Miracle-dz/UniFAR-FTUs huggingface dataset:
3D Convolutional Neural Network for Hyperspectral Image Classification Using Generative Adversarial Network

Recently, hyperspectral image (HSI) classification has become a hot spot in the field of machine learning. Unlike traditional black and white dual-channel images or R, G, B three-channel images, HSI has multiple channels in the spectral dimension, one channel captures light of a specified wavelength. Hyperspectral data sets are usually three-dimensional. In order to extract the spectral spatial features in such images and differentiate images from unknown classes, We have introduced domain adaptation technology, which is a well established technique for appling an algorithm trained in one or more “source domains” to a different (but related) “target domain”, and proposed a framework based on Three-dimensional Convolutional Neural Network (3D-CNN), combined with Generative Adversarial Networks (GAN) to deal with unknown classes hyperspectral image classification. Our framework can achieve high-precision classification on hyperspectral images. Compared with other deep learning based methods, Our model has advantages in many aspects, such as universality for different hyperspectral data sets, and requiring fewer parameters and epochs during training. We use datasets of Pavia University scene to evaluate the adaptability of our method on the open set domain. The classification accuracy of the proposed method for unknown categories is 87.16% on PaviaU and 99.43% on Salinas.
Multilevel Capsule Weighted Aggregation Network Based on a Decoupled Dynamic Filter for Remote Sensing Scene Classification

The improvement of remote sensing scene classification(RSSC) by effectively extracting discriminant representations for complex and diverse scenes remains a challenging task. The capsule network(CapsNet) can encode the spatial relationship of features in an image, which exhibits encouraging performance. Nevertheless, the original CapsNet is unsuitable for RSSC with complex image background. In addition, conventional neural network methods only use the last features from their last convolutional layer and discount the intermediate features with complementary information. To search the additional information in intermediate convolutional layers and increase the performance of feature aggregation, this paper proposes a multilevel capsule weighted aggregation network (MCWANet) based on a decoupled dynamic filter(DDF), in which a new multilevel capsule encoding module and a new capsule sorting pooling (CSPool) method are implemented by combining the advantageous attributes of a residual DDF block, weighted capsule aggregation, and the new CSPool method. Extensive experiments on two challenging datasets, AID and NWPU-RESISC45, demonstrate that multilevel and multiscale features can be extracted and fused to extract semantically strong feature representation and that the proposed MCWANet performs competitively in RSSC.
Next-Generation Neural Networks: Capsule Networks With Routing-by-Agreement for Text Classification

These days, neural networks constantly prove their high capacity for nearly every application case and are considered as key technology for learning systems. However, neural networks need to continuously evolve for managing new arising challenges like increasing task complexity, explainability of decision making processes, expanded problem domains, providing resilient and robust systems etc. One possible enhancement of traditional neural networks constitutes the innovative Capsule Network (CapsNet) technology, which combines the expressiveness of distributed entity representations with an intelligent and interpretable signal propagation, named as routing-by-agreement. Since CapsNets represent a relatively young acquirement, further research is essential for gaining profound knowledge about CapsNet theory and best practices for diverse application areas. This paper wants to contribute to the progress of CapsNets for the task of text classification. For this purpose, various research questions about this technology get formulated and experimentally answered with the aid of six selected datasets. In addition, this paper serves as a possible starting point for researchers as well as for practitioners to deal with CapsNets in the text domain, by supplying a survey about its theory, text classification basics and the combination of both areas. The analysis results empirically prove the robustness of CapsNets with routing-by-agreement for a wide spectrum of net architectures, datasets and text classification tasks. Hence, CapsNets can be viewed as a next-generation neural network technology, which offers high potential as text classification method and should be topic of future research.
Construction and application of digital strawberries based on point cloud data

The construction of digital crops is important for understanding the morphological structure of crops and realizing agricultural information management. In this study, a laboratory-made high-throughput phenotyping robot was used to achieve a fast three-dimensional (3D) scan of real strawberries. The feature points of strawberry leaves were extracted from the 3D point cloud data, and the leaves were triangularly meshed via the Delaunay triangulation algorithm, and then the texture and color were mapped to the corresponding meshes to generate digital leaves. For fruit inflorescences and stolons, the non-uniform rational basis spline surfaces were first generated based on the 3D point cloud data, and then the collected texture and color were mapped to the surfaces. Finally, all the surfaces of corresponding organs were connected using the T-sample seamless stitching algorithm. The organs were combined to form a complete digital strawberry. The integration of 3D laser scanning technology and strawberry structure characteristics makes it easy to implement the digitization, with the advantages of comprehensive information and straightforward expandability.
The rise in European unemployment: a bargaining perspective

