Reviewerly/exHarmony huggingface dataset:
On the Use of Deep Learning for Video Classification

The video classification task has gained significant success in the recent years. Specifically, the topic has gained more attention after the emergence of deep learning models as a successful tool for automatically classifying videos. In recognition of the importance of the video classification task and to summarize the success of deep learning models for this task, this paper presents a very comprehensive and concise review on the topic. There are several existing reviews and survey papers related to video classification in the scientific literature. However, the existing review papers do not include the recent state-of-art works, and they also have some limitations. To provide an updated and concise review, this paper highlights the key findings based on the existing deep learning models. The key findings are also discussed in a way to provide future research directions. This review mainly focuses on the type of network architecture used, the evaluation criteria to measure the success, and the datasets used. To make the review self-contained, the emergence of deep learning methods towards automatic video classification and the state-of-art deep learning methods are well explained and summarized. Moreover, a clear insight of the newly developed deep learning architectures and the traditional approaches is provided. The critical challenges based on the benchmarks are highlighted for evaluating the technical progress of these methods. The paper also summarizes the benchmark datasets and the performance evaluation matrices for video classification. Based on the compact, complete, and concise review, the paper proposes new research directions to solve the challenging video classification problem.
Machine learning and ontology-based novel semantic document indexing for information retrieval

The goal of information retrieval (IR) systems is to find the contents most closely related to the user's information needs from a pool of information. However, conventional IR methods neglect semantic descriptions of document contents and index documents based on the words that they include. When users and indexing systems use different terms to express the same subject, a vocabulary gap emerges. To overcome this limitation and to enhance the effectiveness of the IR systems, this paper introduced a novel hybrid semantic document indexing employing machine learning and domain ontology. The presented technique uses a skip-gram with negative sampling-based machine learning model and a domain ontology to determine the concepts for annotating unstructured documents. The proposed work also introduced multiple feature based novel concept ranking algorithm where statistical, semantic, and scientific named entity features of the concept were used to assign relevance weight to the annotations. The fuzzy analytical hierarchy process was used to derive the parameters of these feature weights. The final step is to rank the concepts according to their relevance to the document. Five benchmark publicly accessible datasets from the computer science domain were used in a series of experiments to validate the results of presented method. Experiment findings showed that the proposed method performs better than state-of-the-art techniques on these datasets, by improving average accuracy by 29%, while an improvement of 25% was recorded in F-measure. The improvement in average accuracy demonstrates that the performance of the proposed approach is better than the state-of-the-art methods in extracting document concepts accurately even when the same concept is referred to by distinct terms in the document and domain ontologies. The proposed system's ability to find similar concepts when the documents possess no concept from domain ontology is demonstrated by the improvement in F-measure, which is attributed to high recall rates of the proposed indexing scheme while maintaining high accuracy.
Magnon scattering modulated by omnidirectional hopfion motion in antiferromagnets for meta-learning

Neuromorphic computing is expected to achieve human-brain performance by reproducing the structure of biological neural systems. However, previous neuromorphic designs based on synapse devices are all unsatisfying for their hardwired network structure and limited connection density, far from their biological counterpart, which has high connection density and the ability of meta-learning. Here, we propose a neural network based on magnon scattering modulated by an omnidirectional mobile hopfion in antiferromagnets. The states of neurons are encoded in the frequency distribution of magnons, and the connections between them are related to the frequency dependence of magnon scattering. Last, by controlling the hopfion's state, we can modulate hyperparameters in our network and realize the first meta-learning device that is verified to be well functioning. It not only breaks the connection density bottleneck but also provides a guideline for future designs of neuromorphic devices.
MISNet: Multiscale Cross-Layer Interactive and Similarity Refinement Network for Scene Parsing of Aerial Images

Although progress has been made in multisource data scene parsing of natural scene images, extracting complex backgrounds from aerial images of various types and presenting the image at different scales remain challenging. Various factors in high-resolution aerial images (HRAIs), such as imaging blur, background clutter, object shadow, and high resolution, substantially reduce the integrity and accuracy of object segmentation. By applying multisource data fusion, as in scene parsing of natural scene images, we can solve the aforementioned problems through the integration of auxiliary data into HRAIs. To this end, we propose a multiscale cross-layer interactive and similarity refinement network (MISNet) for scene parsing of HRAIs. First, in a feature fusion optimization module, we extract, filter, and optimize multisource features and further guide and optimize the features using a feature guidance module. Second, a multiscale context aggregation module increases the receptive field, captures semantic information, and extracts rich multiscale background features. Third, a dense decoding module fuses the global guidance information and high-level fused features. We also propose a joint learning method based on feature similarity and a joint learning module to obtain deep multilevel information, enhance feature generation, and fuse multiscale and global features to enhance network representation for accurate scene parsing of HRAIs. Comprehensive experiments on two benchmark HRAIs datasets indicate that our proposed MISNet is qualitatively and quantitatively superior to similar state-of-the-art models.
Solar Radiation Forecasting Based on the Hybrid CNN-CatBoost Model

