Reviewerly/exHarmony huggingface dataset:
A novel approach for large‐scale environmental data partitioning on cloud and on‐premises storage for compute continuum applications

Summary Cloud‐based services have proved useful in several research fields, such as engineering, health science, and astrophysics, to mention a few examples. The computational environmental science community developed a strong need for cloud facilities to store, process, and manage data from observations and numerical models for simulations and forecasts. Weather forecast models and global sensor networks deal with multidimensional geo‐referenced data∖sets. However, environmental data consumer applications usually require a relatively small amount of multidimensional input data slice to analyze a specific area or time interval. Hence, reducing data dimension for information retrieval is mandatory. This paper presents a twofold solution: a technique to load and retrieve the sliced multidimensional data set on different cloud services such as Amazon Web Service (AWS), Google Cloud Platform, and Microsoft Azure. The experimental results performed on these cloud services highlight that the proposed method can significantly speed up the process of loading and retrieving the data slices compared to working with the entire data set in bulk or OPeNDAP server.
Evaluation of metric and representation learning approaches: Effects of representations driven by relative distance on the performance

Several deep neural network architectures have emerged recently for metric learning. We asked which architecture is the most effective in measuring the similarity or dissimilarity among images. To this end, we evaluated six networks on a standard image set. We evaluated variational autoencoders, Siamese networks, triplet networks, and variational auto-encoders combined with Siamese or triplet networks. These networks were compared to a baseline network consisting of multiple separable convolutional layers. Our study revealed the following: (i) the triplet architecture proved the most effective one due to learning a relative distance — not an absolute distance; (ii) combining auto-encoders with networks that learn metrics (e.g., Siamese or triplet networks) is unwarranted; and (iii) an architecture based on separable convolutional layers is a reasonable simple alternative to triplet networks. These results can potentially impact our field by encouraging architects to develop advanced networks that take advantage of separable convolution and relative distance.
Cross-Chain-Based Trustworthy Node Identity Governance in Internet of Things

As a flourishing technical adoption boosted by 5G technology in recent years, Internet of Things (IoT) has become a common implementation in contemporary network-based solutions, such that tons of IoT devices, seen as nodes in the network, are employed. In order to meet the demands of the network edge, IoT devices mostly have limited computation and storage capabilities; however, this setting also introduces trust and security concerns. One of the noticeable threats is that malicious nodes forge or impersonate other legal nodes’ identities, which results in trustworthiness issues. In this paper, we focus on the trust issue of IoT device identities and propose a cross-chain-based solution that applies a reply chain for identifying the legality of device nodes. Our proposed relay chain-based IoT model supports secure and reliable data transmission through the relay chain with the authorization and trust of all IoT blockchains. The relay chain realizes the multi-party verification of IoT devices; thus, addressing the trust problem of device identities. The consensus of the relay chain is executed by the arbitration committee nodes rather than all nodes. Considering the efficiency and adaptability, we also have proposed a novel Optimal Cross-chain Decision-making algorithm to optimize the consensus for the identity-oriented relay chain. The proposed algorithm can minimize the cross-chain overhead while ensuring the fairness of the consensus. Our experiment evaluations have demonstrated that the proposed approach could increase the cross-chain efficiency under triple constraints.
Adaptive Split-Fusion Transformer

Neural networks for visual content understanding have recently evolved from convolutional ones to transformers. The prior (CNN) relies on small-windowed kernels to capture the regional clues, demonstrating solid local expressiveness. On the contrary, the latter (transformer) establishes long-range global connections between localities for holistic learning. Inspired by this complementary nature, there is a growing interest in designing hybrid models which utilize both techniques. Current hybrids merely replace convolutions as simple approximations of linear projection or juxtapose a convolution branch with attention without considering the importance of local/global modeling. To tackle this, we propose a new hybrid named Adaptive Split-Fusion Transformer (ASF-former) that treats convolutional and attention branches differently with adaptive weights. Specifically, an ASF-former encoder equally splits feature channels into half to fit dual-path inputs. Then, the outputs of the dual-path are fused with weights calculated from visual cues. We also design a compact convolutional path from a concern of efficiency. Extensive experiments on standard benchmarks show that our ASF-former outperforms its CNN, transformer, and hybrid counterparts in terms of accuracy (83.9% on ImageNet-1K), under similar conditions (12.9G MACs / 56.7M Params, without large-scale pre-training). The code is available at: https://github.com/szx503045266/ASF-former.
RTMC: A Rubost Trusted Multi-View Classification Framework

