Reviewerly/exHarmony huggingface dataset:
Adaptive Kernel Kalman Filter Multi-Sensor Fusion

The adaptive kernel Kalman filter (AKKF) is an effective Bayesian inference method for non-linear system estimation/tracking. With the AKKF, the posterior distributions of hidden states are embedded into a kernel feature space and approximated by the feature mappings of particles with associated kernel weights. The kernel weighted mean vector and associated covariance matrix are predicted and updated according to the kernel Kalman rule (KKR). In this paper, the AKKF is extended for the use in multi-sensor bearing–only tracking (BOT) systems. First, the centralized fusion based AKKF is formulated as a baseline for the AKKF application in multi-sensor BOT systems. Then, considering the computational capacities, transmitted power and forward link bandwidth constraints, the semi-decentralized fusion based AKKF is proposed. In this extended AKKF scheme, the prediction and update steps are executed at the fusion center (FC) and sensors separately. The prior and posterior kernel weight vectors and matrices are exchanged between the FC and sensors. Simulation results are presented to assess the performance of the proposed extended AKKFs compared with fusion based particle filter (PF) and Gaussian particle filter (GPF) for a multi-sensor BOT problem.
The Generalized Covering Radii of Linear Codes

Motivated by an application to database linear querying, such as private information-retrieval protocols, we suggest a fundamental property of linear codes– the generalized covering radius. The generalized covering-radius hierarchy of a linear code characterizes the trade-off between storage amount, latency, and access complexity, in such database systems. Several equivalent definitions are provided, showing this as a combinatorial, geometric, and algebraic notion. We derive bounds on the code parameters in relation with the generalized covering radii, study the effect of simple code operations, and describe a connection with generalized Hamming weights.
The Partnerships for Data Innovations (PDI): Facilitating data stewardship and catalyzing research engagement in the digital age

Abstract For agricultural research to seize the potential of data‐driven technologies, profound changes are needed in the full spectrum of data management, highlighted by a shift of researchers, institutions, and agencies toward a culture of data stewardship. The USDA‐ARS established the Partnerships for Data Innovations (PDI) in 2019 to achieve this transformation from within. From the onset, PDI has integrated its customer–partners in developing and implementing state‐of‐the‐art digital tools, leveraging off‐the‐shelf technologies when advantageous. In its short life, PDI has transformed more than 100 projects, from projects catalyzing collaborative efforts and underpinning information management to those salvaging end‐of‐life data repositories. Empowered by this active inclusive partnership and through sharing digital solutions on a “Digital Research Workbench” within the ARS Agricultural Collaborative Research Outcomes System infrastructure, PDI hopes to accelerate agricultural research through standardization, automation, and integration and to help usher in a culture of digital age data stewardship.
An improved supervised term weighting scheme for text representation and classification

Term weighting scheme has significant effects on the text classification performance. The main reason is that in text classification tasks, term weighting scheme determines the way in which texts are represented in the vector space model. Currently, term frequency-inverse document frequency is the most widely utilized term weighting scheme but it does not use the available category information of the training texts. Taking this resource of category information (or category factor) into account in the study, an improved supervised term weighting method for representing text is developed, which combines a new measure of information namely cumulative residual entropy and the proportional distortion function. To verify the text classification performance of our proposed scheme, we conducted an extensive experimental comparison of proposed scheme with existing schemes on two corpora (i.e., Reuters-21578 and 20 Newsgroups datasets) with different characteristics. Results explicitly show that our proposed scheme can obtain significantly better effect for text classification than others. Specifically, when linear support vector machine classifier is run, performances were improved to 0.972 and 0.833 (micro-F1) on Reuters-21578 dataset and 20 Newsgroups dataset, respectively.
More Than a Gimmick - Digital Tools for Boardgame Play

Despite increasing interest in the use of digital tools in boardgames for both commercial and research purposes, little research has to date explored how and why these tools are used. We interviewed 18 professionals working in the boardgame industry to explore the combination of digital tools and tabletop play, which affords new experiences and opportunities for both players and designers. We generated five key themes from the interview data. Participants engaged with ontological questions about the fundamental nature of games; they showed strong opinions about the use of digital tools; they discussed the impacts of digital tools for game design as well as in their design practice; they raised concerns about the costs to develop and maintain such tools; and they considered how they affect the in-game player experience. From these themes, we generate five design principles for digital tools for boardgame play: traceability, completeness, integration, privacy, and materiality. Grounded in empirical data, these design principles guide game designers and researchers seeking to explore this novel design space. Our research focuses attention on the role of digital components in play and on the need for thoughtful implementation that considers the entire lifecycle of the game, from development through publication and, ultimately, archival access.
Env-QA: A Video Question Answering Benchmark for Comprehensive Understanding of Dynamic Environments