The renewable energy industry is rapidly expanding due to environmental pollution from fossil fuels and continued price hikes. In particular, the solar energy sector accounts for about 48.7% of renewable energy, at the highest production ratio. Therefore, climate prediction is essential because solar power is affected by weather and climate change. However, solar radiation, which is most closely related to solar power, is not currently predicted by the Korea Meteorological Administration; therefore, solar radiation prediction technology is needed. In this study, we predict solar radiation using extra-atmospheric solar radiation and three weather variables: temperature, relative humidity, and total cloud volume. We compared the performance of single models of machine and deep learning in previous work. For the single-model comparison, we used boosting techniques, such as extreme gradient boosting and categorical boosting (CatBoost) in machine learning, and the recurrent neural network (RNN) family (long short-term memory and gated recurrent units). In this paper, we compare CatBoost (previously the best model) with CNN and present a CNN-CatBoost hybrid model prediction method that combines CatBoost in machine learning and CNN in deep learning for the best predictive performance for a single-model comparison. In addition, we checked the accuracy change when adding wind speed and precipitation to the hybrid model. The model that considers wind speed and precipitation improved at all but three (Gangneung, Suwon, and Cheongju) of the 18 locations.
Impact of Human Resources Management Strategies on Organizational Learning of Islamic Banks in Jordan

The major aim of the study was to examine the impact of Human Resources Management strategies on Organizational Learning. Therefore, it focused on Islamic banks operating in Jordan. Data were primarily gathered through self-reported questionnaires creating by Google Forms which were distributed to a purposive sample of managers via email. The statistical program AMOSv24 was used to test the study hypotheses and achieve objectives. The study results demonstrated that Human Resources Management strategies have a positive impact relationship on Organizational Learning of Islamic banks operating in Jordan. Based on the study results, researchers recommend managers of Islamic banks in Jordan to develop mechanisms that reward employees for learning new things, in a way that enhances adaptive and generative learning processes, and enhances confidence building among employees which allowing organizational learning processes of both adaptive and generative types.
Non-Fungible Token Transactions: Data and Challenges

Non-fungible tokens (NFT) have recently emerged as a novel blockchain hosted financial asset class that has attracted major transaction volumes. Investment decisions rely on data and adequate preprocessing and application of analytics to them. Both owing to the non-fungible nature of the tokens and to a blockchain being the primary data source, NFT transaction data pose several challenges not commonly encountered in traditional financial data. Using data that consist of the transaction history of eight highly valued NFT collections, a selection of such challenges is illustrated. These are: price differentiation by token traits, the possible existence of lateral swaps and wash trades in the transaction history and finally, severe volatility. While this paper merely scratches the surface of how data analytics can be applied in this context, the data and challenges laid out here may present opportunities for future research on the topic.
DilateFormer: Multi-Scale Dilated Transformer for Visual Recognition

As a <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">de facto</i> solution, the vanilla Vision Transformers (ViTs) are encouraged to model long-range dependencies between arbitrary image patches while the global attended receptive field leads to quadratic computational cost. Another branch of Vision Transformers exploits local attention inspired by CNNs, which only models the interactions between patches in small neighborhoods. Although such a solution reduces the computational cost, it naturally suffers from small attended receptive fields, which may limit the performance. In this work, we explore effective Vision Transformers to pursue a preferable trade-off between the computational complexity and size of the attended receptive field. By analyzing the patch interaction of global attention in ViTs, we observe two key properties in the shallow layers, namely locality and sparsity, indicating the redundancy of global dependency modeling in shallow layers of ViTs. Accordingly, we propose Multi-Scale Dilated Attention (MSDA) to model <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">local</i> and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">sparse</i> patch interaction within the sliding window. With a pyramid architecture, we construct a Multi-Scale Dilated Transformer (DilateFormer) by stacking MSDA blocks at low-level stages and global multi-head self-attention blocks at high-level stages. Our experiment results show that our DilateFormer achieves state-of-the-art performance on various vision tasks. On ImageNet-1 K classification task, DilateFormer achieves comparable performance with 70% fewer FLOPs compared with existing state-of-the-art models. Our DilateFormer-Base achieves 85.6% top-1 accuracy on ImageNet-1 K classification task, 53.5% box mAP/46.1% mask mAP on COCO object detection/instance segmentation task and 51.1% MS mIoU on ADE20 K semantic segmentation task.
A multi-agent reinforcement learning driven artificial bee colony algorithm with the central controller

