Reviewerly/exHarmony huggingface dataset:
Assessing the value and usage of data management planning and data management plans within the U.S. Geological Survey

First posted September 27, 2023 For additional information, contact: Director, Science Analytics and Synthesis ProgramU.S. Geological SurveyP.O. Box 25046, Mail Stop 302Denver, CO 80225 As of 2016, the U.S. Geological Survey (USGS) Fundamental Science Practices require data management plans (DMPs) for all USGS and USGS-funded research. The USGS Science Data Management Branch of the Science Analytics and Synthesis Program has been working to help the USGS (Bureau) meet this requirement. However, USGS researchers still encounter common data management-related challenges that may be reduced or eliminated by better planning. In 2021, USGS staff were given a series of surveys aimed to better understand current data management planning practices, perceptions, and needs. The survey results indicated that adoption and integration of data management planning and DMPs into USGS research project workflows are broad, if inconsistent, across USGS Science Centers and programs. The USGS Science Data Management Branch can help improve clarity and guidance on the purpose, intended audience, content, workflows, and evaluation processes for DMPs. It would also be beneficial to provide additional supporting cyberinfrastructure to support DMP activities. Survey responses indicated it would be beneficial for the Science Data Management Branch to develop a strategy, other than through DMPs, for teaching and encouraging good data management practices. Although these surveys were an opportunity for USGS staff to provide feedback on their experiences, the surveys may also have revealed the desire for more frequent evaluations, cross-disciplinary communication, and training on research data management and DMP development and integration, in the context of USGS policy, Fundamental Science Practices requirements, and overall Bureau expectations. Data management-related roles such as data manager or steward, information technologist, and repository manager may need to be formally recognized as skilled professional career positions within the Bureau. At a minimum, the best practice for USGS would be to create and maintain DMPs as living documents, integrated with existing systems that are broadly accessible to all stakeholders, and include quantitatively measurable benefits tied directly to a clearly defined purpose.
A Novel Framework for Scene Graph Generation via Prior Knowledge

The scene graph generation aims to recognize objects and infer the relationships between them, which can provide a comprehensive understanding of image visual perception. However, the long-tailed issue of relations remains challenging for scene graph generation. This paper proposes a novel framework based on knowledge-driven data-driven joining to address the long-tail issues in scene graph generation. The proposed framework consists of two modules: the relation inference module and the prior knowledge learning module. The relation inference module aims to learn the relational features of entity pairs in images and the structural features of scene graphs. The prior knowledge learning module aims to learn the triplet representation from the knowledge graph and use it as prior knowledge to provide logical guidance and constraints for relation inference. This provides prior bias for relation inference to transfer the bias towards head categories to reasonable categories, thereby mitigating the long-tail problem. Experiment results indicate that the proposed framework outperforms on Visual Genome datasets and that the generated scene graph relation is logically reasonable.
Graph-Based Model Compression for HSR Bogies Fault Diagnosis at IoT Edge via Adversarial Knowledge Distillation

Deep graph neural networks (GNNs) have demonstrated their exceptional expressive capability in detecting fault features from multisensor signals for fault diagnosis. However, the excessive depth of these models often hinders their deployment on Internet of Things (IoT) systems. In order to facilitate the implementation of graph-based models on IoT devices for fault diagnosis of HSR bogie, this paper proposes a novel compression technique called GraKD. GraKD distills the latent knowledge from the teacher model to a lightweight student model in an adversarial manner. The discriminator of GraKD comprises a representation identifier and a logit identifier. The former effectively distinguishes between the node representations of the student and the teacher by discerning the local affinity of connected node patch-patch pairs and the global affinity represented by the patch-global pairs. The latter employs a residual multi-layer perceptron block to differentiate between the logits of the student and the teacher. The effectiveness of GraKD is validated using data collected from test rigs of bogies. A series of experiments substantiate the broad applicability of GraKD in compressing various GNN-based models.
UnbiasNet: Vehicle Re-Identification Oriented Unbiased Feature Enhancement by Using Causal Effect

