Reviewerly/exHarmony huggingface dataset:
Surprising Effectiveness of Random Feature Embeddings in eXtreme Classification

The goal of eXtreme Multi-label Learning (XML) is to automatically annotate a given data point with the most relevant subset of labels from an extremely large vocabulary of labels (e.g., a million labels). Lately, many attempts have been made to address this problem that achieve reasonable performance on benchmark datasets. In this paper, rather than coming-up with an altogether new method, our objective is to present and validate a simple baseline for this task. Precisely, we investigate a global and structure preserving feature embedding technique using random projections whose learning phase is independent of training samples and label vocabulary. Further, we show how an ensemble of multiple such learners can be used to achieve further boost in prediction accuracy with only linear increase in training and prediction time. Experiments on three public XML benchmarks show that the proposed approach obtains competitive accuracy compared with many existing methods. Additionally, it also provides around 6572× speed-up ratio in terms of training time and around 14.7× reduction in model-size compared to the closest competitors on the largest public dataset. We have also shared our code for reproducibility.
Investigating the Relation Between Gender Expression of Mixed Reality Avatars and Sexuality of Male Users

Mixed reality environments are the next step in remote collaboration, yet there has been little attention towards avatar design for the en-joyability and efficiency of diverse users, particularly for the lesbian, gay, bisexual, transgender and queer (LGBTQ+) community. We believe that it is important to adapt the design of virtual avatars users collaborate with depending on not only the users gender but also their sexuality. In this study, we focus on examining the relationship between the sexual orientation of male users with the gender expression of virtual avatars, while they perform a collaborative task in mixed reality. We analyzed the perception of straight cisgender men and gay cisgender men in relation to the gender expression of the above mentioned agents and find differences that might affect design decisions for creating user-base specific avatars. We found that Female avatars tend to have a more positive perception when it comes to gay men. And for mixed reality, gay men also tend to have both negative and positive experiences more intensely than straight men.
Shape-preserving Swapping Autoencoder for Garment Texture Transfer

Garment image synthesis became possible since GAN has achieved great success in the field of image generation. However, the works in this area are limited. This paper is focused on solving the problem of swapping the texture of clothing images. Existing methods like SwappingAE and style transfer can not solve this problem well. A shape-preserving swapping autoencoder (SP-SwappingAE) is proposed to solve the clothing swapping problem. Comparing to SwappingAE, we proposed a condition discriminator to retain the structure of input images. To verify our proposed method, we collect a clothing dataset, named FCI, including 60,000 different types of upper garment images. Experiment results on FCI dataset showed that our proposed method beat SwappingAE and the style transfer algorithm. The high-resolution result shows that the swapped images have realistic texture and structure. In future works, we will explore more clothing design methods with artificial intelligence.
Application of Cooperative Script Learning Mode through Listening Story Method in Improving Listening Ability of Class V

The background of this research was to find out whether the Cooperative Script learning model experienced an improvement if it was applied to Indonesian language learning in the story listening skills of fifth grade students. a story. One of the efforts to improve students' listening skills is to apply the Cooperative Script learning model that uses discussion-based learning and the teacher is only a facilitator. This study uses a descriptive qualitative research design. The subjects of this study were all fifth grade students of Madrasah Ibtidaiyah Darussalam. Data collection techniques used in this study were observation, interviews and documentation. Data analysis uses data presentation and conclusion drawing. The results of this study indicate that the ability of students to listen to stories through the Cooperative Script method can be improved as seen in the activity and student learning outcomes. So this method is very effective to apply.
River and pools segmentation of Google Earth images using morphological gradient operator

In this paper presents a new procedure to segment the river and water pools in Google Earth images. The segmentation of the image is a process used in several fields, including the cartography science. The feature extraction is a task that needs to experience work, and high accuracy data to obtain successful results. The aims of this paper is to study Digital Image Processing, with emphasis on the mathematical gradient operators, to segmenting the high resolution image utilizing morphological gradient operators in three dimensions. The procedure implemented by MATLAB software was used color, high resolution image of the Google Earth, with the accomplishment the testing which has satisfy results without converting the color image to binary or the gray image.
A Privacy Preserving and Format-Checkable E-voting Scheme

