Miracle-dz/UniFAR-FTUs huggingface dataset:
Multi-scale Progressive Complementary Fusion Network for Fine-Grained Visual Classification

In fine-grained visual classification(FGVC), the small inter-class variations and the large intra-class variations are always inherent attributes, so it is much more challenging than traditional classification tasks. Recent works mainly tackle this problem by employing attention mechanisms to locate the most discriminative parts. However, these methods tend to neglect other inconspicuous but distinguishable parts, and can not effectively fuse the features information of different scales and different degrees of discrimination. In this work, we propose a multi-scale progressive complementary fusion network (MPCF-Net) to tackle these problems. In particular, we propose: (i) A three-step multi-scale progressive training method, which employs the image slicer to generate puzzle images at different scales followed by multi-step progressive training. This enables the network to capture multi-granularity local feature information and gradually expand its attention to the global structural information as the training progresses for multi-granularity information fusion. (ii) A plug-and-play feature complementary enhancement module (FCEM) that explicitly enhances the features extracted by the current layer of the network, while also enabling the next layer of the network to extract potential complementary feature information to diversify the features. Our experiments were conducted on four FGVC benchmark datasets and yielded state-of-the-art or competitive results.
Ink Animation VR Design Based on Wireless Sensor and HTML5 Interactive Technology

Ink element is one of the unique cultural symbols in China, which has profound cultural heritage and spiritual connotation. Animation is a form of art design, and it is a good way to convey and express information in an intuitive artistic image. The use of ink and wash elements for animation design has important value and significance in the start of Chinese culture. It can develop animation design, innovate animation creation forms, and can highlight Chinese style. In this article, we have studied the creation process, creation format, and creation method of using angular digital technology to make ink VR design. It combines art and technology and combines practical experience with wireless sensors. Based on HTML5 interactive technology, we have studied its advantages more deeply, better introduced the theme of ink element animation, solved the problem of realizing ink effect in 3D virtual environment, and better introduced ink animation in virtual reality environment. During the production process, students will learn how to simulate digital objects, render ink and wash effects, build a virtual environment in the later stage, and explore the knowledge, operation, and meaning of ink and wash elements and the animation design process. This paper studies the wireless sensor and HTML5 interactive technology and applies it to the design of ink and wash animation VR, aiming to promote its vigorous development and application.
Chinese Relation Extraction of Apple Diseases and Pests Based on A Dual-Channel Neural Network

Relation extraction is one of the basic tasks in the construction of the knowledge graph and intelligent Q&A of apple diseases and pests. In view of multiple relation categories between apple disease and pest entities and the unbalanced distribution of relation data, we propose a novel model named DC-ARE for relation extraction based on dual-channel mechanism. Specifically, the dual channels are used to enhance the semantic representation and obtain richer features of data. The performance of DC-ARE is evaluated on AppleRE, a self-made apple disease and pest relation extraction dataset containing 23 relation categories. It realizes an F1-score of 94.08% under the AppleRE dataset. Compared to BLSTM and Att-BLSTM, the F1-score increases by 4.38% and 3.25%, respectively. The F1-scores on seven small samples are all higher than comparison models. The results demonstrate that the proposed model has better performance in relation extraction of apple diseases and pests, and can solve the problem of unbalanced distribution of relation data.
Recognition of activities of daily living based on the vertical displacement of the wrist

The upper limbs plays a key role in the performance of ADL, from support to total execution. However, Often rehabilitations therapies sacrifices the regaining of functionality to other functions as transportation. Therefore an automatic system capable of recognize ADL based on the information provided by upper limbs is needed. Lots of algorithms have been reported using inertial sensors with limited results. The aim of this work is to describe an algorithm to recognize some ADL performed with the upper limb, such as eating, drinking, talking by phone, combing hair and brushing teeth. The algorithm is based on an alternative novel sensor that provides information of the vertical displacement of the wrist relative to the shoulder, and can be used in a free-living environment. The detection system combines decision trees (DT) and Hidden Markov Models (HMM). Efficiencies reported goes from 61% up to 100%.
Optimal Proactive Caching for Multi-View Streaming Mobile Augmented Reality