Vehicle re-identification is a crucial task that matches images of the same vehicle across different camera viewpoints. Many previous attention-based studies have approached this problem by exploring the regions of interest in vehicles. However, the generated attention in these models is susceptible to noisy data, as they are unable to provide powerful supervision to distinguish biased and unbiased clues during the attention learning process. To address the problems mentioned above, we aim to design a robust vehicle re-ID network that utilizes the causal effect to effectively transfer attention from biased to unbiased clues. In this paper, we propose an unbiased feature-enhanced network (UnbiasNet), which consists of an unbiased feature-aware block (UFAB) and a novel causal effect-based joint constraint (CEC). In particular, we propose an unbiased feature-aware block as an attention module to extract rich and discriminative information. We conduct a counterfactual intervention on our attention module to generate biased feature representations. Moreover, we propose a novel causal effect-based joint constraint that consists of original prediction constraint and total indirect effect constraint. The original prediction constraint ensures that unbiased feature-aware block converges correctly. The total indirect effect constraint utilizes the generated biased features as supervisory information to motivate unbiased feature-aware block to explore a greater number of unbiased features during the training process. Our approach had an inference time of 1.39 ms per image, which introduces only a few parameters during the training phase and none during the testing phase. We carry out comprehensive experiments to illustrate the effectiveness of the UnbiasNet on three challenging datasets.
Redundancy-Free Self-Supervised Relational Learning for Graph Clustering

Graph clustering, which learns the node representations for effective cluster assignments, is a fundamental yet challenging task in data analysis and has received considerable attention accompanied by graph neural networks (GNNs) in recent years. However, most existing methods overlook the inherent relational information among the nonindependent and nonidentically distributed nodes in a graph. Due to the lack of exploration of relational attributes, the semantic information of the graph-structured data fails to be fully exploited which leads to poor clustering performance. In this article, we propose a novel self-supervised deep graph clustering method named relational redundancy-free graph clustering (R <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$^2$</tex-math> </inline-formula> FGC) to tackle the problem. It extracts the attribute-and structure-level relational information from both global and local views based on an autoencoder (AE) and a graph AE (GAE). To obtain effective representations of the semantic information, we preserve the consistent relationship among augmented nodes, whereas the redundant relationship is further reduced for learning discriminative embeddings. In addition, a simple yet valid strategy is used to alleviate the oversmoothing issue. Extensive experiments are performed on widely used benchmark datasets to validate the superiority of our R <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$^2$</tex-math> </inline-formula> FGC over state-of-the-art baselines. Our codes are available at https://github.com/yisiyu95/R2FGC.
Light Field Neural Rendering

Classical light field rendering for novel view synthesis can accurately reproduce view-dependent effects such as reflection, refraction, and translucency, but requires a dense view sampling of the scene. Methods based on geometric reconstruction need only sparse views, but cannot accurately model non-Lambertian effects. We introduce a model that combines the strengths and mitigates the limitations of these two directions. By operating on a four-dimensional representation of the light field, our model learns to represent view-dependent effects accurately. By enforcing geometric constraints during training and inference, the scene geometry is implicitly learned from a sparse set of views. Concretely, we introduce a two-stage transformer-based model that first aggregates features along epipolar lines, then aggregates features along reference views to produce the color of a target ray. Additionally, we propose modifications that allow the model to generalize to scenes without any fine-tuning. Our model outperforms the state-of-the-art on multiple forward-facing and 360 ° datasets, with larger margins on scenes with severe view-dependent variations. Code and results can be found at https://light-field-neural-rendering.github.io/.
DTASUnet: A local and global dual transformer with the attention supervision U-network for brain tumor segmentation