Electronic voting (e-voting) is widely used because of its convenience and efficiency. In response to the security problems in e-voting, such as the legality of voters, privacy disclosure, etc., this paper proposes a novel e-voting scheme that can check the format of ballots without disclosing its content based on homomorphic encryption. Firstly, voters encrypt their ballots with Paillier encryption before sending them to the counter. Then, the counter decomposes the encrypted ballots using the proposed n-ary conversion protocol, and performs the format check of the ballots. Only ballots with the correct format are counted. During the whole process of voting, no one except the voter himself can know each ballot’s content, even the counter, so that the privacy of ballots is preserved. Finally, the counter performs an additive homomorphism operation on the encrypted ballots and the voting manager decrypts it to tally the result. Besides the requirements including the legality, privacy, and integrity, we furtherly consider the validity of the ballots in e-voting and make the scheme more practical than the existing methods.
Position prediction algorithm design for FIRA Simulation of 5VS5 Robot

Robot football simulation match is a kind of simulated football game with software. For the fira5vs5 robot simulation football project, many scholars have proposed the optimization of the team’s strategy on this project. On the basis of the dynamic role assignment of players in the game, this paper analyzes the principle of the least square method and the application process of the least square method, gives the algorithm design and obtains the final result. Platform verification and game results show that this strategy can greatly improve the goal rate of shooting players and achieve better shooting effect.
Digital Recognition of Breast Cancer Using TakhisisNet

In this chapter, the authors evaluate several basic image processing and advanced image pattern recognition techniques for automatically analyzing bioimages, with the aim of designing different ensembles of canonical and deep classifiers for breast lesion classification in ultrasound images. The analysis starts from convolutional neural networks (CNNs) in a square matrix that is used to feed other CNNs. The novel ensemble, named TakhisisNet, is the combination by sum rule of the whole set of the modified CNNs and the original one. Moreover, the performance of the system is further improved by combining it with some handcrafted features. Experimental results obtained on the well-known OASBUD breast cancer dataset (i.e., the open access series of breast ultrasonic data) and on a large set of bioimage classification problems show that TakhisisNet obtains very valuable results and outperforms other approaches previously tested in the same datasets.
Deep Multi-mode Learning for Book Spine Recognition

Traditional library book inventory or shelf-reading costs a tremendous human resource to ensure the reading accuracy of books. This paper provides a deep multi-mode learning solution for book spine recognition in the library with higher reliability and lower cost. Our book recognition method includes improved Hough Transformation in image segmentation for book spine, optical character recognition with deep learning to extract text information on book spine, and combined text matching to the final inventory. The experiment shows that we achieve good performance in the overall accuracy of book spine recognition and the average time per spine.
Computational experimental evaluation of the time variance of cryptocurrency mining using cryptographic hash functions

When trading cryptocurrencies, mining is the process of searching for a solution to a computationally difficult problem. Due to the nature of the search, the time required for mining is subject to fluctuations. This is undesirable from the standpoint of security. In order to reduce the variance of mining time, we implemented the multiple concatenation mining method proposed in existing research and evaluated its performance. Although the results differ from theoretical values, the effect of small variance was confirmed.
Semantic-Based Image Retrieval Using RS-Tree and Knowledge Graph

High-level semantic image retrieval is the problem which has applied in many different fields. In this paper, we approached a method of similarity image retrieval and image semantic extraction based on the combination of RS-Tree and knowledge graph. The main tasks include: (1) building a knowledge graph to store objects, attributes, and relationships of objects on images; (2) searching a set of similar images based on the low-level features stored in RS-Tree; (3) extracting a scene graph of image using Visual Genome dataset; (4) generating SPARQL query based on the scene graph to extract high-level semantic of the image from the built knowledge graph. In order to evaluate the efficiency as well as compare the accuracy of RS-Tree, the COREL and Wang image datasets are used. On the basis of the proposed method, a knowledge graph is built based on the Visual Genome dataset. The experimental results are compared with related works to demonstrate the effectiveness of the proposed method. Therefore, our proposed method is feasible in semantic-based image retrieval systems.
A Secure and Large-Scale Multiparty Conditional Payment Scheme based on Blockchain