Purpose – To investigate the reasons for the persistent rise in European (Euro area) unemployment during the 1990s, especially compared to the USA. Design/methodology/approach – An econometric model of the European labour market is estimated based on the bargaining framework outlined by McDonald and Solow. A data set is constructed by aggregating individual country data. Because of the lack of adequate data, the supply side is treated as an unobserved component and estimated using the Kalman Filter. Findings – The rise in European unemployment is predominately due to the slow and incomplete reaction of the supply side to negative demand shocks in the labour market. The traditional factors which generate low flexibility in the labour market are to blame. Research limitations/implications – The extent to which the differences in unemployment between the Euro area and the USA can be accounted for by differences in labour productivity is identified as an important issue for future research. Originality/value – The paper shows how standard bargaining models can be used to explain poor unemployment performance in the Euro area. The approach taken is original in the sense that the European labour market is modelled as a single entity rather than a collection of individual countries, and that unobserved component models are used to overcome the lack of supply side data.
Proactive Content Caching at Self-Driving Car Using Federated Learning with Edge Cloud

Proactive content caching in self-driving cars poses several challenges, particularly because of the dynamic nature of content popularity, heterogeneity in user preferences, and privacy issues for data sharing. To tackle these issues, in this paper, we study the significance of proactive content caching strategy in self-driving cars for optimizing content retrieval cost and quality-of-experience (QoE) with the edge cloud infrastructure. To that end, we propose a low-complexity content popularity prediction mechanism in a federated setting where we extract local content popularity patterns in the self-driving cars using long short-term memory (LSTM)-based prediction mechanism. Then, we leverage the privacy-preserving distributed model training paradigm of Federated Learning (FL) to create a global model by applying the Federated Averaging (FedAvg) algorithm on local LSTM models to create a regional content popularity prediction model. With extensive simulations on real-world datasets, we show the obtained global model helps to improve the local cache hit ratio, cache space utilization, and correspondingly minimize latency overhead at the self-driving cars.
Community-Based Tourism Planning: an Application of the APPA Approach to Ansted, West Virginia

Rural communities throughout the world have been struggling with developing effective ways to improve their quality of life while without losing their rural atmosphere. Community-based tourism planning has been regarded as an effective means of achieving this dual goal. This article applied the Appreciative Participatory Planning and Action (APPA) approach to a rural community town of Ansted in West Virginia, USA, whereas the tourism assets, tourism development priorities and challenges, and tourism products/markets were identified for the town. In addition, residents' attitudes toward tourism development in the town were also examined. The results indicate that local residents' attitudes were highly positive and homogeneous. The majority did not believe that issues from increased tourism would be a problem for the community. Rather, they were more disposed toward tourism development for local economic development and benefits. The article concludes that tourism planning for the town cannot be conducted and implemented independent of regional planning and development that involve other factors or economic activities such as coal mining in this study. In addition, community-based tourism planning cannot be separated from community planning as a whole and should be integrated into other aspects of community development programs.
Deep Multi-Task Conditional and Sequential Learning for Anti-Jamming

Multi-task learning provides plenty of room for performance improvement to single-task learning, when learned tasks are related and learned with mutual information. In this work, we analyze the efficiency of using a single-task reinforcement learning algorithm to mitigate jamming attacks with frequency hopping strategy. Our findings show that single-task learning implementations do not always guarantee optimal cumulative reward when some jammer’s parameters are unknown, notably the jamming time-slot length in this case. Therefore, to maximize packet transmission in the presence of a jammer whose parameters are unknown, we propose deep multi-task conditional and sequential learning (DMCSL), a multi-task learning algorithm that builds a transition policy to optimize conditional and sequential tasks. For the anti-jamming system, the proposed model learns two tasks: sensing time and transmission channel selection. DMCSL is a composite of the state-of-the-art reinforcement learning algorithms, multi-armed bandit and an extended deep-Q-network. To improve the chance of convergence and optimal cumulative reward of the algorithm, we also propose a continuous action-space update algorithm for sensing time action-space. The simulation results show that DMCSL guarantees better performance than single-task learning by relying on a logarithmically increased action-space sample. Against a random dynamic jamming time-slot, DMCSL achieves about three times better cumulative reward, and against a periodic dynamic jamming time-slot, it improves by 10% the cumulative reward.
Comparative Analysis of Real-Time Global Illumination Techniques in Current Game Engines