Visual understanding goes well beyond the study of images or videos on the web. To achieve complex tasks in volatile situations, the human can deeply understand the environment, quickly perceive events happening around, and continuously track objects’ state changes, which are still challenging for current AI systems. To equip AI system with the ability to understand dynamic ENVironments, we build a video Question Answering dataset named Env-QA. Env-QA contains 23K egocentric videos, where each video is composed of a series of events about exploring and interacting in the environment. It also provides 85K questions to evaluate the ability of understanding the composition, layout, and state changes of the environment presented by the events in videos. Moreover, we propose a video QA model, Temporal Segmentation and Event Attention network (TSEA), which introduces event-level video representation and corresponding attention mechanisms to better extract environment information and answer questions. Comprehensive experiments demonstrate the effectiveness of our framework and show the formidable challenges of Env-QA in terms of long-term state tracking, multi-event temporal reasoning and event counting, etc.
Interaction via Bi-directional Graph of Semantic Region Affinity for Scene Parsing

In this work, we devote to address the challenging problem of scene parsing. It is well known that pixels in an image are highly correlated with each other, especially those from the same semantic region, while treating pixels independently fails to take advantage of such correlations. In this work, we treat each respective region in an image as a whole, and capture the structure topology as well as the affinity among different regions. To this end, we first divide the entire feature maps to different regions and extract respective global features from them. Next, we construct a directed graph whose nodes are regional features, and the bi-directional edges connecting every two nodes are the affinities between the regional features they represent. After that, we transfer the affinity-aware nodes in the directed graph back to corresponding regions of the image, which helps to model the region dependencies and mitigate unrealistic results. In addition, to further boost the correlation among pixels, we propose a region-level loss that evaluates all pixels in a region as a whole and motivates the network to learn the exclusive regional feature per class. With the proposed approach, we achieves new state-of-the-art segmentation results on PASCAL-Context, ADE20K, and COCO-Stuff consistently.
Assessing L2 English speaking using automated scoring technology: examining automarker reliability

(2021). Assessing L2 English speaking using automated scoring technology: examining automarker reliability. Assessment in Education: Principles, Policy & Practice: Vol. 28, Use of Innovative Technology in Oral Language Assessment, pp. 411-436.
Self-supervised Transfer Learning for Hand Mesh Recovery from Binocular Images

Traditional methods for RGB hand mesh recovery usually need to train a separate model for each dataset with the corresponding ground truth and are hardly adapted to new scenarios without the ground truth for supervision. To address the problem, we propose a self-supervised framework for hand mesh estimation, where we pre-learn hand priors from existing hand datasets and transfer the priors to new scenarios without any landmark annotations. The proposed approach takes binocular images as input and mainly relies on left-right consistency constraints including appearance consensus and shape consistency to train the model to estimate the hand mesh in new scenarios. We conduct experiments on the widely used stereo hand dataset, and the experimental results verify that our model can get comparable performance compared with state-of-the-art methods even without the corresponding landmark annotations. To further evaluate our model, we collect a large real binocular dataset. The experimental results on the collected real dataset also verify the effectiveness of our model qualitatively.
Data Augmentation in Logit Space for Medical Image Classification with Limited Training Data

Successful application of deep learning often depends on large amount of training data. However in practical medical image analysis, available training data are often limited, often causing over-fitting during model training. In this paper, a novel data augmentation method is proposed to effectively alleviate the over-fitting issue, not in the input space but in the logit space. This is achieved by perturbing the logit vector of each training data within the neighborhood of the logit vector in the logit space, where the size of neighborhood can be automatically and adaptively estimated for each training data over training stages. The augmentations in the logit space may implicitly represent various transformations or augmentations in the input space, and therefore can help train a more generalizable classifier. Extensive evaluations on three small medical image datasets and multiple classifier backbones consistently support the effectiveness of the proposed method.
A Comparative Study of Methods for Visualizable Semantic Embedding of Small Text Corpora

