Reviewerly/exHarmony huggingface dataset:
AutoAtlas: Neural Network for 3D Unsupervised Partitioning and Representation Learning

We present a novel neural network architecture called AutoAtlas for fully unsupervised partitioning and representation learning of 3D brain Magnetic Resonance Imaging (MRI) volumes. AutoAtlas consists of two neural network components: one neural network to perform multi-label partitioning based on local texture in the volume, and a second neural network to compress the information contained within each partition. We train both of these components simultaneously by optimizing a loss function that is designed to promote accurate reconstruction of each partition, while encouraging spatially smooth and contiguous partitioning, and discouraging relatively small partitions. We show that the partitions adapt to the subject specific structural variations of brain tissue while consistently appearing at similar spatial locations across subjects. AutoAtlas also produces very low dimensional features that represent local texture of each partition. We demonstrate prediction of metadata associated with each subject using the derived feature representations and compare the results to prediction using features derived from FreeSurfer anatomical parcellation. Since our features are intrinsically linked to distinct partitions, we can then map values of interest, such as partition-specific feature importance scores onto the brain for visualization.
Mapping of environmental supporting capacity based on ecosystem services in Wajo Regency 2021

Population growth causes an increase in economic activity that occurs all the time. This has an impact on increasing the need for space for settlements and other needs to support human life. The carrying capacity of the environment is very important. A decrease in the carrying capacity for food production can lead to humanitarian and ecological disasters, such as food and water insecurity. Large-scale deforestation for agriculture in the tropics is an example of land cover change negative impact on the natural ability to support human needs. Uncontrolled land use dynamics will result in biophysical changes in the landscape that tend to be destructive. This dynamic also has an impact on exceeding the carrying capacity of the environment. Wajo Regency has the potential of natural resources, especially in the agricultural sector. Most of Wajo Regency is fertile and has potential for agricultural development. This is a special concern for local governments in improving the farmers' economy and supporting national food security. The rapid development also accelerates the conversion of land functions, and puts pressure on the sustainability of the food supply ecosystem. The results of the interpretation of Sentinel 2 Imagery (2020), Wajo Regency is dominated by rice fields (114,296 Ha). The landscape map of Wajo Regency is dominated by undulating, wavy folds structural plains, made of non-carbonate sedimentary rock. From data analysis using the Analytical Hierarchy Process (AHP) method for food supply, water supply, protection against natural disasters, as well as flow management and flood protection, five classification classes were obtained: very high, high, medium, low and very low.
Enhancing Automated FaaS with Cost-aware Provisioning of Cloud Resources

Compute resources are becoming more diverse, more specialized, and more physically distributed every day. To properly use these resources, the new paradigm of serverless computing aims to abstract away the complexity associated with resource configuration and workload deployment. Building on this serverless architecture are efforts for automated resource selection and automated task distribution and coordination. As computing resources advance, so too does application-specific optimizations, as is the case of deep learning on GPUs or even the more specialized TPUs. To better navigate the efficient and effective use of these diverse resources, we present the addition of automated, cost-aware provisioning of cloud resources to a state-of-the-art automated serverless framework. By automating the selection, provisioning, and configuration of cloud resources, our framework, which we call DELTA+, will enable truly cost-aware usage of the cloud by navigating complex computational tradeoffs. In this proposal, we will outline how we plan to introduce burstable cloud computing to automated serverless infrastructure and to present our initial findings with respect to system design and performance.
P‐3.6: A training system for the rescue process of acute and severe diseases based on AR