Mobile Augmented Reality (MAR) applications demand significant communication, computing and caching resources to support an efficient amalgamation of augmented reality objects (AROs) with the physical world in multiple video view streams. In this paper, the MAR service is decomposed and anchored at different edge cloud locations to optimally explore the scarce edge cloud resources, especially during congestion episodes. In that way, the proposed scheme enables an efficient processing of popular view streams embedded with AROs. More specifically, in this paper, we explicitly utilize the notion of content popularity not only to synthetic objects but also to the video view streams. In this case, popular view streams are cached in a proactive manner, together with preferred/popular AROs, in selected edge caching locations to improve the overall user experience during different mobility events. To achieve that, a joint optimization problem considering mobility, service decomposition, and the balance between service delay and the preference of view streams and embedded AROs is proposed. To tackle the curse of dimensionality of the optimization problem, a nominal long short-term memory (LSTM) neural network is proposed, which is trained offline with optimal solutions and provides high-quality real-time decision making within a gap between 5.6% and 9.8% during inference. Evidence from a wide set of numerical investigations shows that the proposed set of schemes owns around 15% to 38% gains in delay and hence substantially outperforms nominal schemes, which are oblivious to user mobility and the inherent multi-modality and potential decomposition of the MAR services.
JOINT SUPER-RESOLUTION AND IMAGE RESTORATION FOR PLÉIADES NEO IMAGERY

Abstract. Modern Earth Observation optical satellite systems, such as Airbus’s Pleiades Neo (PNeo) push the boundaries of high spatial resolution by providing commercial imagery products with up to 30cm ground sampling distance (GSD). To further enhance the quality of the images, the in-space imaging system is usually complemented by on-ground image restoration processing, such as deconvolution and denoising (Latry et al., 2012). Recent advances leverage Convolutional Neural Networks (CNNs) to improve the image restoration quality (K. Zhang et al., 2021a). Single Image Super-Resolution (SISR), or Zoom, the process of obtaining a higher resolution (HR) image from a lower resolved (LR) source, has recently gained traction for both medium resolution sensors such as Sentinel 2 (Lanaras et al., 2018) and high resolution such as Pléiades and GeoEye-1 (Zhu et al., 2020). This process further enhances the resolution of the image to improve downstream applications such as mapping (L. Zhang et al., 2021) and small objects recognition (Shermeyer and Van Etten, 2019). While SISR for remote sensing has been successfully tackled using CNNs (Rohith and Kumar, 2021) the main challenge for reaching acceptable image quality performance lies in the generation of realistic LR/HR training pairs (K. Zhang et al., 2021b). In this paper, we propose:a dedicated simulation chain leveraging extremely-high-resolution (EHR) aerial imagery to generate realistic 30cm Pléiades Neo images and their corresponding fully restored HR equivalent at 15cm GSDA residual-based CNN architecture which we train to jointly restore and zoom the images All contributions are assessed on real PNEO images.We deployed the trained models in a production context, to enhance the full Pléiades Neo products – with a swath of 47k pixels – in an efficient and scalable manner.
Self-Organizing Fuzzy Belief Inference System for Classification

Evolving fuzzy systems (EFSs) are widely known as a powerful tool for streaming data prediction. In this article, a novel zero-order EFS with a unique belief structure is proposed for data stream classification. Thanks to this new belief structure, the proposed model can handle the interclass overlaps in a natural way and better capture the underlying multimodel structure of data streams in the form of prototypes. Utilizing data-driven soft thresholds, the proposed model self-organizes a set of prototype-based if–then fuzzy belief rules from data streams for classification, and its learning outcomes are practically meaningful. With no requirement of prior knowledge in the problem domain, the proposed model is capable of self-determining the appropriate level of granularity for rule-based construction, while enabling users to specify their preferences on the degree of fineness of its knowledge base. Numerical examples demonstrate the superior performance of the proposed model on a wide range of stationary and nonstationary classification benchmark problems.
Serum NOX4 as a Promising Prognostic Biomarker in Association with 90-Day Outcome of Severe Traumatic Brain Injury

Purpose Nicotinamide adenine dinucleotide phosphate oxidase 4 (NOX4) is related to brain oxidative stress. We attempted to examine the association between serum NOX4 levels, severity and prognosis of severe traumatic brain injury (sTBI). Methods We measured serum NOX4 levels in 105 patients with sTBI. Trauma severity was assessed using Glasgow coma scale (GCS) and Rotterdam computed tomography (CT) classification. Study outcome data on death and worst outcome (Glasgow outcome scale score of 1–3) were collected at 90 days after trauma. Multivariate analyses were performed to determine independent factors for overall survival and worst outcome. Area under receiver operating characteristic curve (AUC) was estimated to assess prognostic predictive ability. Results Serum NOX4 levels were tightly correlated with GCS score (t=−5.843, P < 0.001) and Rotterdam CT score (t = 4.231, P < 0.001). During 90 days of follow-up, 50 participants (47.6%) experienced a worse outcome, 28 (26.7%) died and the mean overall survival time was 71.9 days (95% confidence interval (CI), 65.7–78.1 days). Serum NOX4 was independently associated with an increased risk of short overall survival (hazard ratio, 1.129; 95% CI, 1.039–1.228) or worse outcome (odds ratio, 1.053; 95% CI, 1.014–1.095). Serum NOX4 levels had a certain predictive value for the patient’s risk of mortality (AUC, 0.803; 95% CI, 0.714–0.874) or worse outcome (AUC, 0.780; 95% CI, 0.689–0.855). Moreover, its AUC was in the range of GCS score and Rotterdam CT score (both P > 0.05) and it significantly improved their AUCs (both P < 0.05). Conclusion Serum NOX4 levels in the acute phase of sTBI were associated with trauma severity, an increased risk of mortality and worse outcome, suggesting that serum NOX4 could be an important prognostic factor for sTBI.
A Design Optimization Method with Sparse Scattered Data and Evolutionary Computation

