Reviewerly/exHarmony huggingface dataset:
Agile software development and UX design: A case study of integration by mutual adjustment

Agility is an overarching ideal for empirically-driven software development processes that embrace change in order to improve quality, economy, and simplicity. While the pursuit of Agility has held prominence in software practice and research for over two decades, user experience (UX) designers struggle to integrate their work processes with agile software development. As empirical processes are constantly evolving, so is this integration struggle for UX designers. We, therefore, present an industrial case study of how a Danish software company integrates UX design and agile software development. We conducted a case study involving (a) one iteration of individual interviews with 10 employees (four UX designers, three software developers, two project managers, and one solution architect) and (b) a follow-up iteration consisting of a workshop with 6 employees (three UX designers, two solution architects, and one project manager) two years later. We analyzed how the company's approach to integration with 'upfront design' and 'work in parallel' involve mutual adjustments as opposed to assimilation or separation of UX design and software development. Our analysis shows how integration through mutual adjustments made distinct contributions to UX designers' and software developers' pursuit of Agility. They experienced notably different work processes that still dealt effectively with change and contributed to quality, economy, or simplicity. Nevertheless, as shown from a follow-up workshop two years after our first interviews, these processes were still susceptible to integration struggles over time. We conclude that integration based on mutual adjustment potentially makes Agility for UX designers and software developers different and mutually complementary. This integration contrasts with assimilation, which potentially makes their Agility mutually indistinguishably, and with separation, which makes their Agility different and mutually competing.
ICT and current trends as a path to STEM education: implementation and prospects

The aim of the study is to build a model for the introduction of STEM education in institutions of general secondary education, to demonstrate the experience of using ICT in STEM education. Research objectives: to determine the role and place of information and communication technologies in the model of introduction of STEM education in general secondary education institutions for the formation of key competencies; to find innovative solutions to create conditions that will promote the interest of student youth in the choice of STEM professions. Object of research: the model of the introduction of STEM education in institutions of general secondary education Subject of research: information and communication technologies in the model of introduction of STEM education in institutions of general secondary education as a component of the school educational environment. Analysis of scientific publications makes it possible to determine the role and place of ICT in the model of introduction of STEM education in general secondary education institutions. The presented experience of the systemic implementation of STEM education in general secondary education institutions allows the formation of professional competencies of teachers in the field of STEM education and can be useful in their professional activities. The study shows that the systemic implementation of STEM technologies in the educational process increases the effectiveness of training, the level of motivation of participants in the educational process and the quality of knowledge in the subjects of the natural science cycle, and also contributes to the formation of key competencies of students.
A prior-guided deep network for real image denoising and its applications

Recently, the popularity of deep learning in computer vision has brought about a rapid development of deep convolutional neural network (CNN)-based image denoising algorithms. Most of the existing methods have shown their superiority in removing additive white Gaussian noise (AWGN) with a specific noise level, but have limited performance in handling much more complicated real noise. To address this problem, we develop a novel prior-guided dynamic tunable network (PDTNet) for real image denoising. Firstly, we break up the image denoising optimization problem into noise estimation and image reconstruction sub-problems, and employ the inference process to guide the architecture design of PDTNet. Then, we exploit an internal and external dual modulation (IEDM) scheme to achieve real image denoising. Specifically, a designed global spatial and channel attention (GSCA) is embedded in the external estimator and internal stacked dynamic residual blocks (DRBs) to extract global features from the noise prior and iterative image features, respectively. Next, a dynamic weight generator block (DWGB) is leveraged to adaptively combine external and internal features at each DRB. In addition, we also analyze a realistic real noise model from the physical perspective to generate synthetic noisy images for model training. Experimental results show the superiority of PDTNet over state-of-the-arts both quantitatively and visually and its applications on different networks and tasks.
A Bi-level representation learning model for medical visual question answering

Medical Visual Question Answering (VQA) targets at answering questions related to given medical images and it contains tremendous potential in healthcare services. However, researches on medical VQA are still facing challenges, particularly on how to learn a fine-grained multimodal semantic representation from relatively small volume of data resources for answer prediction. Moreover, the long-tailed distribution labels of medical VQA data frequently result in poor performance of models. To this end, we propose a novel bi-level representation learning model with two reasoning modules to learn bi-level representations for the medical VQA task. One is sentence-level reasoning to learn sentence-level semantic representations from multimodal input. The other is token-level reasoning that employs an attention mechanism to generate a multimodal contextual vector by fusing image features and word embeddings. The contextual vector is used to filter irrelevant semantic representations from sentence-level reasoning to generate a fine-grained multimodal representation. Furthermore, a label-distribution-smooth margin loss is proposed to minimize generalization error bound of long-tailed distribution datasets by modifying margin bound of different labels in training set. Based on standard VQA-Rad dataset and PathVQA dataset, the proposed model achieves 0.7605 and 0.5434 on accuracy, 0.7741 and 0.5288 on F1-score, respectively, outperforming a set of state-of-the-art baseline models.
Estimation of global solar radiation using sunshine-based models in Ethiopia