Although artificial intelligence can enhance the value of big data and blockchain, it poses serious threats to personal privacy data and enterprise business secrets, making the problem of data island one of the biggest obstacles and bottlenecks restricting the development of blockchain. Blockchain, as a public distributed ledger technology, have particularly been deployed in some mature payment systems with widespread adoption. While the ever-growing network congestion, transaction costs and continuously dropping transaction rates limit its large-scale and widespread applications. To address these issues, in this paper we first propose two new definitions about an aggregated signature with deterability and a conditional anonymous multiparty payment scheme. Then the security requirements of the conditional anonymous multiparty payment scheme are described and a concrete anonymous multiparty payment scheme is constructed based on an aggregated signatures with deterability (ASE for short) and a conditional anonymous multiparty payment scheme with punishability (CAPP for short), whose security can be demonstrated under the security model. Finally, we compare the security of our scheme with other related works and make some evaluations to show the utility of our scheme.
RGCFace: Regularized Global Center loss for Deep Facial Expression Recognition

In the facial expression recognition task, we expect that the intra-class facial expression images could be distributed compactness while the inter-class ones could be distributed separability. However, the current loss functions of Convolutional Neural Network (CNN) cannot balance these two aspects obviously at the same time and they are weak in learning discriminative features. In this paper, a novel Regularized Global Center (RGC) loss is proposed to maximize the decision boundary in angular space directly instead of optimizing the inter/intra class distance, so as to enhance the discriminative ability of the deeply learned features by uniformly distributing the class centers on the feature space. In particular, a Normalization layer and a Scale layer (N-S) are introduced to make different features distributed on one hypersphere. Extensive experiments on three public facial expression datasets (CK+, MMI, and Oulu-CASIA) have proved that the proposed method outper-forms the comparable methods on the recognition accuracy.
A Demonstration of the Taxonomy of Functional Augmented Reality for Human-Robot Interaction

With the rising use of Augmented Reality (AR) technologies in Human-Robot Interaction (HRI), it is crucial that HRI research examines the role of AR in HRI to better define AR-HRI systems and identify potential areas for future research. A taxonomy for AR in HRI has recently been proposed for the field. However, it was limited to the definition of the framework, and exemplifying its use was missing. In this paper, we perform a demonstration of how the aforementioned taxonomy of AR in HRI can be used to analyse an existing AR-HRI system and come up with questions for alternative ways AR-HRI could be designed and further extended.
Erasure Decoding Scheme of Topological Fault Tolerance Quantum Computation With 3D Concatenation Codes

The possibility of practical large-scale quantum computation is largely dictated by the error threshold and qubit overhead of the error correcting scheme. To achieve this goal, architectures that perform fault tolerance quantum computation (FTQC) with 3D cluster states have been designed; however, the current benchmarks for such constructions are still below general experimental requirements. In light of this, we propose a new 3D FTQC scheme by exploiting geometric tolerance to erasure errors using cluster states with novel lattice structures, specifically, by replacing the physical qubits with small-sized error detection codes that allow simple applications of logical CZ gates. We are able to locate Pauli errors locally and effectively convert them into loss errors. With a combination of code concatenation and multi-staged decoding, the scheme is able to achieve higher performance than traditional cubic lattices in protecting quantum information. We have surveyed a wide range of possible concatenation schemes and Monte-Carlo simulations have been performed on the decoders to study their respective scaling behavior. The scheme has demonstrated an overall decrement in active qubit overhead and an improvement of the error threshold, which varies from 10 % to 160% depending on the noise model, indicating a relatively higher rate of fault tolerance compared with previous cluster states-based codes.
Multi-document Question Answering Powered by External Knowledge

