Reviewerly/exHarmony huggingface dataset:
Learning a World Model With Multitimescale Memory Augmentation

Model-based reinforcement learning (RL) is regarded as a promising approach to tackle the challenges that hinder model-free RL. The success of model-based RL hinges critically on the quality of the predicted dynamic models. However, for many real-world tasks involving high-dimensional state spaces, current dynamics prediction models show poor performance in long-term prediction. To that end, we propose a novel two-branch neural network architecture with multi-timescale memory augmentation to handle long-term and short-term memory differently. Specifically, we follow previous works to introduce a recurrent neural network architecture to encode history observation sequences into latent space, characterizing the long-term memory of agents. Different from previous works, we view the most recent observations as the short-term memory of agents and employ them to directly reconstruct the next frame to avoid compounding error. This is achieved by introducing a self-supervised optical flow prediction structure to model the action-conditional feature transformation at pixel level. The reconstructed observation is finally augmented by the long-term memory to ensure semantic consistency. Experimental results show that our approach is able to generate visually-realistic long-term predictions in DeepMind maze navigation games, and outperforms the prevalent state-of-the-art methods in prediction accuracy by a large margin. Furthermore, we also evaluate the usefulness of our world model by using the predicted frames to drive an imagination-augmented exploration strategy to improve the model-free RL controller.
Word‐Based Method for Chinese Part‐of‐Speech via Parallel and Adversarial Network

Chinese part-of-speech (POS) tagging is an essential task for Chinese downstream natural language processing tasks. The accuracy of the Chinese POS task will drop dramatically by word-based methods because of the segmentation errors and the word sparsity. Also, there are several Chinese POS tagging sets with different criteria. Some of them only have a small-scale annotated corpus and are hard to train. To this end, we propose a modified word-based transformer neural network architecture. Meanwhile, we utilize an adversarial transfer learning method that splits the architecture into shared and private parts. This work directly improves the ability of the word-based model, instead of adopting a joint character-based method. Extensive experiments show that our method achieves state-of-the-art performance on all datasets, and more importantly, our method improves performance effectively for the word-based Chinese sequence labeling task.
Tweedie hidden Markov random field and the expectation-method of moments and maximisation algorithm for brain MR image segmentation

In this paper, a segmentation algorithm of brain magnetic resonance imaging is elaborated. The proposed method rests on Tweedie hidden Markov random field processing and the Expectation Method of moments and Maximisation algorithm. This new type of stochastic process stands for a Markov Random Field whose state sequence cannot be observed directly and whose spatial information is encoded through the mutual influences of neighbouring sites. Tweedie models are a special case of exponential dispersion models with power mean variance. In image processing problems as medical image segmentation no work has been recorded in terms of demonstrating the applicability of the Tweedie models with hidden Markov random field and different Tweedie parameters values p. Hence, our research stands for the pioneering work aiming at proving the efficiency of the Tweedie models in brain image segmentation. Expectation- Method of moments and Maximisation algorithm results from the combination of both EM (Expectation-Maximisation) algorithm and the method of moments. The algorithm was validated on synthetic data and tested on real images. The basic merit of our proposed algorithm of segmentation lies in its ability to identify the brain tumour. Experiments using Tweedie hidden Markov random field showed that the proposed method produces more intuitive results with a recognition rate of 93.72. In fact, the experimental results highlighted that our approach outperforms other approaches that are reported in literature.
Integration of optimized local directional weber pattern with faster region convolutional neural network for enhanced medical image retrieval and classification

Abstract With a broad usage of digital imaging data in hospitals, the medical image repository size is growing in a very rapid manner. This creates difficulty in querying and managing these vast databases that in turn leads to the requirement of Content‐Based Medical Image Retrieval (CBMIR) systems. The CBMIR is considered as the major ambiguous and challenging task for reducing the semantic gap among the human queries and images in the datasets having more information content. The main intention of this paper is to enhance medical image retrieval and classification using the improved pattern descriptor and deep learning. For the medical image retrieval, this paper develops the Optimized Local Directional Weber Pattern with a multi‐objective similarity function. This similarity function focuses on the measures like Structural Similarity Index Measure, Peak Signal‐to‐Noise Ratio, Mean Squared Error MSE, and correlation. Likewise, the development of an improved Faster‐Region Convolutional Neural Network (Faster‐RCNN) is adopted in the classification phase. Both the retrieval and classification are enhanced by the Modified Wind Speed‐based Deer Hunting Optimization Algorithm. Considering classification and retrieval tasks, the experiments on benchmark datasets reveal an enhancement from the introduced algorithm when compared over the existing methods.
Ship detection in infrared images via bounding boxes based on improved YOLOX