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A robust registration method for UAV thermal infrared and visible images taken by dual-cameras

Automatic registration of unmanned aerial vehicle (UAV) thermal infrared and visible (TIR&V) images is fundamental for subsequent applications. However, few studies address this issue due to significant radiation gap, shape gap, and texture gap among TIR&V images. The area-based methods are not able to satisfy the accuracy and robustness of location at the same time, while the image pyramid-based methods are computationally expensive. To alleviate these problems, we proposed a so-called TWMM method for the registration of UAV TIR&V images taken by the camera equipped with both thermal infrared and visible sensors. TWMM is realized by combining Template matching with Weights, Multilevel local max-pooling, and Max index backtracking. TWMM consists of four steps: (1) computing similarity maps of the atomic patches using template matching with weights; (2) building pyramid similarity maps using multilevel local max-pooling; (3) deducing the corresponding points (CPs) from top to bottom using max index backtracking; and (4) eliminating outliers and estimating homography. Among the four steps, step 1 and step 2 are used to compute the similarity maps of patches with different sizes; step 3 and step 4 are used to deduce CPs and estimate homography with multiple similarity maps. TWMM was comprehensively evaluated with 600 UAV image pairs under four different scenes and also compared with current methods (i.e. SIFT, SURF, RIFT, RCB, TFeat, HardNet, RANSAC_Flow, HOPC, and CFOG). These image pairs have multiple features, i.e., different land covers, spatial resolutions, and illumination conditions, etc. Results indicate that TWMM achieves an 86.0% correct CP ratio (RCP) and a 96.0% correct matching rate (CMR) for all test images, which is a 15.1% improvement and 11.6% improvement, respectively, over the best state-of-the-art methods. TWMM also shows better robustness than other methods for weak-light images, achieving a 20.7% improvement in RCP and a 28.1% improvement in CMR. Therefore, TWMM is an effective and robust method for UAV TIR&V image registration and has good ability under different scenes.
Robust Hyperspectral Object Tracking by Exploiting Background-Aware Spectral Information With Band Selection Network

Deep color trackers mainly use pre-trained convolutional neural networks for classification and regression, but it is difficult to discriminate targets in complex backgrounds for its limited spectral information. Compared with color video, hyperspectral videos provide better discriminative ability due to the abundant material-based information. However, it is hard to train a robust deep model for hyperspectral videos. The key issues are that there exists much redundant information in hyperspectral videos and the training samples are inadequate. In this paper, a new background aware hyperspectral tracking (BAHT) method is designed for hyperspectral tracking task. Our method firstly designs a background aware band selection module to preserve bands that can better recognize a target from backgrounds. Then the selected bands are input to backbone networks, which are pre-trained on color videos, to describe the appearances of targets with deep semantic features. Experiments on hyperspectral video tracking dataset illustrate the good performance of BAHT tracker compared with popular color and hyperspectral trackers.
Unmanned Aerial Vehicles Traffic Management Solution Using Crowd-Sensing and Blockchain

Unmanned aerial vehicles are gaining immense attention in diverse commercial and civil applications. However, adopting this technology will rely on management services that support safe operation. UAV traffic management systems (UTM) aim to facilitate safe, efficient, and fair access to the low-altitude airspace. However, these systems are centralized and lack protocols for secure interaction between the system agents such as authorities, service providers, and end-users. Another limitation of proposed UTM architectures is the absence of efficient mechanisms to enforce airspace rules and regulations. To address these issues, we present a decentralized UTM protocol that controls access to airspace and ensures the security objectives of confidentiality, integrity, and availability. For this purpose, we exploit features of the blockchain and smart-contract technologies. In addition, we employ a mobile crowdsensing technique to seamlessly enforce airspace rules and regulations that govern UAV operations. The solution is implemented on top of the Ethereum platform and evaluated using four different tools for smart-contract verification. We also provide security and performance analysis of the solution. For reproducibility, we made our implementation publicly available on Github.
Development of a recurrent neural networks-based NMPC for controlling the concentration of a crystallization process