Multi-view learning aims to fully exploit the information from multiple sources to obtain better performance than using a single view. However, real-world data often contains a lot of noise, which can have a large impact on multi-view learning. Therefore, it is necessary to identify noise contained in multi-view data to achieve robust and trusted classification. In this paper, we propose a robust trusted multi-view classification framework, RTMC. Our framework uses multi-view affinity and repellence encoding to learn effective latent encodings of multi-view data. We also propose a trust-aware discriminator to estimate trust scores by identifying noise contained in the data. We adopt prototype queues, which store latent encodings of different classes, to accurately identify the noise. Finally, trusted multi-view classification is proposed to jointly predict the trust scores of classification and achieve robust classification results through a trusted fusion strategy. RTMC is validated on six challenging multi-view datasets and the experimental results demonstrate the robustness and effectiveness of our method.
Unsupervised Deep Homography Estimation based on Transformer

Homography estimation is a crucial problem in computer vision, which aims to provide an optimal transformation matrix for aligning images captured from different viewpoints. Current methods extract shallow features from image pairs and introduce learnable mask modules to improve homography estimation performance. However, they struggle to capture long-term dependencies between features and comprehend the global structures of image features. A deep unsupervised homography learning framework is proposed in this paper, consisting of a weight-sharing feature extraction network and a homography estimation network based on the Transformer model. The former extracts the local features of images, while the latter learns the correlation between them and understands the global features of images, enabling the algorithm to better estimate the homography of unaligned images. Experimental results demonstrate that the proposed method outperforms the advanced methods for estimating homography matrices in the CA-Unsupervised dataset.
The UCD Sprint: Bringing Users Along to Sprint

One of the most important aspects of software development is that the software is suitable for the intended users. Software professionals often tailor software development to their own needs, resulting in the software being unsuitable for many users. The course will introduce a User-Centered Design Sprint (UCD Sprint) process, which aims at extending the understanding of users’ needs and wants. Participants will practice two steps from the UCD Sprint: the user group analysis and stating user experience goals. This in-person course appeals to researchers and developers interested in exploring their innovative ideas through a user-centred progressive and concise step-by-step process.
Public speaking training in front of a supportive audience in Virtual Reality improves performance in real-life

Abstract Public speaking is a challenging task that requires practice. Virtual Reality allows to present realistic public speaking scenarios in this regard, however, the role of the virtual audience during practice remains unknown. In the present study, 73 participants completed a Virtual Reality practice session while audience was manipulated to be supportive or unsupportive or presentations were practiced without audience. Importantly, following the virtual practice, participants held the presentation during a real university course via Zoom. We measured emotional experience, self-efficacy, and the subjective evaluation of performance at baseline, after VR practice, and after the real presentation. Additionally, participants’ performance in the real presentation was evaluated by instructors (blinded to condition). Supportive in contrast to unsupportive audiences led to more positive believes about one’s own performance, while there were no changes in beliefs in the group without audience. Importantly, practice in front of a supportive compared to unsupportive audience resulted in a more positive evaluation of speaker confidence in real-life public speaking as rated by the instructors. These results demonstrate an impact of virtual social feedback during public speaking on subsequent subjective performance evaluation. This may increase self-confidence resulting in actual improved public speaking performance in real-life.
The Mediating Role of Employee Engagement in the Relationship Between Leadership Styles and Organizational Performance

Organizational performance is the combined efforts of the employees and leadership. Their effective engagement and better working relationships enhance organizational performance and effectiveness. Open communication among employees and Leadership bolsters organizational performance and provides social, emotional, psychological, ethical, and moral support, safety, and security. This study explores employee engagement's mediating role in assessing Small and Medium Enterprises (SMEs). A mixed methodology was adopted for the study. In the first step, a Systematic Literature Review (SLR) was adopted as a methodology for the analysis. In the second stage, a case study supported the study's findings. The study found that leadership style affects organizational performance. Moreover, employee engagement has a significant effect on organizational performance. The study also proposed a model based on the content review, which is recommended to be tested in different contexts and industries around the globe.
Hyperbolic Contrastive Graph Representation Learning for Session-based Recommendation

