Reviewerly/exHarmony huggingface dataset:
Cluster-based Curriculum Learning for Vision-and-Language Navigation

The vision-and-language navigation (VLN) task requires the agent to navigate by following both natural language instructions and vision. Prior works focus on improving the navigation model or adopting data augmentation to enhance the accuracy and efficiency of navigation. However, few attempts have been made to optimize the training process. In this paper, we propose a cluster-based curriculum learning (CCL) method to schedule appropriate curricula to acquire a more rational learning process. Our CCL dynamically segments the tasks in training dataset into three subsets: easy, medium and hard, according to whether tasks can be successfully completed by the present navigation model. Meanwhile, we propose a dynamic data extraction method based on the volume of subsets to adjust the priority of training from easy to hard. On the other hand, the existing metrics are hard to show the possibility that the agent will complete the task exactly along the expected path. We propose a new metric named path success rate (PSR), where a path is defined as success when the agent passes by all locations described by the instruction. Extensive experiments are conducted to demonstrate the effectiveness and superiority of our proposed CCL on VLN tasks.
L-WiM: Collaborative Exploration in Immersive Environments

An immersive environment provides multiple users a shared space for collaborative data explorations through head-mounted displays (HMDs). Effective collaboration is built based on different strategies, including the ability to focus on one's own tasks, be aware of others' location, orientation and scale, as well as share data/insights when needed. However, for astronomical data exploration, it might be the case that abundant information is distributed at multiple levels of magnitude. Therefore, it is difficult to observe others' contextual situations and exchange insights efficiently due to the reason that collaborators' avatars may be too far or even in different scales. In this work, we use World-in-Miniature (WiM) to show the virtual environment at the level of each user. Based on that, we propose L-WiM, a novel interactive user interface for collaborative astronom-ical data exploration that links multiple WiMs. Through L-WiM, collaborators can see their contextual information at a glance, such as the scales, spatial locations, view directions as well as the sur-rounding environment. Users are supported to communicate via voice messages and visual cues after they select other users' WiMs.
Can Collaborative Learning Be Private, Robust and Scalable?

In federated learning for medical image analysis, the safety of the learning protocol is paramount. Such settings can often be compromised by adversaries that target either the private data used by the federation or the integrity of the model itself. This requires the medical imaging community to develop mechanisms to train collaborative models that are private and robust against adversarial data. In response to these challenges, we propose a practical open-source framework to study the effectiveness of combining differential privacy, model compression and adversarial training to improve the robustness of models against adversarial samples under train- and inference-time attacks. Using our framework, we achieve competitive model performance, a significant reduction in model’s size and an improved empirical adversarial robustness without a severe performance degradation, critical in medical image analysis.
Feature Norm-Based Deep Network for Multi-Domain Fashion Image Retrieval

Current fashion image searching technology based on fine-grained fashion recognition on fashion images has recently achieved great success in online shopping. However, this technique is limited to a single domain—real product images—and thus is inflexible. Recognition and search performance are degraded to a large extent when the distribution of the target data is different from the source training data. To improve the flexibility of fashion image retrieval, we propose multi-domain fashion image recognition in this work. We firstly established Fashion-DA, a large-scale fashion dataset comprising 14 fashion categories and a total of 13,435 images originating from three domains. Then, we propose an unsupervised domain adaption approach based on adaptive feature norm to handle data with different feature distributions. The experiment evaluated the effectiveness of the proposed method.
SISTEM IDENTIFIKASI DINI PENYAKIT STROKE DENGAN MENGGUNAKAN JARINGAN SYARAF TIRUAN PERAMBATAN BALIK

Heart disease is a disease with the second-highest mortality rate in the world. This happens because of an unhealthy human lifestyle. This unhealthy lifestyle affects the performance of the body's organs in carrying out their functions. Stroke can be prevented by exercising regularly, eating nutritious foods, not consuming alcohol, and not consuming tobacco. One way to find out if someone is free from stroke or not can be done by medical check-ups. However, this method is quite expensive. Given these problems, this study aims to design an early identification system for detecting early-stage stroke. The system is designed by utilizing the condition and history of the subject for identification. This study uses a back propagation neural network for the classification process. Variations in the use of hidden layers in each experiment were used to obtain the highest accuracy in the training process. From the results of the study, it was found that the system designed can detect early stroke with an accuracy rate of 97.8%.
Federated Learning Meets Edge Computing: A Hierarchical Aggregation Mechanism for Mobile Devices