China is one of the countries with the most serious natural disasters in the world. Hidden dangers and risks of all kinds of accidents are intertwined and superimposed, and the factors affecting public safety are increasing. It is urgent to strengthen the construction of emergency medical rescue capacity. During the nineteenth collective learning of the Central Political Bureau, general secretary Xi Jinping stressed that we should strengthen the capacity building of aviation emergency rescue and improve the airspace guarantee mechanism for emergency rescue. As the most important aerial medical evacuation platform, helicopter has the advantages of fast response, strong mobility, wide rescue range, direct arrival at the first line of treatment, etc. it has become an important means of modern rescue, and also the only way to open life treatment in case of land damage. From the international experience, the helicopter emergency medical system established by most developed countries plays an important role in disaster relief and anti‐terrorism. China has a large population, complex terrain and uneven regional development. In case of disasters, the casualties are often more severe and the demand for treatment is more urgent. It is urgent to establish an aviation medical emergency rescue system suitable for the national conditions and with helicopters as the main carrier, and accelerate the research and development of key technical equipment. In recent years, more and more people realize the necessity and benefits of the application of interactive technology for aviation medical simulation training in major events and disasters. Interaction refers to the transmission and exchange of intelligence, data, data and technical knowledge in all aspects of nature and society. From the perspective of information theory, local chronicles are a tangible text information carrier, which collects all kinds of information in a certain region. Interactive technology is to use certain means to achieve the purpose of interaction, and gradually enter the era of multi field application! In thefield of aviation medical rescue, it is in urgent need of penetration and integration. This paper mainly develops an AR emergency rescue process training system. Through the creation of data sets, action recognition based on computer vision and deep learning, the selection of recognition model and action capture, the action is recognized based on AR, and then 22 joint points of human hand and upper arm skeleton are taken to label the data space position. Through the above learning results, we can get the real‐time hand recognition based on depth information Upper arm skeleton output. Neural network is used to recognize human skeleton action. The skeleton information coordinates are imported into RNN neural network for deep learning. Finally, based on the complementarity of CNN and LSTM, combined with the idea of dual stream structure network, the action recognition is carried out.
Networks for Compositional Data

Networks are increasingly used to capture the multitude of interaction mechanisms among microbes. Due to the dynamic nature of microbial communities and their interactions, and given the paucity of well-curated databases for microbial interactions, these networks are often inferred from microbiome abundance data. While a number of procedures have been recently proposed for this task, their properties have not been fully investigated. Two desirable properties of network estimation procedures are (1) their robustness to the spurious correlations resulting from the compositional nature of the microbiome data and (2) their ability to uncover the true network structure from limited observations. This chapter reviews commonly used procedures for inferring microbial interaction networks. It also presents a comprehensive empirical evaluation of these procedures.
A Valence Catalogue for Norwegian

Essential aspects of a verb’s usage reside in its valence environments. The Norwegian valence resource here presented, called NorVal, has 6,300 verb lemmas. About 3,360 of them are associated with sets of frames, and the organization of entries is divided into one enumeration of the total number of frame-specific entries, which is about 15,750, and one enumeration of lemmas, counting 6,300. About 300 frame types are distinguished inducing the 15,750 frame specific entries, taking into account most grammatical factors distinguishing verb frames and verb-headed construction types. Both the frame types and the two dimensions of entries are represented in string-based formalisms, enabling simple procedures for comparing individual valence frames, frame-specific entries, and entries representing lemmas, and for doing statistics over types and combinations of all of these. The paper illustrates the resources relative to their representation of light reflexives, verb particles, and frames including sentential constituents.
Electromagnetic Spectral Bands Used for Remote Sensing

Electromagnetic bands in remote sensing cover the spectrum from visible blue across the optical and infrared bands to microwave radar. With applications from different geological settings and climatic zones we will determine which spectral bands or band combinations are useful for the mapping task at hand. Optical bands are useful for photographic assessments. The coastal blue band is used for shallow offshore mapping. Very near infrared is sensitive for vegetation, whereas shortwave infrared has its main application for lithology and landuse mapping. Radar images are used for two different tasks: first for surface textural characterization using radar surface backscatter. And second, when microwave radar can penetrate the ground, then volume backscatter and absorption is used to map shallow subsurface features.
Fusion of 2.5D Face Recognition through Extreme Learning Machine via Manifold Flattening

A flexible feature descriptor, Gabor-based Region Covariance Matrix (GRCM), embeds the Gabor features into the covariance matrix has emerged in face recognition. GRCM locates on Tensor manifold, a non-Euclidean space, utilises distance measures such as Affine-invariant Riemannian Metric (AIRM) and Log-Euclidean Riemannian Metric (LERM) to calculate the distance between two covariance matrices. However, these distance measures are computationally expensive. Therefore, a machine learning approach via manifold flattening is proposed to alleviate the problem. Besides, several feature fusions that integrate the 2.5D partial data and 2D texture image are investigated to boost the recognition rate. Experimental results have exhibited the effectiveness of the proposed method in improving the recognition rate for 2.5D face recognition.
Joint Attention Mechanism for Unsupervised Video Object Segmentation