One of the important measures for ship safety is ship detection. Due to the limited visibility at night, it is crucial to detect the ship's infrared night-vision image. We updated the YOLOX to enhance the precision and real-time of ship detection at night, primarily by replacing the CspLayer with a Ghostbottleneck that could adjust to a simple infrared image of the sea environment. Based on the public ship infrared image datasets, our configuration is trained and evaluated. Experiments reveal that, compared to the present algorithm, our method can expedite convergence rate, enhance the F1-score of ship infrared image detection by 0.02, and increase the mPA value by 2.93%.
Show of Hands: Leveraging Hand Gestural Cues in Virtual Meetings for Intelligent Impromptu Polling Interactions

Increased virtual meeting software usage has allowed people to meet remotely in a more seamless fashion. However, compared to in-person meetings, valuable interaction cues such as impromptu group polling are less optimally executed due to increased difficulty in gauging remote participants, while also requiring prior meeting setup for automated counting with built-in polling tools. We propose a novel intelligent user interface approach for virtual meeting software that supports impromptu polling interactions by leveraging real-time hand gesture recognition and video filter feedback. We conducted studies to design and evaluate this intuitive gesture-based polling system with visual feedback. Our results demonstrated that our system was able to recognize attendees’ gestures and poll responses with reasonable accuracy, and showed improvements in hosts’ task workload performance. From our findings, our interface informs hosts of valuable results while maintaining organic gestural interaction cues with attendees similar to in-person meetings.
A Smooth Reformulation of Collision Avoidance Constraints in Trajectory Planning

This work describes a novel, computational-cost-reducing collision avoidance constraint for trajectory planning of robotic systems through adopting a smooth approximation of the maximum function. The proposed method reduces the number of constraints in a trajectory planning problem by expressing a number of constraints as one single constraint that is approximately equivalent to the most critical constraint. This reformulation lowers planning times while still maintaining identical guarantees on collision avoidance as a traditional approach. Although this paper focuses on collision avoidance constraints, the presented approach can easily be extended towards other constraints. The algorithm is numerically validated on three cases, both in two and three dimensional environments. It is compared in terms of computational efficiency with a traditional trajectory planner where all collision avoidance constraints are treated separately. The presented results show a promising improvement in performance of trajectory planning for robotic applications.
Vision 360: Image Caption Generation Using Encoder-Decoder Model

Vision360 incorporates three features in itself Image elaboration, speech to text, text to speech. The Main feature is Image Caption Generation, i.e., not only it is responsible for Image Segmentation, Object classification but it also establishes a relation between the objects classified that too with a logical relation that somehow gives the human vibe. Encoder-decoder model has been used. CNN has been used for Image and LSTM has been used for text. The paper also demonstrates the integration InceptionV3 model. Vision360 is a way of providing aid to blind people or partially blind people. It&#x2019;s a way to bring convenience in their proximity in a single touch. It tries to bridge the gap that they have been feeling all along while walking on the same path with different people. A task to describe an Image is not very hard but if we want to automate this task of depicting something from an image and make the machine do it, it&#x2019;ll be nearly impossible, even if the new researches have been made and feature extraction is attainable. Logically establishing semantically and syntactically correct sentences is still a hard task to accomplish. We used encoder-decoder model for parallel training of Image and text data, and used InceptionV3 for extracting feature vector. We evaluated our result on BLEU score metric and the model achieved BLEU score in-range of 0.70 to 0.78 for various images in the validation set.
Relation-aware collaborative autoencoder for personalized multiple facet selection

Collaborative-based personalization has been one of the most successful techniques used in building personalization for recommender systems and facet selection. The technique predicts users’ interests based on the preferences of similar people or items. The prediction is usually made on one single group of users or items/facets. However, multiple facet selection creates a different challenge where the prediction needs to be based on the similarity among different groups of users and facets. In conventional collaborative approach, user–facet representation is created from the concatenation of user preferences on each facet. This creates a spared representation which affects the accuracy of the personalized model. It is essential to develop a more suitable representation that effectively represents the collaborative preferences given across multiple facets and a predictive model to estimate the possible preferences across those groups. Multiple facets appear to be correlated to each other and this can be useful for associating the existing preferences. None of the previous works has addressed the issue due to the association of facet relationships. Hence, this paper aims to examine the effectiveness of a new approach that utilizes multiple-facet relationships to associate the collaborative interests across different facets. This study proposes a new collaborative-based personalization model for multiple facet selection, called Relation-aware Collaborative Autoencoder (RCAE) Model. A new embedding methodology was introduced for incorporating multiple facet relationships into user–facet interaction. Evaluations based on four real-world datasets demonstrated that the proposed model utilizing facet relationships has achieved significant improvement over the conventional collaborative approach.
Design and Application of Interactive Algorithm for Advertising Media Screen Based on Smart Sensor

