achieved to remove the confounding of irrelevant background and bias of pre-trained source models. Experiments on public dataset like Pulmonary Chest X-Ray Abnormalities show that CSDA outperforms existing domain adaptation approaches and state-of-the-art source-free methods on unlabeled target data for medical image classification.
Human Detection in Thermal Images Using YOLOv8 for Search and Rescue Missions

The effectiveness of life-saving search and rescue missions relies on swiftly locating and supplying crucial information about injured or missing persons, which optimize resource allocation and operational efforts while reducing expenses. Our research work explores the advances in deep learning techniques for efficient and accurate human detection using thermal images targeting the application of search and rescue missions. This paper focuses on exploring YOLOv8, the latest version of the You Only Look Once (YOLO) object detection model, to detect humans in diverse scenarios. A novel dataset of 17,148 grayscale thermal images with 90,882 annotations of humans is constructed carefully to represent various conditions and scenarios. All available YOLOv8 variants of different sizes and architectures (from Nano to Extra Large) are trained and evaluated using this dataset. The evaluation of the trained models demonstrates the effectiveness of YOLOv8 models in human detection exhibiting a high average precision of 95%. Furthermore, the impact of model size on precision and accuracy is highlighted.
Algorithm for Generating Robust S-Boxes Using Adjacency Matrix Parameters

In modern block cipher schemes, the substitution block (S-box) acts as a key nonlinear element that plays an important role in generating entangled entanglement in the creation of the ciphertext. S-boxes with reduced differential uniformity and higher non-linearity are better suited to thwart cryptanalysis attempts. This paper proposes an innovative approach that involves constructing 8 × 8 S-boxes by generating adjacency matrices based on selected graph parameters. After that, the affine transformation is applied. Notably, this process uses any 8-vertex graph and its unique number of edges, resulting in an adjacency matrix. S-boxes are then generated using an affine mapping method using an adjacency matrix structure similar to Rijndael's algorithm. The efficiency and reliability of the resulting S-boxes are carefully evaluated against various cryptographic metrics. The robustness test includes parameters such as non-linearity (NL), strict avalanche criteria (SAC), differential approximation probability (DAP) and linear approximation probability (LAP). This comprehensive evaluation confirms that all structured S-boxes satisfy the vital algebraic properties. In addition, a comparative analysis is carried out comparing the properties of the new S-boxes with the latest analogues found in the existing literature. The results show a significant advantage in terms of resilience to potential exploits by malicious actors. In the end, the outcomes of this research emphasize the significant potential and benefits of the suggested S-box-focused encryption approach, positioning it as a compelling substitute for conventional encryption methods.
Countering online terrorist content: A social regulation approach

Abstract After a period of self‐regulation, countries around the world began to implement regulations for the removal of terrorist content from tech platforms. However, much of this regulation has been criticised for a variety of reasons, most prominently for concerns of infringing free speech and creating unfair burdens for smaller platforms. In addition to this, regulation is heavily centred around content moderation, however, fails to consider or address the psychosocial risks it poses to human content moderators. This paper argues that where regulation has been heavily criticised yet continues to inspire similar regulation a new regulatory approach is required. The aim of this paper is to undertake an introductory examination of the use of a social regulation approach in three other industries (environmental protection, consumer protection and occupational health and safety) to learn and investigate new regulatory avenues that could be applied to the development of new regulation that seeks to counter terrorist content on tech platforms and is concerned with the safety of content moderators.
A Bibliometric Analysis of Artificial Intelligence and Human Resource Management

In the current business scenario, artificial intelligence (AI) is the trend and is becoming the new normal. There is an increasing application of AI in business and organizations in all sectors. The adoption of AI in the human resource department is not an exception. The different functions of human resource management such as recruitment, management of employees, and engagement have been changed by the application AI. The manual HR works are taken over by machines enabling HR managers to deal with more strategic roles. The aim of this work is to accomplish a bibliometric analysis which addresses the implementation of AI in human resource management. The bibliometric database Web of Science was chosen for the data extraction. The initial search yielded 3475 articles, out of which 210 were taken for final analysis. VOS viewer was used for producing the bibliometric information. The findings indicate the rising trend of using AI in HRM.
The World Literature Knowledge Graph

Digital media have enabled the access to unprecedented literary knowledge. Authors, readers, and scholars are now able to discover and share an increasing amount of information about books and their authors. However, these sources of knowledge are fragmented and do not adequately represent non-Western writers and their works. In this paper we present The World Literature Knowledge Graph (WL-KG), a semantic resource containing 194, 346 writers and 971, 210 works, specifically designed for exploring facts about literary works and authors from different parts of the world. The knowledge graph integrates information about the reception of literary works gathered from 3 different communities of readers, aligned according to a single semantic model. The resource is accessible through an online visualization platform, which can be found at the following URL: https://literaturegraph.di.unito.it . This platform has been rigorously tested and validated by 3 distinct categories of experts who have found it to be highly beneficial for their respective work domains. These categories include teachers, researchers in the humanities, and professionals in the publishing industry. The feedback received from these experts confirms that they can effectively utilize the platform to enhance their work processes and to achieve valuable outcomes.
Seamless Data Exchange: Advancing Healthcare with Cross-Chain Interoperability in Blockchain for Electronic Health Records

The rapid digitization of healthcare records has led to the accumulation of vast amounts of sensitive patient data, stored across various systems and platforms. To ensure the secure and efficient exchange of Electronic Health Records (EHRs) among healthcare providers, researchers, and patients themselves, the concept of cross-chain interoperability within blockchain technology emerges as a promising solution. Nevertheless, existing blockchain platforms exhibit several limitations. In order to address the issue of non-interoperability, the suggested method involves creating a connection between two similar blockchain networks. This solution is exemplified through the use of an Electronic Health Records (EHR) structure, which is distributed across distinct Ethereum Testnets and implemented via a Solidity Smart Contract. The paper aims to demonstrate the viability of bridging the gap and fostering seamless interoperability between blockchain networks. However, establishing effective communication between these smart contracts proves to be a complex endeavor, whether within a singular blockchain or spanning multiple blockchains. This complexity presents a formidable obstacle, particularly when diverse hospitals require the sharing or exchange of critical information. Consequently, a solution becomes imperative to facilitate cross-chain communication among smart contracts. This solution provides seamless operation both within the confines of a single blockchain and across disparate blockchains. By achieving this, cross-chain interoperability can be realized, enabling distinct blockchain networks to mutually comprehend and actively engage with each other.
Perceptions of Students on Project-Based Learning: Evidences from Public Schools in Pokhara

Many initiatives introduced to enhance students learning in Nepal, and Project-Based Learning is one such. It is compulsorily integrated into the school curriculum in all subjects. Despite its timely use and integration in a school course, many arguments are raised regarding its relevance and application. This study explores the lived experiences of the students studying at the secondary level in public schools run by the Nepal government regarding their perception of Project- Based Learning in school. Seven students were interviewed in-depth, and 15 interested students in classes nine contributed reflective notes to the data collection process. The study has coined three themes by processing the data: perceptions of students on project based learning, enhancement of skills and competencies, and challenges students face in the school context of Pokhara. Findings revealed that the project-based learning introduced in school turned out to be supportive to students in shaping their learning autonomously and through real-life learning. Therefore, the research revealed a positive impact on improving classroom practices and enhancing learning among participating students. The research also finds that some students find it burdensome and loaded as many projects are assigned in a hurry. Future research might explore modes of effective interventions for helping students grow in their practices of project-based learning. Keywords: project work benefits, student development, improved classroom practice, higher engagement