Glioma refers to a highly prevalent type of brain tumor that is strongly associated with a high mortality rate. During the treatment process of the disease, it is particularly important to accurately perform segmentation of the glioma from Magnetic Resonance Imaging (MRI). However, existing methods used for glioma segmentation usually rely solely on either local or global features and perform poorly in terms of capturing and exploiting critical information from tumor volume features. Herein, we propose a local and global dual transformer with an attentional supervision U-shape network called DTASUnet, which is purposed for glioma segmentation. First, we built a pyramid hierarchical encoder based on 3D shift local and global transformers to effectively extract the features and relationships of different tumor regions. We also designed a 3D channel and spatial attention supervision module to guide the network, allowing it to capture key information in volumetric features more accurately during the training process. The experimental results show that DTASUnet exhibited superior or competitive performance compared to other state-of-the-art algorithms. In the BraTS 2018 validation set, the average Dice scores of DTASUnet for the tumor core (TC), whole tumor (WT), and enhancing tumor (ET) regions were 0.845, 0.905, and 0.808, respectively. For the BraTS 2020 validation set, the average Dice scores of TC, WT, and ET were 0.844, 0.906, and 0.790, respectively. These results demonstrate that DTASUnet has utility in assisting clinicians with determining the location of gliomas to facilitate more efficient and accurate brain surgery and diagnosis.
A Content Based Image Retrieval Mechanism Based on Primitive Features

Content-based image retrieval (CBIR) is one of the most practiced areas in recent times. In a CBIR system, each image is represented using its primitive features like color, texture, and shape. These primitive features are extracted in various ways to form the final feature vector and used for image retrieval based on a selected similarity measurement. The size of a feature vector is also one of the deciding factors in computation time. Hence, smaller-sized feature vectors with comparable results are always advised. In this paper, to make the system closer to human perception, we have used the HSV counterpart. As all the pixel values do not contain vital information and unnecessarily increase computational overhead. Hence, in this work, we have used mid-rise quantization. In this work, we have used local statistical parameters to evaluate the color information. For texture extraction, GLCM is employed, and the shape feature is extracted using adaptive tetrolet transformation. We have validated our system using various widely accepted benchmark databases. The retrieved results are capable enough to demonstrate its improvement over other works.
Recognizing traffic signs with synthetic data and deep learning

Recently, in-depth learning about computer vision and object classification tasks has surpassed other machine learning (ML) algorithms. This algorithm, alike similar ML algorithms, requires a dataset for training. In most real cases, developing an appropriate dataset is expensive and time-consuming. Also, in some situations, providing the dataset is unsafe or even impossible. In this paper, we proposed a novel framework for traffic sign recognition using synthetic data and deep learning. The main feature of the proposed method is its independence from the real-life dataset, which leads to high accuracy in the real test dataset. Creating one-by-one synthetic data is more labor-intensive and costlier than providing real data. To tackle the issue, the proposed framework uses a procedural method, which gives the possibility to develop countless high-quality data that are close enough to the real data. Due to its procedural nature, this framework can be easily edited and tuned.
The Role of Background Blur and Contrast in Perceived Translucency of See-through Filters

Translucency is an appearance attribute, which primarily results from subsurface scattering of light. The visual perception of translucency has gained attention in the past two decades. However, the studies mostly address thick and complex 3D objects that completely occlude the background. On the other hand, the perception of transparency of flat and thin see-through filters has been studied more extensively. Despite this, perception of translucency in see-through filters that do not completely occlude the background remains understudied. In this work, we manipulated the sharpness and contrast of black-and-white checkerboard patterns to simulate the impression of see-through filters. Afterward, we conducted paired-comparison psychophysical experiments to measure how the amount of background blur and contrast relates to perceived translucency. We found that while both blur and contrast affect translucency, the relationship is neither monotonic, nor straightforward.
Towards High-level, Intuitive Descriptors of Material Appearance

Material appearance perception depends both on the physical interaction between light and material, and on how our visual system processes the information reaching our eyes. Currently, there is a disconnect between the physical properties underlying material appearance models used in simulations, and perceptual properties that humans rely on when interpreting visual depictions of material appearance. Our goal is to bridge this gap, creating high-level, intuitive descriptors of visual appearance that are linked to the underlying physical properties of the material. This can in turn benefit final applications such as material appearance editing, acquisition, gamut mapping, or compression. Here, we review two approaches proposing representations of appearance that are better aligned with human perception: one based on the use of intuitive attributes, and another exploring the use of natural language descriptions of material appearance. All our data and models are publicly available.
Hierarchical neural network detection model based on deep context and attention mechanism