Touch is one of the most important human senses. With the popularization of touch-screen mobile phones, tablet computers, and other devices, touch-screen interactive technology has become a norm in people’s daily lives, and advertisements that were once dominated by vision and hearing have added an interactive experience in the dimension of touch. Traditional advertising media screens can only complete simple information dissemination functions and cannot interact with users in a two-way manner. They can only receive information one-way and passively and lack interactivity. Touch-screen interactive advertising forms a good interaction with the target audience, thereby disseminating advertising information to achieve the purpose of promotion or brand image building. This paper designs a set of advertising media screen interaction systems based on smart sensors, including a gesture interaction module, a remote interaction module, and a touch interaction module. The gesture interaction module can recognize 5 static gestures and send gesture commands to control the advertising media screen. The remote interaction module can remotely control the advertising media screen, and the touch interaction module can control the advertising media screen through the touch screen. According to the functional requirements, the overall design of software and hardware is given, and the technical background of each module of the software is introduced. Next, the depth image-based gesture recognition method is studied. The number of fingers and the center distance feature are fused as feature vectors, and the weighted template matching method is used to classify and recognize gestures. Finally, the design and implementation of the interactive system are introduced.
A Study on School-Based Teacher Collaboration in Designing and Implementing Online English Classes for University Students

This qualitative case study investigates a school-based teacher learning community’s efforts in conducting online synchronous and asynchronous English classes for college students. It analyzes English instructors’ experiences and opinions as well as students’ responses. It explores important considerations for effective online teaching in a school-based learning environment. To this end, this study conducted interviews with four college English professors who are members of the teacher learning community and gave questionnaires to 23 female college students who enrolled in College English (Listening and Speaking) during the COVID-19 pandemic to determine how satisfied they were with the online classes. The results show that students were satisfied with both asynchronous and synchronous online English classes although they reported that their workload increased for the asynchronous version. Designing and implementing the online English classes was challenging for the instructors, although the teachers reported having enhanced their expertise. Based on these results, this study makes suggestions regarding the enhancement of teacher professional development through a teacher learning community. It also provides implications for how a teacher learning community can take shape, function, grow, and impact learning, and how it can support teaching online English classes. Further research is required, particularly in regard to teacher professional development through a school-based teacher learning community.
An overview of the embedded speech-to-text technology

The first part of the paper briefly introduces the automatic speech recognition (ASR) technology, mainly about its development status and its implementation steps. The second part summarizes the current technical bottlenecks and related solutions in this field, and also predicts the prospects.
Improving content discovery and viewer engagement with AI

We are quickly heading towards the golden age of video. For consumers there is a wealth of video services to choose from, ranging from broad video offerings to content tailored to a specific audience or genre. Many services come with premium content and competitive pricing. This however brings a dilemma: people will only subscribe to a few services, and are more likely to replace a service for another, in case the service they use does not meet their needs. Churn rates of video services are already increasing, and with new services being launched in 2022, the fight for viewer attention will further intensify.
High-Throughput Analysis of Offshore Well Drill-Cuttings via SEM-Automated Mineralogy Using Single-Step Trans-Vertical Moulds

Single-step trans-vertical moulds used to prepare grain mounts for SEM-automated-mineralogy based instruments have been shown to lead to more representative sample statistics. For samples that contain a variety of mineral phases, denser phases can settle to the bottom of an epoxy mould during the curing phase. Slicing and polishing a cross section through density-stratified moulds of the sample material can compensate for this. Aliquots from offshore drill well cuttings were prepared using three different methods: (1) traditional epoxy mounting, (2) two-step trans-vertical moulds, and (3) single-step trans-vertical moulds. All were analysed with a FEI Quanta 650 FEG instrument using MLA software with similar acquisition parameters. The results indicate that the single-step trans-vertical moulds are reproducible, lead to more accurate statistics, and yield MLA calculated elemental assays that closely match with data from standard analytical methods. In addition, these moulds can be made in half the time of the two-step trans-vertical moulds, and because of the unique geometry of the custom-made SEM holders, the analytical throughput is doubled. Depending on the size fraction of the well cuttings, the throughput can be further increased by slicing off the remaining epoxy to allow for more samples in the 26x TV holder. A unique cell holder was created to accommodate these trans-vertical samples, allowing them to fit into a LA-ICP-MS instrument such that detailed follow-up microanalyses can be conducted on specific minerals. For instance geochronology can be conducted on grains of interest (e.g., zircons), which had been mapped by the SEM-MLA.
Ethnic-Guided Soft Tissue Cephalometric Analysis on Lambani Indian Inhabitants for Forensic Facial Reconstruction