Text embedding has recently emerged as a very useful and successful method for semantic representation. Following initial word-level embedding methods such as Latent Semantic Analysis (LSA) and topic-based bag-of-words approaches like Latent Dirichlet Allocation (LDA), the focus has turned to language models and text encoders implemented as neural networks - ranging from word-level models to those embedding whole documents. The distinctive feature of these models is their ability to infer semantic spaces at all levels based purely on data, with no need for complexities such as syntactic analysis or ontology building. Many of these models are available pre-trained on enormous amounts of data, providing downstream applications with general-purpose semantic spaces. In particular, embedding models at the sentence level or higher are most useful in applications because the meaning of text only becomes clear at that level. Most text embedding methods produce text embeddings in high-dimensional spaces, with a dimensionality ranging from a few hundred to thousands. However, it is often useful to visualize semantic spaces in very low dimension, which requires the use of dimensionality reduction methods. It is not clear what language models and what method of dimensionality reduction would work well in these cases. In this paper, we compare four text embedding methods in combination with three methods of dimensionality reduction to map three related real-world datasets comprising textual descriptions of items in a particular domain (sports) to a 2-dimensional semantic visualization space. The results provide several insights into the utility of these methods for data of this type.
Detection of Violence in Cartoon Videos Using Visual Features

Technology play an important role in today era. Tremendous growth has been observed in the field of communication and technology. Same is noteworthy in case of video and multimedia. An enormous amount of visual clips containing all types of scenes can easily be accessed both online and offline. In the use of internet has increase the trend of watching cartoons as a source of entertainment among children. There is a tendency that cartoon video has scenes that make some people uncomfortable because of the contents of the video that may be against their feelings and acceptability levels. There could be a number of objectionable elements in cartoon videos. This work presents a framework based on knowledgebase, with visual features which is divided into two categories i.e. character and object database. That deal with the detection of violent contents in cartoon videos which can have negative impact on child personality. A prototype of this system is developed and implemented in Mat lab. The experimental results show that the proposed system successfully detects the violent scenes in cartoon videos.
Ortho-Shot: Low Displacement Rank Regularization with Data Augmentation for Few-Shot Learning

In few-shot classification, the primary goal is to learn representations from a few samples that generalize well for novel classes. In this paper, we propose an efficient low displacement rank (LDR) regularization strategy termed Ortho-Shot; a technique that imposes orthogonal regularization on the convolutional layers of a few-shot classifier, which is based on the doubly-block toeplitz (DBT) matrix structure. The regularized convolutional layers of the few-shot classifier enhances model generalization and intra-class feature embeddings that are crucial for few-shot learning. Overfitting is a typical issue for few-shot models, the lack of data diversity inhibits proper model inference which weakens the classification accuracy of few-shot learners to novel classes. In this regard, we broke down the pipeline of the few-shot classifier and established that the support, query and task data augmentation collectively alleviates overfitting in networks. With compelling results, we demonstrated that combining a DBT-based low-rank orthogonal regularizer with data augmentation strategies, significantly boosts the performance of a few-shot classifier. We perform our experiments on the miniImagenet, CIFAR- FS and Stanford datasets with performance values of about 5% when compared to state-of-the-art.
1D Phase Unwrapping Based on the Quasi-Gramian Matrix and Deep Learning for Interferometric Optical Fiber Sensing Applications

Phase unwrapping is one of the key problems in interferometric fiber sensors, which usually acts as the system performance bottleneck. Compared with the two-dimensional phase unwrapping, the one-dimensional phase unwrapping suffers more seriously from noise. Because modern phase unwrapping algorithms need to make the best use of all the adjacent phase points when evaluating the true phase at a given point. But the available adjacent points in one-dimensional phase unwrapping are very limited. A two-step one-dimensional phase unwrapping algorithm is proposed in this work to combat the above problems. In the first step, the one-dimensional phase is encoded into two-dimensional array based on the quasi-Gramian matrix, and in the second step the deep convolutional neural network (DCNN) is adopted for phase unwrapping. Both simulation and actual experiment results show that the unwrapped phase quality by using our method obviously outperforms the traditional methods with the signal-to-noise ratio (SNR) of lower than 4 dB, and it can still work stably even for negative SNR.
Image analysis algorithm for the verification of hexagonal symmetry in spherical nanostructures