Session-based recommendation (SBR) learns users' preferences by capturing the short-term and sequential patterns from the evolution of user behaviors. Among the studies in the SBR field, graph-based approaches are a relatively powerful technique, which generally extract item information by message aggregation under Euclidean space. However, such methods cannot effectively extract the hierarchical information contained among consecutive items in a session, which is critical to represent users' preferences. In this paper, we present a hyperbolic contrastive graph recommender (HCGR), a principled session-based recommendation framework involving Lorentz hyperbolic space to efficiently capture the coherence and hierarchical representations of the items. Within this framework, an adaptive hyperbolic attention computation is designed to aggregate the graph message of each user's preference in a session-based behavior sequence. In addition, a contrastive ranking loss with the hyperbolic distance is used to separate the positive and negative items. Compared with the Euclidean distance, the advantage of the hyperbolic distance in the contrastive ranking loss is analyzed. The results of extensive experiments on four real-world datasets demonstrate that HCGR consistently outperforms state-of-the-art baselines by 0.43 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\%$</tex-math></inline-formula> -14.44 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\%$</tex-math></inline-formula> in terms of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$HitRate$</tex-math></inline-formula> , <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$NDCG$</tex-math></inline-formula> and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$MRR$</tex-math></inline-formula> .
Optimal Design of Video Player Tracking Algorithm in Basketball Game Based on Support Vector Machine

With the rapid growth of the number of videos, how to efficiently retrieve the required video information has become an urgent problem, and video annotation is the basis of video indexing and video search. Target tracking technology is of great significance in basketball game video, which is the basis of high-level semantic tasks such as video summary generation, player movement analysis, game strategy formulation and basketball event detection. Intelligent video monitoring system mainly refers to the automatic detection, segmentation, tracking, classification and higher level behavior analysis of objects in the monitoring scene without human intervention, so as to achieve the goal of “real-time monitoring and timely response.” In this paper, a real-time player segmentation and tracking algorithm is implemented to solve the problem of player team membership and tracking in basketball video. Compared with support vector machine algorithm, the two algorithms proposed in this paper have better robustness and accuracy.
Confluence Criteria for Logically Constrained Rewrite Systems

Abstract Numerous confluence criteria for plain term rewrite systems are known. For logically constrained rewrite system, an attractive extension of term rewriting in which rules are equipped with logical constraints, much less is known. In this paper we extend the strongly-closed and (almost) parallel-closed critical pair criteria of Huet and Toyama to the logically constrained setting. We discuss the challenges for automation and present , a new tool for logically constrained rewriting in which the confluence criteria are implemented, together with experimental data.
A Critical Review of the Demola Framework and Its Applicability in a Low-Income Economy: A Case Study of The Gambia

This paper critically reviews the appropriateness of the Demola Framework for adoption in a low-level income country. According to the World Bank, Low-income countries are developing countries with emerging markets or newly industrialized countries (Abubakar et al., 2019). The Demola framework has been developed and used in some European countries such as Finland, Germany, and Spain as a curriculum delivery approach for entrepreneurship education courses (De Carolis, 2019). The framework has been widely used in bridging the gap between the academia and industry, for example in an impact study conducted in Finland, it was established that due to the frameworks’ responsiveness to building synergies between students and industry, 7 polytechnics and other universities have adopted it as part of their curricula delivery approach (Catalá-Pérez et al., 2020). Based on these, its concepts are being tested in The Gambia, a country in West Africa where for the first time in the tertiary and higher education sector, a new policy decision has been rolled out to embed entrepreneurship training as part of the science, technology, and engineering education programme. This programme forms the first stage of a national development initiative to transform one of the public technical training institutions into a University of Applied Science Engineering and Technology that will be entrepreneurial and will be producing business minded graduates.
Weakly supervised fine-grained semantic segmentation via spatial correlation-guided learning

Weakly supervised semantic segmentation (WSSS) mainly adopts class activation map (CAM) to recognize different categories with generated pseudo masks of only image-level labels. Recently, many advanced works focus on learning the semantic correlation to refine the conventional CAM, which only identify sparse and discriminative semantic regions, severely weakening the further learning ability of spatial features. To copy with the above problem, a spatial correlation-guided learning framework is proposed to exploit the spatial and semantic correlation between adjacent pixels for weakly supervised fine-grained semantic segmentation (WSFGSS). From the spatial perspective, self-supervised multi-view clustering (SMC) is designed to fully mine spatial correlation by clustering of multiple views including scale, angle and position. Moreover, a hybrid self-supervised (HS) loss function is used to further promote the optimized speed and accuracy of three spatial representations. From the semantic perspective, the affinity matrix is applied to describe the semantic similarity between different pixels by building a weighted graph, and combine above robust pseudo label into a probability transition matrix. Therefore, the initial CAM is gradually corrected during the iterative optimization by the random walk algorithm. Finally, the refined CAM is utilized as supervision information for training the standard segmentation network effectively. Sufficient experimental results on BSDS500, PASCAL VOC 2012 and MS COCO datasets show that the proposed SMC method obtains more accurate pseudo labels than the recent unsupervised segmentation models. Meantime, with these pseudo labels, the proposed fine-grained framework achieves the state-of-the-art performance for WSFGSS.
Time Series Forecasting for Energy Production in Stand-Alone and Tracking Photovoltaic Systems Based on Historical Measurement Data