In this work, we propose an unsupervised video object segmentation framework based on a joint attention mechanism. Based on the feature extraction of video frames, this method constructs a joint attention module to mine the correlation information between different frames of the same video, and uses the global consistency information of the video to guide the segmentation. The joint attention module includes a soft attention unit and a co-attention unit. The former emphasizes important information in the feature embedding of a frame, and the latter enhances the features of the current frame by calculating the correlation between features from different frames. Furthermore, in order to exchange information more comprehensively and deeply in different frames, superimposing the joint attention module can achieve better performance. We conducted experiments on several benchmark datasets to verify the effectiveness of our algorithm, experimental results show that the joint attention module can capture global consistency information significantly and improves the accuracy of segmentation.
Discussion on the Application of Highway Maintenance Platform Based on GIS + BIM Technology—Taking Gansu Province as an Example

Gansu Province is a large province in Western China, and its geographical location and economic status are very important.With the state's attention to the western development, the economy of the western region represented by Gansu Province has developed rapidly, and the construction of roads and other infrastructure has also been developed.Taking Gansu Province as an example, this paper studies and discusses the application of highway maintenance platform based on GIS + BIM Technology.Firstly, this paper expounds the advantages of GIS + BIM Technology in highway construction and maintenance, and analyzes the development status of GIS + BIM Technology; Secondly, it expounds how to solve the problem of highway maintenance from the aspects of GIS + BIM system and its advantages and difficulties in highway maintenance, and briefly explains the difficulties existing in highway maintenance in Gansu Province.Then, starting from the path of GIS + BIM Technology to solve highway maintenance in Gansu Province, this paper analyzes the expected effect of GIS + BIM Technology in highway maintenance in Gansu Province.
Appearance-Motion Fusion Network for Video Anomaly Detection

Detection of abnormal events in surveillance video is an important and challenging task, which has received much research interest over the past few years. However, existing methods often only considered appearance information or simply integrated appearance and motion information without considering their underlying relationship. In this paper, we propose an unsupervised anomaly detection approach based on deep auto-encoder, which can effectively exploit the complementarity of both appearance and motion information. Two encoders are used to extract appearance features and motion features from RGB and RGB difference frames, respectively, and then a feature fusion module is employed to fuse appearance and motion features to produce discriminative feature representations of regular events. Finally, the fused features are sent to their corresponding decoders to predict future RGB and RGB differential frames for determining anomaly events according to reconstruction errors. Experiments and ablation studies on some public datasets demonstrate the effectiveness of our approach.
Fractal Coordinates for Incremental Procedural Content Generation

Incremental procedural content generation (IPCG) has been used in games such as Minecraft to provide a unique flavor of gameplay, but requires that parts of the world can be generated independently of one another in any order and will always fit together in the end. Noise functions such as simplex noise are a very popular building block for IPCG systems, because they support this property, but noise functions alone have their limitations, one of which being an inability to create non-local structures or continuity. To combat this, noise functions are usually applied at several scales to provide fractal continuity and recognizable features similar to real world geography’s fractal complexity. This paper describes a system of fractal coordinates suitable for use with IPCG that generalize this idea of multiple layers of structure at different scales, and demonstrates how it can be used to achieve some interesting effects.
Significance of Introducing Internet Financial Supply Chain into Cities Under the Background of Big Data

In recent years, supply chain finance has become a new kind of finance service and became a new growth points of commercial Banks. As a key development city in Guangdong Province, Zhanjiang integrates sea, land and air traffic. Drived by the national policy of “One Belt And One Road”, Zhanjiang as a port city has achieved a remarkable achievement with the throughput of goods exceeding one million. Under the background of Big data and Internet finance, how to further promote the transformation and upgrading of industrial structure in Zhanjiang and build Haikou City with a good financing environment for enterprises has become an unavoidable topic and research in the future. Based on the existing development of Zhanjiang, this paper conducts a feasibility study on the introduction of Internet finance supply chain in Zhanjiang, solves the financing problems of basic links and various industries in Zhanjiang, arranges a new financing strategic environment for the economy of Zhanjiang region, promotes industrial transformation and upgrading in Zhanjiang region, and brings significant economic benefits to Zhanjiang.
Geologic Feature Extraction from Remote Sensing Data

In this section we will use remote sensing data to extract structural, depositional and erosional features from digital relief models and satellite imagery, jointly interpret them using the concept of geomorphology with goal of building geologic models to explain the geological setting of the study area. This process benefits from including existing information by other experts familiar with the target area and with the processes that shaped the geologic setting. Therefore, we have provided references related to the geologic processes on a global and regional scale, historic studies that provided insights into the geological setting and maps that can be used to correlate and calibrate the interpretation obtained from the remote sensing data.
COMBINE: A Pipeline for SQL Generation from Natural Language