In order to improve the ability of sentence event detection in natural language processing and solve the problem of event processing caused by polysemy, an event detection model based on neural network is proposed. The model adjusts the structure to a hierarchical neural network model based on neural network, and introduces attention calculation into the internal structure to realise the correlation analysis of sentence context. The value of the model is judged through performance analysis and application test. The results show that the average harmonic value of the model in polysemy detection is 74.1%, which is higher than the existing model. The application test shows that the model can detect events for sentences in different environments. The results show that the hierarchical neural network event detection model with deep contextual representation and attention mechanism has good performance, which provides theoretical support for the development of multi event detection technology.
Sentiment analysis of Amazon product reviews

The rapid development of online shopping sites has pushed people's shopping to a new way. Online shopping not only provides convenience to people but also "suggestions." Moreover, there are always many reviews from previous consumers on shopping websites, helping people know more about the product and make decisions. This paper represents the sentiment analysis of Amazon reviews using three models: Random Forest, Naive Bayes, and SVM. These models are trained with token counts, and term frequency-inverse document frequency (TF-IDF) features to make better comparisons. Classification performances are evaluated by precision, recall, and F-1 scores, and exploration is implemented into the dataset providing information about Amazon reviews. The results show that Random Forest and SVM models perform well on positive-labeled data but provide suboptimal results on negative-labeled and neutral-labeled data. Overall, Naive Bayes has the best performance for all three classifications. However, classifications might be biased during the analysis. Thus, more improvements are expected in future research about this topic to obtain more accurate and ideal results, and more machine learning models are supposed to be implemented.
Artificial Intelligence and Blockchain: A Breakthrough Collaboration in IP Law

Artificial Intelligence and blockchain technology now influence the intellectual property environment. For example, machine learning is now regularly used in trademark and patent searches in IP databases. Machine learning implementations in different IP areas, including patent tracking, copyright evaluation, and trademark comparisons, can now be seen more often. A vast range of LP notary implementations are proposed using blockchain technologies, and new networks emerge, using both AIs and blockchains to build new IP communities. The growth of such technology in LPP is mainly driven by the IT industries, particularly technology entrepreneurs. In the context of the non-registered IP rights such as copyright (which under the Berne Convention for the Protection of Literary and Artistic Works and in many jurisdictions is not registrable) and unregistered design rights, blockchain technology can play an important role, too, because it can prove its conception, use and qualification (for example the countries where the design was first marketed and originality). Uploading the author or creator’s work and design data into a blockchain would produce a time-stamped record and solid proof of originality.So, IPO has laid the groundwork for the technology’s fusion into the IP ecosystem. Several other possible uses include ledger management, the creation of a monitoring authority to track the use of IP properties in the market and commercialization for investors via a bidding system. It will provide innovators with a centralized, government-backed market to attract investors and technology titans.
Emerging Technologies and Intellectual Property Rights: A Cross-Jurisdictional Examination of AI and Patent Laws in India and the USA

The rapid development of Artificial Intelligence (AI) is swiftly reaching a critical juncture, which will greatly affect a multitude of sectors such as business, healthcare, and agriculture. While the enhanced capabilities of AI offer considerable potential for beneficial outcomes, they also present a substantial obstacle to current legal structures regulating patents. This conundrum carries extensive consequences for creativity, economic growth, and the broader community.[i] In light of this, it is critical that stakeholders, including patent experts, policymakers, and academics, engage in robust dialogues to develop strategies for encouraging innovation and navigating the complex legal terrain that arises from patent law in the AI paradigm.[ii] The stakes are high, and the consequences of failing to address these challenges could be dire. In light of the current context, this paper examines various facets of patent law as they relate to AI creations, encompassing patent eligibility principles, the patentability of AI in the United States and India, and the more extensive consequences of AI and patent law's convergence. The paper offers a comparative analysis of the legal structures in these two nations, illuminating the similarities and distinctions in how AI is addressed in each legal system. In conclusion, the paper proposes a set of suggestions for modifying legal frameworks to keep up with the rapid advancements in AI technology. By concentrating on the difficulties that emerge from the interplay of AI and patent law, the paper underscores the necessity for continuous discussion and cooperation among all parties involved, ensuring that creativity flourishes in this rapidly changing and exhilarating domain. &nbsp