Federated learning (FL) has been proposed and applied in edge computing scenarios. However, the complex edge environment of wireless networks, such as limited device computing resources and unstable signals, leads to increase communication overhead and reduced performance for federated learning. Therefore, we propose a hierarchical aggregation mechanism to improve federated learning performance in a resource-constrained wireless edge environment. We propose three feature models to quantify the FL performance and design a fuzzy $$\mathcal {K}$$ -means clustering mechanism. We construct an optimization problem for the process of hierarchical aggregation. And a cluster-based hierarchical federated learning algorithm (CluHFed) is designed, which consists of fuzzy clustering, asynchronous aggregation, and topology reconstruction. At last, we make an experiment with Pytorch. The results show that the proposed algorithm improves the accuracy by 2.6%–35.8% and reduces the latency of FL networks by 5.9% compared with other popular federated learning algorithms.
Automatic Sketch Generation Method Based on Text

Because ordinary people (non-artists) lack professional painting skills, they are often unable to make accurate sketches of visual perception or ideas. In the process of drawing, if there are missing parts of the object and distortion of the strokes, it will affect the further application of the hand-drawn sketch. On the contrary, natural language is the most commonly used and simpler way for people to describe the world. Therefore, we propose an end-to-end network SGBP-AttnGAN to obtain the mapping relationship from end-to-end text to scene level sketches. Aiming at the characteristics of the data modality of sketches, SGBP-AttnGAN mainly includes two parts: sketch multi-scale selection feature fusion network SGNet and sketch multi-mode fusion feature extraction network BPNet. Extensive experiments and comparisons with the original AttnGAN demonstrate that our method is more suitable for the given text description in terms of semantic content and visual perception.
The value of the humanitarian potential of physics for the formation of the professional identity in the framework of the competence-based approach

The article discusses the problems of humanization of physics education in order to develop professional and personal qualities. It is shown that within the framework of the competence-based approach, there is a shift in emphasis in physics education to professionally oriented training. The importance of the formation of future specialists' professional competence as a set of knowledge, abilities, skills and acquired experiences is emphasized. The special role of the competence in subject history as a type of professional competence of a physicist is noted. It is shown that the humanization of physics education is associated with the formation of the key competence of a specialist - the ability to renew his competencies. The most significant indicators of improving the quality of physics education in terms of revising its content are noted.
Research of User Experience of Video-sharing Apps Based on LDA Method - A Case Study of TikTok

With the widespread growth of social media, “user experience” has become an important issue in designing products. However, there are few studies in the field to discuss the user experience of the emerging industry: video-sharing apps. Based on the honeycomb model of user experience, this research takes 10,000 app reviews of TikTok in the App Store and uses the LDA method to conduct text analysis to explore the content and characteristics of five user experience indicators of video-sharing apps. The research found that users pay different attention to various aspects of user experience, and there are similarities and differences in the focus of user experience between social media apps and video-sharing apps. Enterprises of video-sharing apps should focus on each dimension of user experience, and design and improve products according to the specificity.
Frequent Closed Subgraph Mining: A Multi-thread Approach

Frequent subgraph mining (FSM) is an interesting research field and has attracted a lot of attention from many researchers in recent years, in which closed subgraph mining is a new topic with many practical applications. In the field of graph mining, GraMi (GRAph MIning) is considered state-of-the-art, and many algorithms have been developed based on the improvement of this approach. In 2021, we proposed the CloGraMi algorithm based on GraMi to mine closed frequent subgraphs from a large graph rapidly and efficiently. However, with NP time complexity and extremely high cost in terms of running time, graph mining is always a challenging problem for all researchers. In this paper, we propose a parallel processing strategy aiming to improve the execution speed of our CloGraMi algorithm. Our experiments on six datasets, including both undirected and directed graphs, with different sizes, including large, medium and small, show that the new algorithm significantly reduces running time and improves performance, and has better performance compared to the original algorithm.
Decision rules in survival analysis of patients with hematologic diseases

Analysis of patient survival is an extremely important element in medicine. It allows us to assess how individual patient characteristics and treatment used affect patient survival. Therefore, a proper and early diagnosis and proper treatment process is a very important aspect. Selected data mining methods can be helpful here to assist the doctor. In the present study, data of 187 pediatric patients with hematologic diseases were analyzed. The first step of the study was to select those patient characteristics and counting methods that had the greatest impact on survival. Then two models were built - containing all features and those obtained using feature selection and then compared their accuracy. The final step was decision rule extraction.
Discovering Deformable Keypoint Pyramids