The most important component of photorealism in Computer Graphics is given by a physically correct approximation of the light transport. Besides the direct illumination from light sources, there is an indirect illumination, produced by the reflections of the light rays on other surfaces of the scene. In Computer Graphics, the process of computing the illumination of a surface by considering both the direct and the indirect illumination is widely known as global illumination. This paper describes several classes of real-time global illumination techniques used in current game engines together with our own implementations of these approaches. All implementations were made in our own framework, specially designed with a multi-pass rendering architecture that allows fast implementation of rendering techniques and the reuse of functionalities. We analyze these classes based on the following criteria: the visual results produced by the indirect diffuse lighting, the ability to produce glossy reflections, shadows, ambient occlusion, subsurface scattering, translucency and volumetric lighting as well as the ability to simulate area lights. We present the quantitative results of our implementations, obtained with the same external parameters for all techniques, thanks to the unified implementations in the same framework. An important observation is that our analysis is focused on the techniques that are based on the rasterization pipeline, thus, the comparison does not include the techniques designed entirely for the ray-tracing pipeline.
Anomaly Detection Techniques in Smart City: A Review from a Framework Perspective

A smart city is a notion in which a city may effectively and efficiently manage its resources in order to improve the quality of life of its residents. With the existence of information technology as an enabler, the city is able to carry out sensing, understanding and acting at a certain level. Anomaly detection is a growing field of research in almost all domains, one of which is smart cities. In a smart city, anomaly detection enters into the context of understanding, where the data received, both cyber physical system sensors and user generated content are processed to obtain unusual data that can affect predictions or forecasts. Many studies have been carried out related to anomaly detection methods and techniques related to the smart city environment but have not been categorized according to the domain or framework that is in accordance with the smart city model. This study aims to present the results of a systematic literature review on anomaly detection techniques categorized according to the Garuda Smart City Model, a model designed for smart city development in Indonesia. Based on the results of a systematic literature review, many research gaps have been found for anomaly detection in a smart city environment related to performance limitations, the effect of implementation methods, and the use of anomaly detection as part of the prediction and forecasting methods.
Perceived Risk and Trust in Adoption E-Wallet: The Role of Perceived Usefulness and Ease of Use

The emergence of digital payment technologies such as electronic wallets has positively affected Indonesia's non-cash society. E-wallet providers are competing to collaborate with banks to facilitate their application. Since e-wallets are used for online transactions, the perceived risk of transaction failure, trustworthiness, ease of use, and application performance are essential variables when users adopt e-wallets. Therefore, this study aims to examine variables that affect user intentions in adopting e-wallets. A total of 400 respondents from DKI Jakarta and Yogyakarta are needed in this study. We confirm that they are already using an e-wallet and intend to accept it—the data interpretation technique used CB-SEM by Lisrel 8.7 Software. Our analysis results confirm that the sixteen hypotheses we tested included mediation, two hypotheses rejected, specifically perceived usefulness and e-wallet adoption, the remaining twelve hypotheses were accepted.
Research on Infrared Spectral Quantitative Analysis of Hydrocarbon Gases Based on Adaptive Boosting Classifier and PLS

The traditional gas logging using hydrogen flame gas chromatography is gradually difficult to meet the requirements of the current complex oil and gas reservoir exploration because of its long detection cycle, many additional equipment, complex use, maintenance process and other defects. Infrared spectroscopy has obvious advantages in detection speed and convenience. But when it comes to homologues with similar molecular structure, it is difficult to quantitatively analyze the component concentration because of the serious overlap of characteristic absorption peaks. To solve this problem, this paper uses the self-developed experimental platform to carry out a large number of infrared spectral experimental studies on the single substance and six component mixtures. Based on a large number of experimental data, a method combining the AdaBoost classifier with optimization window partial least squares (PLS) modeling is proposed. Through the PLS local modeling for the spectra of different components and different concentration ranges, the boosting algorithm is used to classify the original spectral data, and then the component concentration is calculated. This method solves the problems of difficult identification and inaccurate quantitative analysis of alkane mixed gas components in traditional methods, and effectively improves the detection accuracy of infrared spectrum quantitative analysis of mixed components, which provides the core theoretical support for infrared spectroscopy to replace hydrogen flame gas chromatography which is widely used in gas logging.
Anisotropic bulk SmCo7 nanocrystalline magnets with high energy product