This article presents a time series analysis for predicting energy production in photovoltaic (PV) power plant systems, namely fixed and solar-tracking ones, which were located in the north-east of Poland. The purpose of one-day forecasts is to determine the effectiveness of preventive actions and manage power systems effectively. The impact of climate variables affecting the production of electricity in the photovoltaic systems was analyzed. Forecasting models based on traditional machine learning (ML) techniques and multi-layer perceptron (MLP) neural networks were created without using solar irradiance as an input feature to the model. In addition, a few metrics were selected to determine the quality of the forecasts. The preparation of the dataset for constructing the forecasting models was discussed, and some ways for improving the metrics were given. Furthermore, comparative analyses were performed, which showed that the MLP neural networks used in the regression problem provided better results than the MLP classifier models. The Diebold–Mariano (DM) test was applied in this study to distinguish the significant differences in the forecasting accuracy between the individual models. Compared to KNN (k-nearest neighbors) or ARIMA models, the best results were obtained for the simple linear regression, MLPRegressor, and CatBoostRegressor models in each of the investigated photovoltaic systems. The R-squared value for the MLPRegressor model was around 0.6, and it exceeded 0.8 when the dataset was split and separated into months.
SSD-MonoDETR: Supervised Scale-aware Deformable Transformer for Monocular 3D Object Detection

Transformer-based methods have demonstrated superior performance for monocular 3D object detection recently, which aims at predicting 3D attributes from a single 2D image. Most existing transformer-based methods leverage both visual and depth representations to explore valuable query points on objects, and the quality of the learned query points has a great impact on detection accuracy. Unfortunately, existing unsupervised attention mechanisms in transformers are prone to generate low-quality query features due to inaccurate receptive fields, especially on hard objects. To tackle this problem, this paper proposes a novel “Supervised Scale-aware Deformable Attention” (SSDA) for monocular 3D object detection. Specifically, SSDA presets several masks with different scales and utilizes depth and visual features to adaptively learn a scale-aware filter for object query augmentation. Imposing the scale awareness, SSDA could well predict the accurate receptive field of an object query to support robust query feature generation. Aside from this, SSDA is assigned with a Weighted Scale Matching (WSM) loss to supervise scale prediction, which presents more confident results as compared to the unsupervised attention mechanisms. Extensive experiments on the KITTI and Waymo Open datasets demonstrate that SSDA significantly improves the detection accuracy, especially on moderate and hard objects, yielding state-of-the-art performance as compared to the existing approaches. Our code will be made publicly available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/mikasa3lili/SSD-MonoDETR</uri> .
A robust multiple Unmanned Aerial Vehicles 3D path planning strategy via improved particle swarm optimization

Path planning algorithms for Unmanned Aerial Vehicles (UAVs) are essential in various domains, such as search and rescue operations, agriculture, and delivery services. Nonetheless, finding optimal flight paths in complex environments with obstacles remains challenging. This paper proposes a novel path planning algorithm that uses a Nash equilibrium approach based on game theory to strike a balance between the exploitation and exploration capabilities of UAVs. The algorithm is designed in a simulated flight environment with obstacles, utilizing the self-awareness and group awareness coefficients from particle swarm optimization(PSO) as participants in the game theory model. The proposed path planning algorithm significantly improves the navigation of multiple UAVs in complex scenarios with obstacles by achieving a balance between their exploitation and exploration capabilities. Simulation experiments demonstrate that the proposed method surpasses traditional approaches, exhibiting an average improvement of 32.57%, 32.17%, and 29.33% in the algorithm convergence time, average flight distance, and average flight time, respectively. The significance of this research lies in its contribution to advancing UAV path planning algorithms. By integrating game theory and PSO, the proposed method optimizes the exploration and exploitation capabilities of UAVs, leading to improved navigation and resource utilization.