The large-scale real value problem in continuous and discrete optimization problems is a challenging issue for researchers and practitioners in the manufacturing domain. An improved artificial bee colony algorithm (ABC) combined with multi-agent reinforcement learning (called MARLABC) is presented for addressing the large-scale real value optimization problem in this study. Two stages including the training and the testing are introduced in the MARLABC via the multi-agent central controller to improve the convergence speed and local exploitation capability of the algorithm. The optimal strategy pool is constructed by training procedures via the multi-agent central controller with Q-learning mechanism. The effective strategy is selected from the optimal strategy pool for each agent during the testing process in the multi-agent central controller. The elite agents in the training population are reserved to generate the testing population to guide the search. The MARLABC algorithm is compared with the ABC variants and state-of-art algorithms on CEC 2017 benchmark problems. The stability and effectiveness of the MARLABC algorithm are confirmed by the experimental results.
Growable Genetic Algorithm with Heuristic-based Local Search for multi-dimensional resources scheduling of cloud computing

Multi-Dimensional Resources Scheduling Problem (MDRSP, usually a multi-objective optimization problem) has attracted focus in the management of large-scale cloud computing systems as the collaborative operation of various devices in the cloud affects resource utilization and energy consumption. Effective management of the cloud requires a higher performance method to solve MDRSP. Considering the complex coupling between multi-dimensional resources and focusing on virtual machines allocation, we propose GGA-HLSA-RW (GHW, a novel family of genetic algorithms) to optimize the utilization and energy consumption of the cloud. In GGA-HLSA-RW, we add a growth stage to the genetic algorithm and construct a Growable Genetic Algorithm (GGA) using the Heuristic-based Local Search Algorithm (HLSA) with Random multi-Weights (RW) as the growth route. Based on the GHW, we propose GHW-NSGA II and GHW-MOEA/D by applying the sorting strategies and population regeneration mechanism of NSGA II and MOEA/D. To evaluate the performance of GHW, we carry out extensive experiments on the simulation dataset and AzureTraceforPacking2020 for the problems of minimizing the maximum utilization rate of resources for each dimension and minimizing total energy consumption. Experiment results demonstrate the advantages of growth strategy and dimensionality reduction strategy of GHW, as well as validate the applicability and optimality of GHW in realistic cloud computing. The experiments also demonstrate our proposed GHW-NSGA II and GHW-MOEA/D have better convergence rates and optimality than state-of-the-art NSGA II and MOEA/D.
ReLOAD: Using Reinforcement Learning to Optimize Asymmetric Distortion for Additive Steganography

Recently, the success of non-additive steganography has demonstrated that asymmetric distortion can remarkably improve security performance compared with symmetric cost functions. However, most of current existing additive steganographic methods are still based on symmetric distortion. In this paper, for the first time we optimize asymmetric distortion for additive steganography and propose an A3C (Asynchronous Advantage Actor-Critic) based steganographic framework, called ReLOAD. ReLOAD is composed of an actor and a critic, where the former guides action selection for pixel-wise distortion modulation, and the latter evaluates the performance of modulated distortion. Meanwhile, a reward function that considers embedding effects is proposed to unify the goal of steganography and reinforcement learning, so that the minimization of embedding effects can be achieved by learning secure policy to maximize total rewards. Statistical analysis shows that compared with non-additive steganography, ReLOAD achieves lower change rates and makes embedding traces more consistent with cover image textures. Comprehensive experiments conducted on both hand-crafted feature-based and deep learning-based steganalyzers show that ReLOAD significantly promotes the state-of-the-art security performance of current additive methods and even outperforms non-additive steganography when the modification distribution gets sparser.
Energy efficient path planning for autonomous ground vehicles with ackermann steering