Engineering design can be regarded as an iterative optimization process. This process is difficult because of two main problems: the first is that computer-aided engineering (CAE) is time-consuming in terms of evaluating design solutions, while the second is the high dimensionality of design solutions. In the research community, a surrogate model is proposed to deal with the first problem while an evolutionary algorithm is adopted for the second. In this work, we develop a new method with only sparse scattered data, which is very common in many practical scenarios. The surrogate model can also assign a penalty factor for the predicted value, and this penalty factor can be used as one of the targets of the evolutionary algorithm to balance global exploration and local exploit. We also adopt a new evolutionary strategy, which can search high-dimensional space. Three groups of experiments are conducted to validate the proposed methods. The experimental results show that the surrogate model can predict performance and the corresponding penalty factor, the evolutionary strategy is better in terms of searching high-dimensional space compared with other evolutionary strategies, and the whole method can generate new design solutions that are near to the known design solutions. The experimental results show that this method can be used in practical scenarios, especially where they only have sparse scattered data.
Vertical Distribution of Nitrate, Phosphate, and Abundance of Planktonic Diatoms in Pandan Waters, Tapanuli Tengah Regency, North Sumatra

This research was conducted in February 2021 in Pandan Waters, Central Tapanuli Regency, North Sumatra Province. The purpose of this study was to analyze the concentration of nitrate and phosphate, analyze the composition distribution and abundance of planktonic diatoms, and analyze the relationship between nitrate and phosphate concentrations with an abundance of planktonic diatoms at different depths in Pandan Waters. The method used is the survey method. The results of the analysis of vertical concentrations of nitrate and phosphate in Pandan waters tend to increase with increasing depth, while the results of vertical analysis of planktonic diatom abundance in Pandan waters tend to decrease with increasing depth. The results of a simple linear regression test showed that the relationship between nitrate concentration and abundance of planktonic diatoms at different depths had a strong (negative) relationship, while the relationship between phosphate concentration and planktonic diatom abundance at different depths had a very weak (negative) relationship
Evaluation of advances in cortical development using model systems

Compared with that of even the closest primates, the human cortex displays a high degree of specialization and expansion that largely emerges developmentally. Although decades of research in the mouse and other model systems has revealed core tenets of cortical development that are well preserved across mammalian species, small deviations in transcription factor expression, novel cell types in primates and/or humans, and unique cortical architecture distinguish the human cortex. Importantly, many of the genes and signaling pathways thought to drive human‐specific cortical expansion also leave the brain vulnerable to disease, as the misregulation of these factors is highly correlated with neurodevelopmental and neuropsychiatric disorders. However, creating a comprehensive understanding of human‐specific cognition and disease remains challenging. Here, we review key stages of cortical development and highlight known or possible differences between model systems and the developing human brain. By identifying the developmental trajectories that may facilitate uniquely human traits, we highlight open questions in need of approaches to examine these processes in a human context and reveal translatable insights into human developmental disorders.
BOFSanitizer: Efficient locator and detector for buffer overflow vulnerability

Buffer overflow is one of the most serious and common vulnerability that using memory beyond the allocated memory. It can cause many severe threats, including system crash, arbitrary code execution and data corruption. Buffer overflow vulnerability has two main characteristics, one is that buffer overflow vulnerability may not be discovered after its path has been executed. The other is that not every buffer overflow vulnerability could cause crash. Therefore, existing program analysis tools have more or less deficiencies in detecting buffer overflow vulnerability. This paper presents a BOF-Sanitizer method for buffer overflow vulnerability detection. We proposed a pre-process method that combine vulnerability metric and rank, which can effectively avoid the exploration of unnecessary paths and quickly locate potential bug positions. We present a custom memory model of concolic execution and a boundary detection engine based on a low fat pointer, which not only ensure that the executed path is safe, but also effectively reduces the system performance overhead through the real time interacting with the analysis engine and solver in the process of dynamic detection. We implement a prototype system of BOFSanitizer to detect buffer overflow in different application, the result show that BOFSanitizer accurately discovers 88% buffer overflow target points and the detection rate improve over 50% with compare to simple combination of existing program analysis tools.
Innovation in the Public Sector: Insights into the Job Design that Enhances Public Servants’ Innovative Behavior

