Reviewerly/exHarmony huggingface dataset:
Automatic Co-Design of Aerial Robots Using a Graph Grammar

Unmanned aerial vehicles (UAVs) have broad applications including disaster response, transportation, photography, and mapping. A significant bottleneck in the development of UAVs is the limited availability of automatic tools for task-specific co-design of a UAV's shape and controller. The development of such tools is particularly challenging as UAVs can take many forms, including fixed-wing planes, radial copters, and hybrid topologies, with each class of topology showing different advantages. In this work, we present a computational design pipeline for UAVs based on a graph grammar that can search across a wide range of topologies. Graphs generated by the grammar encode different topologies and component selections, while continuous parameters encode the dimensions and properties of each component. We further augment the shape representation with deformation cages, which allow expressing a variety of wing shapes. Each UAV design is associated with an LQR controller with tunable continuous parameters. To search over this complex discrete and continuous design space, we develop a hybrid algorithm that combines discrete graph search strategies and gradient-based continuous optimization methods using a differentiable UAV simulator. We evaluate our pipeline on a set of simulated flight tasks requiring dynamic motions, showing that it discovers novel UAV designs that outperform canonical UAVs typically made by engineers.
Building a Combined Morphological Model for Russian Word Forms

In recent years, high-precision machine learning models for traditional inflectional morphological analysis, as well as models for morpheme segmentation of words were built for Russian. Two these morphological tasks are evidently related, and some NLP applications may require to perform both of them, so development and evaluation of combined morphological model is of research interest. Such a model is supposedly useful for processing texts in languages with rich morphology (e.g., Russian), in particular, for deriving meaning of new words rarely encountered in texts. The paper presents a neural model implementing both inflectional analysis of Russian word forms (with morphological disambiguation) and their segmentation into constituent morphs with their classification. To train the model, a relevant dataset was built, by morphemic labeling of SynTagRus corpus, and transfer learning techniques were applied. Experimental evaluation of the model has shown its sufficiently high quality: 94.2% of precision for morphological tags disambiguation and 88–91% of word-level classification accuracy for segmentation.
Remaining Cyber-Active: Teaching UX Design with Online User-Research Tools During the COVID-19 Pandemic

Teaching user experience (UX) design has increasingly become a virtual process. This is particularly true for instructors running online synchronic courses due to the impact of the recent pandemic. A large portion of user research study, which is the backbone of UX design, is now undertaken using non-contact modes and conducted through online tools and methods to facilitate students learning this critical piece of UX design. User research study is essential to UX design. It leads to creativity and inspires design thinking. To fulfil their coursework on user research study, students explored online resources and utilised remote user research tools, such as online card sorting for user-centred information architecture and a web-based eye tracking machine learning tool for information hierarchy. Even though it lacked physical human interaction, using online user research tools in our experience was found to be accessible and effective and produced satisfactory learning results. This paper shares the experience of the online synchronic teaching of a UX design course at a state university and calls for an open discussion for utilising online tools in UX design education, which holds potential for becoming a new normal in academia in the post-pandemic era.
GazeFollowTR: A Method of Gaze Following with Reborn Mechanism

Gaze following is the task of estimating where an observer is looking inside a scene. Both the observer and scene information must be learned to determine the gaze directions and gaze points. Recently, many existing works have only focused on scenes or observers. In contrast, revealed frameworks for gaze following are limited. In this paper, a gaze following method using a hybrid transformer is proposed. Based on the conventional method (GazeFollow), we conduct three developments. First, a hybrid transformer is applied for learning head images and gaze positions. Second, the pinball loss function is utilized to control the gaze point error. Finally, a novel ReLU layer with the reborn mechanism (reborn ReLU) is conducted to replace traditional ReLU layers in different network stages. To test the performance of our developments, we train our developed framework with the DL Gaze dataset and evaluate the model on our collected set. Through our experimental results, it can be proven that our framework can achieve outperformance over our referred methods.
Enhanced Perspective Generation by Consensus of NeX neural models

Neural rendering is a relatively new field of research that aims to produce high quality perspectives of a 3D scene from a reduced set of sample images. This is done with the help of deep artificial neural networks that model the geometry and color characteristics of the scene. The NeX model relies on neural basis expansion to yield accurate results with a lower computational load than the previous NeRF model. In this work, a procedure is proposed to further enhance the quality of the perspectives generated by NeX. Our proposal is based on the combination of the outputs of several NeX models by a consensus mechanism. The approach is compared to the original NeX for a wide range of scenes. It is found that our method significantly outperforms the original procedure, both in quantitative and qualitative terms.
Space-correlated Contrastive Representation Learning with Multiple Instances