Forensic craniofacial reconstruction is a combination of both scientific technique and artistic skill that assist facial soft tissue approximation on the skull to obtain an image of an individual that varies in the different ethnic groups depending on genetic and environmental factors such as soft tissue norms.The present study was aimed to evaluate the soft tissue norms for Lambani Indian tribes spread across the state of Karnataka in India and compare them with the local inherent ethnolinguistic Kannadiga population.Forty healthy individuals encompassing 20 Lambanis and 20 Kannadigas were selected using demographic information. Lateral cephalograms obtained were analyzed for various soft tissue landmarks that include facial angle, upper lip curvature, skeletal convexity, H angle, nose tip to H-line, upper sulcus depth, lower sulcus depth, upper lip thickness, upper lip strain, lower lip to H line, soft tissue chin thickness, and glabella.It was observed that glabella thickness, upper sulcus depth, and lower lip to H line showed a significant difference between Lambani and Kannadiga populations. Lambani's have a higher facial angle than the Kannadiga group though not statistically significant. Gender-wise comparison had shown a significant difference in variables on upper sulcus depth, glabella among females, and lower lip to H line, glabella among males.The differences obtained between the two ethnic groups in this study clearly suggest the need for separate soft tissue thickness norms for distinctive populations that could be vital in the facial reconstruction of an individual in the field of forensic investigation to narrow down the identification process.
Risk-aware Collection Strategies for Multirobot Foraging in Hazardous Environments

Existing studies on the multirobot foraging problem often assume safe settings, in which nothing in an environment hinders the robots’ tasks. In many real-world applications, robots have to collect objects from hazardous environments like earthquake rescue, where possible risks exist, with possibilities of destroying robots. At this stage, there are no targeted algorithms for foraging robots in hazardous environments, which can lead to damage to the robot itself and reduce the final foraging efficiency. A motivating example is a rescue scenario, in which the lack of a suitable solution results in many victims not being rescued after all available robots have been destroyed. Foraging robots face a dilemma after some robots have been destroyed: whether to take over tasks of the destroyed robots or continue executing their remaining foraging tasks. The challenges that arise when attempting such a balance are twofold: (1) the loss of robots adds new constraints to traditional problems, complicating the structure of the solution space, and (2) the task allocation strategy in a multirobot team affects the final expected utility, thereby increasing the dimension of the solution space. In this study, we address these challenges in two fundamental environmental settings: homogeneous and heterogeneous cases. For the former case, a decomposition and grafting mechanism is adopted to split this problem into two weakly coupled problems: the foraging task execution problem and the foraging task allocation problem. We propose an exact foraging task allocation algorithm, and graft it to another exact foraging task execution algorithm to find an optimal solution within the polynomial time. For the latter case, it is proven \( \mathcal {NP} \) -hard to find an optimal solution in polynomial time. The decomposition and grafting mechanism is also adopted here, and our proposed greedy risk-aware foraging algorithm is grafted to our proposed hierarchical agglomerative clustering algorithm to find high-utility solutions with low computational overhead. Finally, these algorithms are extensively evaluated through simulations, demonstrating that compared with various benchmarks, they can significantly increase the utility of objects returned by robots before all the robots have been stopped.
Framework and Case Studies for Context-Aware AR System (CaARS) for Ubiquitous Applications in the AEC Industry

Augmented Reality (AR) systems can be used for different purposes in the AEC industry, including augmentation of physical environments with virtual objects that enhances information understanding and delivery. Previous research developed single-purpose solutions with limited context identification. Broad context identification and holistic AR application across industry stages and stakeholders is lacking. This article presents a prototype framework for an intelligent Context-aware AR System (CaARS) to enable broader applications in the AEC industry. The framework considers novel enablers, including multi-object scanning, a model slicer for customizing a single 3D model, using anamorphic images for object recognition, and an interactive user interface. The framework was analyzed for three case studies that represent various applications of CaARS in the industry. Unlike previous single-purpose AR applications in the industry, the approach described in this article seeks to address the multiple scenarios where AR augmentation serves to increase design and cognitive understanding, decision-making, and task support.