Accessing data stored in relational databases requires an understanding of the database schema and mainly a query language such as SQL, which, while powerful, is difficult to master. In this sense, recent researches try to approach systems to facilitate this task, in particular by making Text-to-SQL models that attempt to map a question in Natural Language (NL) to the corresponding SQL query. In this paper, we present COMBINE, a pipeline for SQL generation from NL, in which we combine two existing models, RATSQL (We used the version RATSQL v3+BERT; paper’s url: arxiv.org/abs/1911.04942.) and BRIDGE (We used the version BRIDGE v1+BERT; paper’s url: aclweb.org/anthology/2020.findings-emnlp.438/.), that are based on recent advances in Deep Learning (DL) for Natural Language Processing (NLP). Our model is evaluated on the Spider challenge, using Exact Matching Accuracy (EMA) and Execution Accuracy (EA) metrics. Our experimental evaluation demonstrates that COMBINE outperforms the two used models in the same challenge, and at the time of writing, achieving the state of the art in EA with 70%, and competitive result in EMA with 71.4%, on Spider Dev Set.
Intelligent Attack Behavior Portrait for Path Planning of Unmanned Vehicles

With the rapid development of artificial intelligence, opponents can use AI technology to influence the path planning algorithm of unmanned vehicles, making unmanned vehicles face severe safety issues. Aiming at the opponent’s intelligent attack in the scenario of unmanned vehicle path planning, this paper studies the opponent’s intelligent attack behavior portrait technique and proposes an attack behavior portrait scheme based on the knowledge graph. First, according to the simulation experiment of unmanned vehicle path planning based on reinforcement learning, we use Toeplitz Inverse Covariance-based Clustering (TICC) time-series segmentation clustering technology to extract the steps of an opponent’s attack behavior. Then, the attack strategy rules are stored in the knowledge graph to form a portrait of attack behavior for unmanned vehicle path planning. We verified the proposed scheme on the Neo4j platform. The results proved that the method could describe the steps of intelligent attacks on unmanned vehicles well and provide a basis for unmanned vehicle attack detection and establishing an unmanned vehicle defense system. Furthermore, it has good generalizability.
Facial Emotion Generation using StarGAN with Differentiable Augmentation

Emotion generation has remained a challenging task due to the high similarity between each emotion class. In addition, the model had to learn images with various lighting conditions and diverse facial structures. To address this limitation, we propose a modification of StarGAN by applying differentiable augmentation for generating realistic facial emotions. Furthermore, our approach allows both the generator and discriminator to generalize the data better. Finally, we evaluate the performance of the model through an emotion classifier and conduct a quantitative analysis by calculating the accuracy of the generated emotion.
Complex Multivalued Hierarchical Logic (HS-Logic)

A complex multivalued propositional hierarchical logic is proposed, abbreviated as complex HS-logic. It combines two logics—real Lr with two states and an introduced new imaginary logic Li with two other states {i,¬i} that are not equivalent to {T, F}. Through two additional requirements a complex logic is constructed with four unequal states {T, F, i, ¬i}. It has been proved that within the framework of the complex logic thus defined, the true and false states i and ¬i of the imaginary logic have a greater force than those of the real logic ¬T and F. This means that imaginary logic dominates over Li-logic. This allows all four states of HS-logic to be sorted by degree of falsity and degree of truth. Rules have been derived that show the dependencies between the four states of complex logic in disjunction, equivalence and implication, conjunction. Some of these dependencies are shown graphically. Based on the obtained rules, four square Kelly tables were compiled—for disjunction, conjunction, implication and equivalence, respectively. Each of the tables is also four-element—for the four states of complex logic. It is a quaternion for each of the logical operations. Based on Modus Ponens and data from the Kelly table for implication, four inference rules were obtained. It is shown that the proposed complex hierarchical HS-logic meets all the requirements of Boolean algebra and lattice and this allows it to be used to solve logical equations in cases where this cannot be done within the classical propositional logic. Another example of possible applications of the proposed HS-logic is given.
Cyberdream: An Interactive Rave Music Visualization in Virtual Reality

Virtual reality (VR) provides new opportunities for the design of interactive music visualizations. Exploring this area, Cyberdream is a prototype VR application realized through the author's practice-led research, which provides a journey through audio-visual environments based on the aesthetics of 1990s rave music. The project provides three audio-visual 'sound toys', which allow the user to interactively 'paint with sound', thereby facilitating creative play. Through its structural form and audio-visual sound toys, Cyberdream indicates new approaches for the design of music visualizations that harness the spatial properties of VR.