Self-supervised contrastive learning methods have shown promising transferability in pretraining by maximizing the mutual information between two cropped regions as views from the same image. In order to effectively extract mutual information between views, the cropped regions need to be the same instance as prior hypothesis. However, the data collected in general scenes usually have multiple instances, so the two cropped regions probably contain different instances which will mislead the contrastive learning process. In this paper, we make the first attempt to exploit the spatial position relationships of the two cropped regions in self-supervised contrastive learning with images that include multiple instances. Then, we propose an effective method called Space-correlated Contrastive Learning (SpaceCL). Specifically, given two randomly cropped regions as contrastive pairs from the same image, we implement self-supervised contrastive learning by optimizing a space correspondence contrastive similarity loss. As a result, our method achieves state-of-the-art performance and remarkably outperforms other counterparts when pretrained on the COCO dataset of which images contain multiple instances. Experiments show our method outperforms ReSim with 2.6%AP on PASCAL VOC object detection, 0.8%AP <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">bb</sup> and 0.6%AP <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">mk</sup> on COCO object detection and instance segmentation, 1.3%AP <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">mk</sup> on Cityscapes instance segmentation.
Improving Style Randomization via Domain-specific Feature Reweighting for Domain Generalization

Despite the steady progress of neural networks, their applicability to the real world is limited because they often fail to generalize to unseen domains. To overcome this challenge, recent studies have proposed various methods for improving out-of-distribution generalizations. However, these methods require complex architectures or additional learning strategies that involve non-trivial efforts. On the other hand, style randomization, a feature-level augmentation strategy, can increase the networks' generalization capability simply by diversifying the source domains. In this paper, we focus on improving the internal process of style randomization to produce more diverse samples that help networks learn domain-invariant representation. To this end, we propose a novel feature-level augmentation strategy that generates diverse samples for contents as well as styles. Our method can be implemented very simply but outperforms all compared methods in experiments on the DomainBed benchmark.
Research on parallel corpus classification based on pre-trained model

At present, the data in most text classification tasks are only in a single language, but the bilingual text information value can be fully utilized in the scenario of Chinese-English parallel corpus. A classification model combining the text features of pre-training model ERNIE and BERT is proposed. ERNIE is used to process The Chinese corpus, and BERT is used to process the English corpus.TextCNN is used to fuse text feature vectors.Thus, the classification effect of parallel corpus can be improved.Comparative experimental tests were performed on the data set.The results show that this method has better classification effect in parallel corpus.
Pixel-level Class-Agnostic Object Detection using Texture Quantization

Object detection is a widely explored topic within the computer vision research field mostly because it is necessary for almost every system containing some kind of visual scene understanding or interpretation. Significant advances throughout the last 40 years allowed us to evolve from early techniques based on template matching to modern deep detectors capable of detecting thousands of different classes of objects with reasonable performance. Nonetheless, as approaches kept improving, more challenging topics related to object detection have been proposed. Classic object detectors have to be trained with all classes that might be presented in the testing phase. However, this is a problem in real-world scenarios because it is impossible to know the whole domain of possible objects. Hence, a task has emerged called class-agnostic object detection that essentially detects objects without determining their classes. In this paper, we address this task using a convolutional network and texture graylevel quantization. Our results showed that our model could improve 2.1 percentage points (p.p.) from the best baseline on objects that were not annotated in the training phase.
HAJPAQUE: Hardware Accelerator for JSON Parsing, Querying and Schema Validation

JSON (JavaScript Object Notation) is quickly becoming the default currency for semi-structured data exchange on the web; hence, it is heavily used in analytics pipelines. State-of-the-art analytics pipelines can now process data at a rate that exceeds 50 Gbps owing to recent advances in RDMA, NVM, and network technology (notably Infiniband). The peak throughput of the best-performing software solutions for parsing, querying, and validating JSON data is 20 Gbps, which is far lower than the current requirement. We propose a novel HW-based accelerator, HAJPAQUE, that ingests 16-bytes of JSON data at a time and processes all the 16 bytes in parallel as opposed to competing approaches that process such data byte by byte. Our novel solution comprises lookup tables, parallel sliding windows, and recursive computation. Together, they ensure that our online pipeline does not encounter any stalls while performing all the operations on JSON data. We ran experiments on several widely used JSON benchmarks/datasets and demonstrated that we can parse and query JSON data at 106 Gbps (@28 nm).
Understanding User Experience with Recommendations in Social Network Service Feed