Crystallization is a separation and purification process relevant to industrial sectors, such as pharmaceuticals. The maximum possible recovery of solute amount is one of its goals, and the temperature profile is crucial to achieve this. In this work, neural networks-based models were developed to predict the solute concentration of a batch crystallization process and used as internal model in a nonlinear model predictive controller. Three different neural network architectures were considered: the multilayer perceptron (MLP) network, the echo state network (ESN), and the long short-term memory (LSTM). The dataset used for training and testing applied a co-teaching learning algorithm, which uses simulated and experimental data from the batch crystallization of the potassium sulfate (K2SO4). The three network structures were trained to predict the solute concentration one step ahead, using the current temperature and concentration values as feed, and the predictive performances were evaluated for larger prediction horizons. A nonlinear model predictive controller (NMPC) based on the ESN, the most efficient neural network design, was successfully applied to the batch crystallization process to maintain the solute concentration on its desired trajectories by manipulating the operating temperature. The controller's behavior was studied for three different set-points of concentration and supersaturation, varying the initial concentration. The performance of the proposed NMPC was compared to a controller based on a more traditional approach, using the MLP network as internal model.
Towards prior gap and representation gap for long-tailed recognition

Most deep learning models are elaborately designed for balanced datasets, and thus they inevitably suffer performance degradation in practical long-tailed recognition tasks, especially to the minority classes. There are two crucial issues in learning from imbalanced datasets: skew decision boundary and unrepresentative feature space. In this work, we establish a theoretical framework to analyze the sources of these two issues from Bayesian perspective, and find that they are closely related to the prior gap and the representation gap, respectively. Under this framework, we show that existing long-tailed recognition methods manage to remove either the prior gap or the presentation gap. Different from these methods, we propose to simultaneously remove the two gaps to achieve more accurate long-tailed recognition. Specifically, we propose the prior calibration strategy to remove the prior gap and introduce three strategies (representative feature extraction, optimization strategy adjustment and effective sample modeling) to mitigate the representation gap. Extensive experiments on five benchmark datasets validate the superiority of our method against the state-of-the-art competitors.
What Matters in Language Conditioned Robotic Imitation Learning Over Unstructured Data

A long-standing goal in robotics is to build robots that can perform a wide range of daily tasks from perceptions obtained with their onboard sensors and specified only via natural language. While recently substantial advances have been achieved in language-driven robotics by leveraging end-to-end learning from pixels, there is no clear and well-understood process for making various design choices due to the underlying variation in setups. In this letter, we conduct an extensive study of the most critical challenges in learning language conditioned policies from offline free-form imitation datasets. We further identify architectural and algorithmic techniques that improve performance, such as a hierarchical decomposition of the robot control learning, a multimodal transformer encoder, discrete latent plans and a self-supervised contrastive loss that aligns video and language representations. By combining the results of our investigation with our improved model components, we are able to present a novel approach that significantly outperforms the state of the art on the challenging language conditioned long-horizon robot manipulation CALVIN benchmark. We have open-sourced our implementation to facilitate future research in learning to perform many complex manipulation skills in a row specified with natural language.
NHFNET: A Non-Homogeneous Fusion Network for Multimodal Sentiment Analysis

Fusion technology is crucial for multimodal sentiment analysis. Recent attention-based fusion methods demonstrate high performance and strong robustness. However, these approaches ignore the difference in information density among the three modalities, i.e., visual and audio have low-level signal features and conversely text has high-level semantic features. To this end, we propose a non-homogeneous fusion network (NHFNet) to achieve multimodal information interaction. Specifically, a fusion module with attention aggregation is designed to handle the fusion of visual and audio modalities to enhance them to high-level semantic features. Then, cross-modal attention is used to achieve information reinforcement of text modality and audio-visual fusion. NHFNet compensates for the differences in information density of different modalities enabling their fair interaction. To verify the effectiveness of the proposed method, we set up the aligned and unaligned experiments on the CMU-MOSEI dataset, respectively. The experimental results show that the proposed method outperforms the state-of-the-art. Codes are available at https://github.com/skeletonNN/NHFNet.
Global Path Planning Method by Fusion of A-star Algorithm and Sparrow Search Algorithm