The locations of objects and their associated landmark keypoints can serve as versatile and semantically meaningful image representations. In natural scenes, these keypoints are often hierarchically grouped into sets corresponding to coherently moving objects and their moveable and deformable parts. Motivated by this observation, we propose Keypoint Pyramids, an approach to exploit this property for discovering keypoints without explicit supervision. Keypoint Pyramids discovers multi-level keypoint hierarchies satisfying three desiderata: comprehensiveness of the overall keypoint representation, coarse-to-fine informativeness of individual hierarchy levels, and parent-child associations of keypoints across levels. On human pose and tabletop multi-object scenes, our experimental results show that Keypoint Pyramids jointly discovers object keypoints and their natural hierarchical groupings, with finer levels adding detail to coarser levels to more comprehensively represent the visual scene. Further, we show qualitatively and quantitatively that keypoints discovered by Keypoint Pyramids using its hierarchical prior bind more consistently, and are more predictive of manually annotated semantic keypoints, compared to prior flat keypoint discovery approaches. Code is at: https://github.com/jianingq/KeypointPyramids .
Learning Spatial-Temporal Graphs with Self-Attention Intensified Conditional Random Field for Video Person Re-identification

This paper extends the structural graph pooling scheme for video-based person re-identification (re-ID). A temporal-aware feature extractor first employs the short-term temporal correlation of the fine-grained feature maps to generate a set of multi-scale part-based CNN features. Subsequently, a spatio-temporal graph is constructed for these multi-scale part-based features. A structural graph pooling scheme is then used to extract graph features for video re-ID. Specifically, the structured graph pooling is formulated as a node clustering problem based on the structural relationships of the multi-scale part features, addressed by a novel self-attention intensified conditional random field (CRF). Different from the original structural graph pooling approach, CRF is integrated with self-attention to leverage the strength of both schemes to provide long-term structural dependencies. Thereby, it can deal with the deficiency of the existing graph-based approaches on video re-ID in learning the diverse temporal dependency of the multi-scale part features. This enables similar body part information corresponding to the person of interest to be aggregated to diminish the adverse effect of redundant and background information. Simulations on two benchmark datasets showcase the effectiveness of the new method.
Improving Vision Clarity and Object Detection Accuracy in Heavy Rain Base on Neural Network

In heavy rain situation, clarity of both human vision and computer vision are significantly reduced. Rain removal GAN network is developed to resolve this problem. However, we find that this kind of network causes the decreasing of detection accuracy. In this work, we analysis the performance and the propose a detection network to get higher accuracy. Through the comparison of experimental results, our method can improve the IOU accuracy and detect confidence.
Revisiting the Self-supervised Learning Method of Solving Jigsaw Puzzles

Spatial information is important for unsupervised feature learning. It has been proved by previous work that solving jigsaw puzzles as a pretext task can be used to train a convolutional neural network, which is capable of solving other visual tasks, such as image classification and object detection. A jigsaw puzzle solver can tell that an object is made of parts and what these parts are, thus the learned features can capture semantically relevant content. This work is inspiring and proposed a powerful learning mechanism, which outperformed the previous state of the art, as the pre-training method, for object detection and classification tasks on PASCAL VOC 2007. However, the original work still has several deficiencies, especially the evaluating scheme and lack of empirical ablation analysis. In this paper, we choose a more direct evaluating scheme and more appropriate datasets to conduct empirical ablation experiments for the self-supervised learning mechanism of solving jigsaw puzzles, and we also extend the learning mechanism to train auto-encoders for more general evaluation. We have explored how to build convolutional neural networks and auto-encoders by playing jigsaw puzzles with two types of network architecture (siamese-ennead network and straight-line network), resulting in four different jigsaw puzzle solvers. In the experiments, we evaluated the features learned by these solvers and discussed the influences of several training tricks on them. The results showed that solving jigsaw puzzles was very effective for unsupervised representation learning. The best performance of each solver all outperformed the state of the art on STL-10. Especially, one solver achieved 80.07% ± 0.08% classification accuracy, which is about 5.87% higher than the state of the art method on STL-10. This is a huge improvement and well worth reporting. Besides, we reported many empirical results on the influence of different training tricks and network configurations, which are very useful for the application and further research of the jigsaw puzzle solvers.
Three Post-phenomenological Design Projects on and with ML Technologies as Philosophy-in-Practice