ABSTRACT The purpose of this paper is to examine the distinct role of every core job characteristic for public servants’ innovative behavior, taking also into consideration the type of job position (front-line versus back-office). Evidence from 358 Greek public servants suggests that autonomy, task significance, feedback from the job itself, and skill variety do significantly impact employee innovative behavior. Moreover, significant differences regarding these relationships between front-line and back office public servants were revealed, as the former’s innovative behavior is mostly affected by skill variety while the latter’s by task significance. In addition, feedback and skill variety affects only the innovative behavior of Greek front-line public servants. Practical implications focus on redesigning jobs, as a means for improving innovative behavior in public organizations.
Spherical Fuzzy Decision-Making Approach Integrating Delphi and TOPSIS for Package Tour Provider Selection

The spherical fuzzy sets were recently developed among various fuzzy sets to handle the hesitancy representation issue in multiple criteria decision-making (MCDM) problems, where experts provide information about attributes in the form of spherical fuzzy numbers using linguistic variables. The main purpose of this study is to develop a novel approach integrating Delphi Technique for Order of Preference by Similarity to Ideal Solution based on spherical fuzzy sets (SF-Delphi and SF-TOPSIS). First, the SF-Delphi technique is suggested to derive a valid set of critical criteria based on qualitative information and linguistic preferences. Second, the SF-TOPSIS approach is utilized to rank alternatives based on different spherical fuzzy aggregating operators. Hence, to validate the effectiveness of the proposed methodology, an empirical case study of package tour provider selection is given. Seventeen critical criteria related to four main dimensions (price, service quality, information and technology, and location) were shortlisted and validated from literature and expert opinions. Ten potential package tour providers from Vietnam were ranked in this study. A comparative analysis was conducted to check the proposed methodology’s robustness and validity. The results indicated that the novel SF-Delphi technique may become very helpful for dealing with critical factors, and SF-TOPSIS could be applied to decision problems in uncertain data environments. Furthermore, this research’s findings imply that tour operators should emphasize the most critical attributes to increase the appeal and competitiveness of their package tour products.
Long-Term Cost-Effectiveness of Metro Networks Exploiting Point-to-Multipoint Transceivers

A comprehensive analysis of the long-term benefits of deploying point-to-multipoint transceivers in metro networks, taking into account the expected evolution of transceiver technology, is reported. Simulation results obtained over a reference network show that the savings over point-to-point transceivers can be maintained at approximately 35% for a wide range of traffic loads, highlighting the long-term cost-effectiveness of adopting point-to-multipoint transceivers.
Angiogenesis in Spinal Cord Injury: Progress and Treatment

Traumatic spinal cord injury (SCI) provokes the onset of an intricate pathological process. Initial primary injury ruptures local micro-neuro-vascularcomplex triggering the commencement of multi-factorial secondary sequences which exert significant influence on neurological deterioration progress. Stimulating by local ischemia, neovascularization pathways emerge to provide neuroprotection and improve functional recovery. Although angiogenetic processes are prompted, newly formed vascular system is frequently inadequate to distribute sufficient blood supply and improve axonal recovery. Several treatment interventions have been endeavored to achieve the optimal conditions in SCI microenvironment, enhancing angiogenesis and improve functional recovery. In this study we review the revascularization pathogenesis and importance within the secondary processes and condense the proangiogenic influence of several angiogenetic-targeted treatment interventions.
Age-Related Changes in the Fibroblastic Differon of the Dermis: Role in Skin Aging

Skin aging is a multi-factorial process that affects nearly every aspect of skin biology and function. The processes developing in the skin during aging are based on fundamental molecular mechanisms associated with fibroblasts, the main cellular population of the dermis. It has been revealed that the amount of fibroblasts decreases markedly with age and their functional activity is also reduced. This inevitably leads to a decrease in the regenerative abilities of the skin and the progression of its aging. In this review we consider the mechanisms underlying these processes, mainly the changes observed with age in the stem/progenitor cells that constitute the fibroblastic differon of the dermis and form their microenvironment (niches). These changes lead to the depletion of stem cells, which, in turn, leads to a decrease in the number of differentiated (mature) dermal fibroblasts responsible for the production of the dermal extracellular matrix and its remodeling. We also describe in detail DNA damages, their cellular and systemic consequences, molecular mechanisms of DNA damage response, and also the role of fibroblast senescence in skin aging.