A global path planning method (A-SSA) that integrates the A-star algorithm and Sparrow Search Algorithm (SSA) is proposed for the shortest path planning problem of Automated Guided Vehicles (AGV) in a static raster environment. The first stage of the method uses the sparrow algorithm to obtain several key grid points in the raster map, and then uses the A-star algorithm to connect these grid points; the second stage uses the ray method to remove the redundant nodes, and then uses the Bessel curve to generate a continuous, collision-free and smooth shortest path after obtaining the simplified vital nodes. The back-off mechanism is studied for the deadlock problem in path planning, simulation experiments are conducted for the three algorithms within a 30x30 raster map with obstacle coverage of 20%, 25%, 30%, 35%, and 40%, respectively, and the experimental results show that the path length planned by the A-SSA method is the shortest, which proves the effectiveness of the method and can provide a The experimental results show that the path length of the A-SSA method is the shortest, which demonstrates the effectiveness of the method and can provide some reference for the shortest path planning of AGV.
Watermark-Preserving Keypoint Enhancement for Screen-Shooting Resilient Watermarking

Screen-shooting resilient (SSR) watermark is a special kind of robust watermarking. One can extract the watermark message even the embedded image communicates via a physical screen to the camera channel. The keypoint-based SSR watermarking is one promising solution to realize such screen-to-camera communication. The enhanced keypoints were used to locate the embedding region and then perform watermark embedding. However, the keypoint-based SSR watermarking treats the critical two steps, keypoint enhancement and watermark embedding, independently, neglecting their inter-play. This work proposes a watermark-preserving keypoint enhancement algorithm for SSR watermarking. Specifically, we resort to a convex constrained optimization framework to unify keypoint enhancement and watermark embedding. Multiple constraints are imposed to simultaneously ensure the watermark validity and blind synchronization of embedding regions. Our method enables jointly optimizing the watermarking distortion and keypoint enhancement. The proposed method achieves superior watermark extraction accuracy while retaining better watermarked image quality when compared with previous works.
GLTA-GCN: Global-Local Temporal Attention Graph Convolutional Network for Unsupervised Skeleton-Based Action Recognition

Unsupervised skeleton-based action recognition has attracted increasing attention. Existing methods have several limitations: (1) Many actions are highly related to local joints, which is often neglected. (2) Most methods directly employ joint coordinates as frame feature and do not utilize skeleton graph, e.g., topological information. (3) Long-range dependency is not captured well. In this work, a novel unsupervised method called Global-Local Temporal Attention Graph Convolutional Network (GLTA-GCN) is proposed to alleviate the above problems. The network consists of two branches, local and global branches. Each one utilizes graph convolution units and self-attention mechanism to better extract spatio-temporal features. Furthermore, two loss functions are designed to constrain the model to extract more essential local joint feature and maintain intrinsic structural information. Extensive experiments demonstrate that GLTA-GCN achieves state-of-the-art performance. Our code is released on https://github.com/HaoyueQiu/GLTA-GCN.
COVID-19 vaccine and post-pandemic recovery: Evidence from Bitcoin cross-asset implied volatility spillover

This paper examines implied volatility spillovers and connectedness between Bitcoin and a broad range of traditional financial assets (U.S. equity market, gold, crude oil, emerging markets and developing markets) from January 8, 2019 to January 20, 2022. Vector Auto-Regression and Generalized Forecast Error Variance Decomposition are used to compare results before COVID-19, during COVID-19 and after the vaccine becomes available. Results indicate higher connectedness during COVID-19 but very low connectedness after the vaccine is available, signaling recovery in financial markets. We also find that Bitcoin is a strong transmitter of volatility during COVID-19.
Influence prediction model for marketing campaigns on e-commerce platforms

E-commerce platforms are committed to planning various marketing campaigns to make profits, and the impact of marketing campaigns is determined by the number of consumers, as well as the good or bad planning scheme. Therefore, accurate prediction of user behavior and comprehensive analysis of marketing campaign trends can help e-commerce platforms to identify quality marketing campaigns, which are crucial for developing e-commerce platforms. In this paper, we propose a model for predicting the influence trend of marketing campaigns based on the key information between group users and marketing campaigns on e-commerce platforms. To begin, text representation learning is used to unify user representations to address the multi-label nature of platform users, considering that text representation learning can extract deeper correlations between different label information and learn key information in the marketing campaign-user network by mining the correlations between user labels. Furthermore, considering the leading power of key users in the e-commerce platform, the influential spread network is constructed for the characteristics of the group user network under the marketing campaign using the locality-sensitive hashing (LSH) algorithm and combined with the mining of key information in the marketing campaign-user network. Finally, to address the time-series nature of influence development, the influence spread network is fused, and the contextual relationships of the influence spread network are learned using a recurrent neural network (RNN) algorithm to propose a model for predicting the influence trend of marketing campaigns based on key information. Experiments have shown that the model can predict user engagement behavior in the context of a marketing campaign, as well as dynamically depict changes in trends in the influence of the campaign.