In my doctoral research, I am concerned with making machine learning (ML) technologies accessible as a design material using post-phenomenological investigations. I use the latter framework because, on the one hand, it has become particularly widespread in HCI design research given its focus on technological mediation, or the shaping of subjectivity and objectivity, and according perceptions and actions in the world via technology. However, and on the other hand, the relationship between both approaches is reciprocal in my work. While post-phenomenology can inform design research, the former also requires assistance from the latter due to its own conceptual shortcomings regarding ML technologies. In this contribution, I focus on my work fusing post-phenomenology and design research as philosophy-in-practice to explicate conceptual vocabularies and provocative shorthands for approaching ML technologies as a design material.
An analysis of Politeness Strategies Used by Major Characters in Social Game : A study of Seventh Singularity Story of Fate/Grand Order Social Game

Politeness strategy is used in conversations to save addressee’s face by performing an expression that is less threatening for the addressee’s face. The aims of this research are to analyze politeness strategies used by two major characters of the seventh singularity story of Fate/Grand Order social game, and also to analyze the factors that influencing their choicess of politeness strategies. This research employed a descriptive qualitative method. The description of the data is in the form of utterances and examined based on Brown and Levinson theories of politeness strategy. Based on the obtained result, it can be concluded that the two major characters, Mash Kyrielight and Enkidu/Kingu applied four kinds of politeness strategy (bald on record, positive politeness, negative politeness and off record). Both characters mainly used positive politeness strategy, while the least used strategy is bald on-record. Their choice of politeness strategies mostly influenced by social distance factor, imposition and also the power factor.
Design of Improved DES Algorithm Based on Double Plaintext

The traditional symmetric encryption algorithm des relies on key encryption to provide security for data transmission, and is widely used in various fields at home and abroad. However, with the development of computer level, the key space of DES algorithm is insufficient. Now it can crack the key by exhaustive violence in a short time. Therefore, aiming at the disadvantage of short key of DES algorithm, this paper proposes an improved DES algorithm based on double plaintext. Based on the sixteen rounds of DES algorithm, the algorithm uses double plaintext for the seventeenth round of iterative encryption. The data after the sixteenth round of iteration is regarded as the ordinate, and the key of DES encryption is regarded as the abscissa. After the sixteen rounds of DES iteration through double plaintext, two pairs of ordinates and abscissa are obtained. The slope and intercept of the straight line passing through these two points are calculated as the final cipher text output. Simulation results show that compared with the traditional DES algorithm, the improved DES algorithm expands the key space and effectively enhances the security of data transmission.
Comparing Unsupervised Detection Algorithms for Audio Adversarial Examples

Recent works on automatic speech recognition (ASR) systems have shown that the underlying neural networks are vulnerable to so-called adversarial examples. In order to avoid these attacks, different defense mechanisms have been proposed. Most defense mechanisms discussed so far are based on supervised learning, which requires a lot of resources. In this research, we present and compare various unsupervised learning methods for the detection of audio adversarial examples (including autoencoder, VAE, OCSVM, and isolation forest), requiring no adversarial examples in the training data. Our experimental results show that some of the considered methods successfully defend against a simple adversarial attack, e.g., with isolation forest. Even in a more elaborate attack scenario that considers human psychoacoustics, we still achieve a high detection rate with the cost of slightly increased false positive rate, e.g., with an autoencoder. We expect our detailed analysis to be a helpful baseline for further research in the area of defense methods against audio adversarial examples.
Deep Quality Assessment of Compressed Videos: A Subjective and Objective Study

Video quality assessment is critical in optimizing video coding techniques. However, the state-of-the-art methods have limited performance, which is largely due to the lack of large-scale subjective databases for training. In this work, a semi-automatic labeling method is adopted to build a large-scale compressed video quality database, which allows us to label a large number of compressed videos with manageable human workload. The resulting Compressed Video quality database with Semi-Automatic Ratings (CVSAR), so far the largest of compressed video quality database. We train a no-reference compressed video quality assessment model with a 3D CNN for SpatioTemporal Feature Extraction and Evaluation (STFEE). Experimental results demonstrate that the proposed method outperforms state-of-the-art metrics and achieves promising generalization performance in cross-database tests. The CVSAR database has been made publicly available. It can be accessed at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/Rocknroll194/CVSAR</uri> .