Today, recommendations that suggest possibly interesting contents to users using recommender systems have become a prevalent feature in various services. In the social network service (SNS) feed, one of the most active services that use recommendations, users now face the posts from the accounts that they do not follow. However, although the expected user experience with recommendations in the SNS differs from other domains due to a unique aspect of social connections, many parts of interaction with recommendations still have not been explored in the social network context. The aim of our study is to understand user experience with recommendations in the SNS feed and to explore possible design opportunities of the recommendations in the context of social interplay. Under this research goal, we conducted a qualitative survey composed of open-ended questions with 70 SNS users. The results show how SNS users interact with recommendations along with their expectations and obstacles regarding recommendations in their feed. Furthermore, we discuss implications for designing the interaction with the recommendations in the SNS feed.
IndoLayout: Leveraging Attention for Extended Indoor Layout Estimation from an RGB Image

In this work, we propose IndoLayout, a novel real-time approach for generating high-quality occupancy maps from an RGB image for indoor scenes. Such occupancy maps are often crucial for path-planning and mapping in indoor environments but are often built using only information contained in the ego view. In contrast, our approach also predicts occupancy values beyond immediately visible regions from just a monocular image, leveraging learnt priors from indoor scenes. Hence, our proposed network can produce a hallucinated, amodal scene layout that includes areas occluded in the RGB image, such as a navigable floor behind a desk. Specifically, we propose a novel architecture that uses self-attention and adversarial learning to vastly improve the quality of the predicted layout. We evaluate our model on several photorealistic indoor datasets and outperform previous relevant work on all metrics that measure layout quality, including newly adopted ones. Finally, we demonstrate the effectiveness of our method by showing significant improvements on the PointNav task over similar approaches using IndoLayout. For more details, please refer to the project page: https://indolayout.github.io/.
SemanticGAN: Facial Image Editing with Semantic to Realize Consistency

Recent work has shown that face editing in the latent space of Generative Adversarial Networks(GANs). However, it is difficult to decouple the attributes in latent space that reduce the inconsistent face editing. In this work, we proposed a simple yet effective method named SemanticGAN to realize consistent face editing. First, we get fine editing on attribute-related regions and note that we mainly consider the accuracy of the edited images possessing the target attributes instead of whether the editing of irrelevant regions is inconsistent. Second, we optimize the attribute-independent regions that ensure the edited face image consistent with the raw image. Specifically, we apply the generated semantic segmentation to distinguish the edited regions and the unedited regions. Extensive qualitative and quantitative results validate our proposed method. Comparisons show that SemanticGAN can achieve a satisfactory image-consistent editing result.
A Point Matching Strategy of 3D Loss Function for Single RGB Images Deep Mesh Reconstruction

Recent the-state-of-the-art image-based three-dimensional (3D) reconstruction methods that represent 3D shapes mainly using triangular mesh because of its memory efficiency and ability to present surface detail of objects compared to voxel and point cloud. Previous works usually follow an encoding and decoding pattern. A deep neural network to extract the features from the picture and reconstruct the 3D structure. It is a typical supervised learning process, requiring loss function to supervise the training. No existing works directly calculate the loss between the reconstruction mesh and ground truth mesh. Instead, they indirectly used the Chamfer Distance (CD) between point clouds as the loss. Most of the previous works focus on the encoding and decoding parts instead of the loss and CD is used for all works. However, when CD is applied to two point clouds with the same number of points, some points can match any number of points in another point cloud, so some points will be less involved in calculating the loss function, which will reduce the utilization of information. Therefore, We propose a new point matching strategy to calculate the loss. The point matching strategy we proposed limits the maximum number of matches for each point, allowing more points to be more involved in the loss calculation, thereby improving the information utilization rate. Experiments on single view reconstruction (SVR) and auto-encoding methods show that this new loss method can replace CD in this type of works and has better training results and 3D reconstruction quality.
Text Sentiment Analysis Based on BERT-TextCNN-BILSTM

As a critical component of natural language processing, text sentiment analysis has taken on an increasingly important role in areas such as recommendation systems, customer evaluation analysis and opinion referencing. In order to more accurately extract the sentiment embedded in text from text information, this paper proposes a BTB model, which is a text sentiment classification model incorporating BERT, TextCNN, and BILSTM. In the BTB model, the generated word vector has more a priori information, and the important features of the text and contextual information are efficiently used in the sentiment analysis. The results of experiments show that the BTB model achieves high precision, recall and F1 value, and has good generalisation ability.
Coda