Verification of ordering and symmetry is essential to enhance the nanofabrication process of periodic nanostructures. In this paper, we present the open-source software HEXI, which can detect circles and distinguish between perfect hexagonal ordering and defect configurations. The proposed user-friendly image analysis software (implemented in Python) consists of several stages. First, the algorithm identifies circular structures in microscopy (e.g., scanning electron microscopy, atomic force microscopy) images using the Canny edge detector and the Hough circle transform. Then, the detected circles are categorized as hexagonally ordered or non-hexagonally ordered (defects). This classification can be achieved using three different methods: variance in brightness (global or adaptive) and distance. The software generates visual output (detected coloured circles overlaid over the original image) and quantitative data (total/defective circle count, surface coverage, histogram of size distribution). The circle detection and classification are demonstrated on real-world samples (e.g., spin-coated monolayer of polystyrene spheres, arrays of plasma etched nanospheres, holes). They verify the accuracy of the proposed algorithm, which successfully analysed circular nanostructures of various types (close-packed and non-closed packed arrays, unevenly lit large areas) with hexagonal symmetry, independently of preparation technique (nanosphere lithography, electron beam lithography). Finally, the performance and advantages of the software are discussed.
Role of Layers and Neurons in Deep Learning With the Rectified Linear Unit

Deep learning is used to classify data into several groups based on nonlinear curved surfaces. In this paper, we focus on the theoretical analysis of deep learning using the rectified linear unit (ReLU) activation function. Because layers approximate a nonlinear curved surface, increasing the number of layers improves the approximation accuracy of the curved surface. While neurons perform a layer-by-layer approximation of the most appropriate hyperplanes, increasing their number cannot improve the results obtained via canonical correlation analysis (CCA). These results illustrate the functions of layers and neurons in deep learning with ReLU.
Embedding Symbolic Temporal Knowledge into Deep Sequential Models

Sequences and time-series often arise in robot tasks, e.g., in activity recognition and imitation learning. In recent years, deep neural networks (DNNs) have emerged as an effective data-driven methodology for processing sequences given sufficient training data and compute resources. However, when data is limited, simpler models such as logic/rule-based methods work surprisingly well, especially when relevant prior knowledge is applied in their construction. However, unlike DNNs, these "structured" models can be difficult to extend, and do not work well with raw unstructured data. In this work, we seek to learn flexible DNNs, yet leverage prior temporal knowledge when available. Our approach is to embed symbolic knowledge expressed as linear temporal logic (LTL) and use these embeddings to guide the training of deep models. Specifically, we construct semantic-based embeddings of automata generated from LTL formula via a Graph Neural Network. Experiments show that these learnt embeddings can lead to improvements on downstream robot tasks such as sequential action recognition and imitation learning.
Cost-to-Go Function Generating Networks for High Dimensional Motion Planning

This paper presents c2g-HOF networks which learn to generate cost-to-go functions for manipulator motion planning. The c2g-HOF architecture consists of a cost-to-go function over the configuration space represented as a neural network (c2g-network) as well as a Higher Order Function (HOF) network which outputs the weights of the c2g-network for a given input workspace. Both networks are trained end-to-end in a supervised fashion using costs computed from traditional motion planners. Once trained, c2g-HOF can generate a smooth and continuous cost-to-go function directly from workspace sensor inputs (represented as a point cloud in 3D or an image in 2D). At inference time, the weights of the c2g-network are computed very efficiently and near-optimal trajectories are generated by simply following the gradient of the cost-to-go function.We compare c2g-HOF with traditional planning algorithms for various robots and planning scenarios. The experimental results indicate that planning with c2g-HOF is significantly faster than other motion planning algorithms, resulting in orders of magnitude improvement when including collision checking. Furthermore, despite being trained from sparsely sampled trajectories in configuration space, c2g-HOF generalizes to generate smoother, and often lower cost, trajectories. We demonstrate cost-to-go based planning on a 7 DoF manipulator arm where motion planning in a complex workspace requires only 0.13 seconds for the entire trajectory.
Weakly-Supervised Video Object Grounding via Stable Context Learning

We investigate the problem of weakly-supervised video object grounding (WSVOG), where only the video-sentence annotations are provided for training. It aims at localizing the queried objects described in the sentence to visual regions in the video. Despite the recent progress, existing approaches have not fully exploited the potential of the description sentences for cross-modal alignment in two aspects: (1) Most of them extract objects from the description sentences and represent them with fixed textual representations. While achieving promising results, they do not make full use of the contextual information in the sentence. (2) A few works have attempted to utilize contextual information to learn object representations, but found a significant decrease in performance due to the unstable training in cross-modal alignment. To address the above issues, in this paper, we propose a Stable Context Learning (SCL) framework for WSVOG which jointly enjoys the merits of stable learning and rich contextual information. Specifically, we design two modules named Context-Aware Object Stabilizer module and Cross-Modal Alignment Knowledge Transfer module, which are cooperated together to inject contextual information to stable object concepts in text modality and transfer contextualized knowledge in cross-modal alignment. Our approach is finally optimized under a frame-level MIL paradigm. Extensive experiments on three popular benchmarks demonstrate its significant effectiveness.