The autonomous ground vehicles have attracted a great deal of attention as viable solutions to a wide variety of military and civilian applications. However, the energy consumption plays a major role in the navigation of autonomous ground vehicles in challenging environments, especially if they are left to operate unattended under limited on-board power, such as planetary exploration, border patrol, etc. The autonomous ground vehicles are expected to perform more tasks more efficiently with limited power in these scenarios. Although plenty of research has developed an effective methodology for generating dynamically feasible and energy efficient trajectories for skid steering or differential steering vehicles, few studies on path planning for ackermann steering autonomous ground vehicles are available. In this study, an energy efficient path planning method with guarantee on completeness is proposed for autonomous ground vehicle with ackermann steering which is based on A∗ search algorithm. Firstly, the energy cost model is established for the autonomous ground vehicle using its kinematic constraints. Then, given the start and goal states, the energy-aware motion primitives are generated offline using the energy cost model to calculate the cost of each primary trajectory. Lastly, the energy efficient path planner is proposed and the analysis for completeness properties is given. The effectiveness of the proposed energy efficient path planner is verified by simulation over 150 randomly generated maps and real vehicle tests. The results show that a small increase in the distance of a path over the distance optimal path can result in a reduction of energy cost by nearly 26.9% in simulation and 21.09% in real test scenario for autonomous ground vehicles with ackermann steering.
Designing for the Metaverse: A Multidisciplinary Laboratory in the Industrial Design Program

The design research and education landscapes are changing. The widespread development and use of technologies such as Mixed Reality (MR) and the diffusion of Head-Mounted Displays (HMDs) available at low cost are causing a shift in design education toward the Metaverse. In this ever-changing scenario, there is a need to rethink design and teaching methods. However, scientific literature lacks the ability to provide contributions that include MR technology education in the industrial design program. We, therefore, present an innovative laboratory with an integrated multidisciplinary approach that starts from the fundamentals of interaction design and aims to teach students how to design next-generation MR interfaces for the Metaverse. The lab combines theory and practice within three courses: Information Design, Information Systems, and Virtual Design and Simulation. Industrial design students follow a precise multidisciplinary method consisting of five steps, from state-of-the-art analysis to the presentation of a final group design of an MR user interface. Thus, we introduce a class case study by presenting the outcomes of a semester project in the field of household appliances. Evaluation of the teaching method is conducted through a semi-structured questionnaire. Preliminary results show positive outcomes from students in terms of acceptance, effectiveness, usefulness, efficiency, and satisfaction with the teaching method adopted for the laboratory.
A hybrid U-Net model with attention and advanced convolutional learning modules for simultaneous gland segmentation and cancer grade prediction in colorectal histopathological images

In this proposed research work, a computerized Hybrid U-Net model for supplying colon glandular morphometric and cancer grade information is demonstrated. The solution is put forth by incorporating three distinctive structural elements—Advanced Convolutional Learning Modules, Attention Modules, and Multi-Scalar Transitional Modules—into the conventional U-Net architecture. By combining these modules, complex multi-level convolutional feature learning further encompassed with target-specified attention and increased effective receptive-field-size are produced. Three publicly accessible datasets—CRAG, GlaS challenge, LC-25000 dataset, and an internal, proprietary dataset Hospital Colon (HosC)—are used in experiments. The suggested model also produced competitive results for the gland detection and segmentation task in terms of Object-Dice Index as ((0.950 for CRAG), (GlaS: (0.951 for Test A & 0.902 for Test B)), (0.954 for LC-25000), (0.920 for HosC)), F1-score as ((0.921 for CRAG), (GlaS: (0.945 for Test A & 0.923 for Test B)), (0.913 for LC-25000), (0.955 for HosC)), and Object-Hausdorff Distance ((90.43 for CRAG), (GlaS: (23.11 for Test A & 71.47 for Test B)), (96.24 for LC-25000), (85.41 for HosC)). Pathologists evaluated the generated segmented glandular areas and assigned a mean score as ((9.25 for CRAG), (GlaS: (9.32 for Test A & 9.28 for Test B)), (9.12 for LC-25000) (9.14 for HosC)). The proposed model successfully completed the task of determining the cancer grade with the following results: Precision as ((0.9689 for CRAG), (0.9721 for GlaS), (1.0 for LC-25000), (1.0 for HosC)), Specificity (0.8895 for CRAG), (0.9710 for GlaS), (1.0 for LC-25000), (1.0 for HosC)), and Sensitivity ((0.9677 for CRAG), (0.9722 for GlaS), (0.9995 for LC-25000), (0.9932 for HosC)). Additionally, the Gradient-Weighted class activation mappings are provided to highlight the critical regions that the suggested model believes are essential for accurately predicting cancer. These visualizations are further reviewed by skilled pathologists and assigned with the mean scores as ((9.37 for CRAG), (9.29 for GlaS), (9.09 for LC-25000), and (9.91 for HosC)). By offering a referential opinion during the morphological assessment and diagnosis formulation in histopathology images, these results will help the pathologists and contribute towards reducing inadvertent human mistake and accelerating the cancer detection procedure.