The coda summarizes the main points made in the fragments and their commentaries by listing four basic principles about interculturality: interculturality is about humanity first and foremost; interculturality does not make us meet the other but it makes us co-change; interculturality as an object of research and education is polymorphic and must always be considered as such; avoiding perspectives that make the notion meaningless, doxic, purely decorative and worthless for education should be a priority. The book concludes with a recommendation: making a habit of writing fragment about interculturality as a way of expanding our thoughts about and engagement with such a complex notion.
Categorical-Continuous Bayesian Optimization Applied to Chemical Reactions

Chemical reaction optimization is a challenging task for the industry. Its purpose is to experimentally find reaction parameters (e.g. temperature, concentration, pressure) that maximize or minimize a set of objectives (e.g. yield or selectivity of the chemical reaction). These experiments are often expensive and long (up to several days), making the use of modern optimization methods more and more attractive for chemistry scientists. Recently, Bayesian optimization has been shown to outperform human decision-making for the optimization of chemical reactions [16]. It is well-suited for chemical reaction optimization problems, for which the evaluation is expensive and noisy. In this paper we address the problem of chemical reaction optimization with continuous and categorical variables. We propose a Bayesian optimization method that uses a covariance function specifically designed for categorical and continuous variables and initially proposed by Ru et al. in the COCABO method [14]. We also experimentally compare different methods to optimize the acquisition function. We measure their performances in the optimization of multiple chemical reaction (or formulation) simulators. We find that a brute-force approach for the optimization of the acquisition function offers the best results but is too slow when there are many categorical variables or categories. However we show that an ant colony optimization technique for the optimization of the acquisition function is a well-suited alternative when the brute-force approach cannot be (reasonably) used. We show that the proposed Bayesian optimization algorithm finds optimal reaction parameters in fewer experiments than state of the art algorithms on our simulators.
Research and development of robot arm applied to grinding path planning of metal parts

Using the Open CASCADE source to develop a robotic arm simulation grinding path for the metal parts is the purpose of this research. Firstly, a metal parts CAD/CAM grinding script file is created and translated into a special format for driving the presented robot arm. Then the geometric dimensions of the robotic arm and the belt sander in the program are built. Secondly, the forward kinematics and inverse kinematics models of the robotic arm are imported. Finally, the simulation and testing of the metal parts grinding path by combining the above translation program are achieved. Compared with the traditional manual teaching and correction of robotic arm path planning, the grinding efficiency can be improved in this study.
A Comparative Study of Medical Image Retrieval Using Distance, Transform, Texture, and Shape

Content-based medical image retrieval (CBMIR) is the application of computer vision techniques to the problem of medical image search in large databases. Three main techniques are applied to check the applicability. The first technique implemented is distance metrics-based retrieval. The second technique implemented is transform-based retrieval. The transform which has lesser performance is combined with higher performance, to check the applicability of the results. The third technique implemented is content-based medical image retrieval. Texture and shape-based retrieval techniques are also applied. Shape-based retrieval is processed using canny edge with the Otsu method. The multifeature-based technique is also applied and analyzed. The best retrieval rate is achieved by multifeature-based retrieval with 100/50%. Based on more relevant retrieved images all the three, brain, liver, and knee, images are found to be retrieved more with 100/50%.
Secure Collaboration Between Consortiums in Permissioned Blockchains

Secure collaboration between consortiums is imperative for the distributed applications deployed on distinct permissioned blockchains or consortium blockchains to provide superior services. The collaboration can be realized by cross-chain transactions processed by cross-chain protocol. The previous two-phase commit and two-phase locking cross-chain protocol are unable to be applied in the scenario where the consortium can be malicious. In this work, we propose the ledger framework of the multiple consortium blockchain systems including the global cross-chain ledger. The cross-chain ledger establishes the order relationship between cross-chain sub-transactions defined in the cross-chain protocol and adopts the hierarchical Byzantine fault-tolerant consensus to tolerate malicious consortiums. We also give two instances of the framework. Based on the cross-chain ledger, we propose the revoke operation in the two-phase commit and two-phase locking cross-chain protocol. We also make the protocol secure in the presence of malicious consortiums and prove its security. Finally, we conduct comprehensive experiments to characterize the performance of the two instances and the cross-chain protocol. The experimental results reveal the efficiency of our scheme even in the presence of malicious consortiums.