Realizing grain alignment along easy magnetization axis in bulk SmCo7 nanocrystalline materials is crucial for their development as high-performance high-temperature magnets, yet it remains challenging. Here, we report the fabrication of anisotropic bulk SmCo7 nanocrystalline magnets with a small grain size of ∼20 nm and a (00l) texture using high-pressure thermal compression starting from partially amorphous precursors. The synthesized magnet exhibits a high energy product of 18.4 MGOe, 40% larger than the reported highest value (13 MGOe) for bulk nanostructured SmCo7 magnets, and outperforms its anisotropic coarse-grained counterpart. Moreover, our magnet shows a low coercivity temperature coefficient of β = −0.19%/°C. These findings make an important step toward the fabrication of oriented bulk nanostructures for practical applications.
Amphibious Robot with a Novel Composite Propulsion Mechanism

Amphibious robots can be used in both land and underwater environments, which has a wider range of applications than traditional mobile robots. However, the development of propulsion mechanisms for use in amphibious environments is very challenging. In this study, a new type of composite propulsion mechanism is presented, hinged by a plurality of deformable joints, and is suitable for amphibious environments. By changing the relative position of the rotation center of the slider chain and the rotation center of the outer track, the mechanism can be transformed between the land curved-legged state and the underwater webbed state. The mechanism has the advantages of high terrain adaptability and fast speed of curved legs when moving on land, and has good webbed flexibility when moving underwater. In this paper, aiming at the typical terrain of the amphibious environment, a new amphibious robot equipped with the propulsion mechanism is developed, and the kinematic performance of the amphibious robot is tested. The results show that the design method of composite propulsion mechanism suitable for the amphibious environment can improve amphibious robots’ kinematic performance and provide a valuable reference for the design and control of other amphibious robots.
Technology Developed in GICE

Optimization of matching networks is a necessary step to design a high performance microwave circuits. Unfortunately, the problem to synthesize an optimal matching network for a given circuit specification can be reduced to the NP-complete problems [1]. Therefore, enumeration is still the only way to ensure the global optimality so far. However, the design of microwave circuits usually has real-time constraints, and thus it is desired to have an algorithm to fast find a reasonable solution or possibly the global optimum instead of enumeration.
Membrane topology and structure-function relationship of the Apical Sodium-dependent Bile Acid Transporter, ASBT /

The apical sodium-dependent bile acid transporter (ASBT) is a member of the solute carrier transporter superfamily and an essential component of enterohepatic circulation. ASBT has been investigated as a vector to improve oral drug bioavailability and appreciated as a novel molecular target for cholesterol-lowering therapy. This dissertation is aimed to (I) to define the membrane topology of ASBT, and (II) to identify critical amino acid residues involved in sodium and bile acid interaction. Chapter 1 presents an overview of membrane transporters and current transporter research. This part includes a discussion on pharmaceutical relevance of transporters, transporter classification and examples to study transporter function. In the second part, a detailed literature review on ASBT is presented. In Chapter 2, experimental strategies to address Aim I are delineated. In short, /V-glycosylation sites are engineered into the strategic sites on the non­ glycosylated form of ASBT (N10D mutant) to differentiate between the two proposed topology models (7 versus 9 transmembrane domains). Results indicate that /V-glycosylation sites are successfully introduced into the strongly supporting a 7TM model for ASBT. In Chapter 3, approaches to address Aim II are described. Based on the defined membrane topology (7TM), alanine scanning mutagenesis is subsequently applied to 12 highly conserved charged residues in the extracellular domains of ASBT to generate a mutant library; expression and function ([^H]-taurocholic acid (TCA) uptake kinetics and sodium dependency) of wild-type and mutants are analyzed by western blot and uptake studies in the transiently transfected COS-1 cells; uptake inhibition studies with various naturally occurring bile acid analogues are performed to further structurally probe the transporter/substrate interaction. Results show that [̂ H] TCA uptake kinetics is significantly altered in a single functional mutant, D124A, which additionally has decreased affinity to other bile acid analogues containing a 7a hydroxyl group. Sodium activation kinetics indicates a decreased apparent sodium affinity for mutant R254A. In conclusion, kinetic studies of ASBT mutants reveal a probable region for bile acid recognition, Asp124 (on extracellular loop 1), and a candidate region for sodium interaction/translocation, Arg254 (on extracellular loop 3). In Chapter 4, future directions to extend this dissertation work are presented.