Multi-document question answering is a hot-spot research task that retrieves answers for a natural language question from a set of documents. Although existing models try to enhance the recall ability, the performance of answering short questions is still far from satisfaction. In this paper, we address multi-document question answering by leveraging external knowledge to assist the semantic understanding, improving the overall performance. Specifically, we learn the relationship between questions and candidate paragraphs, and select valuable external knowledge. The machine reading comprehension model is trained to predict the answers in the screened external knowledge, which verifies the role of external knowledge in supplementing the answers. Then keywords are extracted to assist the indexing ability of answering short queries over multiple documents. Gate and Attention components are designed to integrate external knowledge through the deep neural network, assisting the retrieval of answers. We investigate the role of external knowledge in supplementing answers, assisting recall, and automatically assisting of deep neural network in the multi-document question answering system. Experimental results confirm the effectiveness of the proposed method.
SFVNet: Stable and Fast Network for Real-Time Video Semantic Segmentation

Real-time video semantic segmentation in dynamic scenes is a dense prediction task. This task is computationally expensive but has high requirements for speed and stability, especially on mobile devices. In this paper, a stable and fast real-time video semantic segmentation network (SFVNet) is presented to deal with the above problems. Each module of SFVNet can be designed or selected freely. SFVNet propagates temporal information and extracts spatial information through several lightweight modules instead of large general networks. The temporal features and spatial features are aggregated under the guidance of inter-frame discontiguous features. An end-to-end training is implemented with a joint loss function, including temporal consistency constraint and sparsely annotated video frame supervision. A new metric is proposed for more appropriate evaluation of video semantic segmentation. It takes both temporal consistency and semantic accuracy into account. Experiments on Cityscapes dataset and various devices demonstrate that SFVNet achieves faster speed and higher consistency with controllable accuracy degradation than the baseline methods. SFVNet shows state-of-the-art efficiency in real-time video semantic segmentation.
Indoor UAV Positioning System Based on Rotational Vision and Cascade Controller

In order to solve the problem of autonomous positioning of UAV indoors, this paper designs a indoor autonomous positioning system of UAV based on rotatable visual and cascade controller. The system consists of a vision positioning module, an operation processing unit and a flight control module. The said vision positioning module includes a servo, a camera and a laser. The operation processing unit controls the rotation of the monocular camera so that the target object appears in the center of the field of view, and transmits the corresponding rotation angle to the flight control module. After processing the altitude information and servo rotation information, the flight control module calculates the position of the UAV and compares it to the ideal position. At the same time, it transmits the position error into the cascade position controller, which makes the UAV fly with a certain speed and accuracy for correction. Finally, we designed an experiment of area positioning and high precision seeding based on a quadrotor platform to verify the feasibility of the proposed scheme.
Effects of Digital Avatar on Perceived Social Presence and Co-presence in Business Meetings Between the Managers and Their Co-workers

Due to the Covid-19 outbreak, more people in the workforce, especially in the IT industry, started working from home. This brought a set of issues and challenges for both workers and companies across the globe such as losing touch with other co-workers. This could potentially result in decrease of the performance and innovation. This paper investigates effects of using digital avatar robots in virtual meeting environment, specifically, focusing on the perception of social presence and co-presence between workers and their managers. Our experiment results showed that majority of participants felt an improvement in social presence, co-presence and overall virtual meeting experience while using digital avatar for their meetings, especially to those who has a meeting with less familiar persons or persons from the higher corporate hierarchy.
MemComputing vs. Quantum Computing: some analogies and major differences

Quantum computing employs some quantum phenomena to process information. It has been hailed as the future of computing but it is plagued by serious hurdles when it comes to its practical realization. MemComputing is a new paradigm that instead employs non-quantum dynamical systems and exploits time non-locality (memory) to compute. It can be efficiently emulated in software and its path towards hardware is more straightforward. I will discuss some analogies between these two computing paradigms, and the major differences that set them apart.
An Online Recurring Concept Meta-learning For Evolving Streams

Humans learning involves remembering patterns from the past to better understand recurring concepts as their knowledge grows. However, previous knowledge in deep neural networks could gradually forget when they are trained on a new concept. In this paper, we address this problem by learning a general representation that can be able to remember the previous information and promote the future learning. In this pursuit, a new controller is introduced by the meta-learning strategy that guides the network to keep balance between the previously learned concepts and the new concept, hence avoids catastrophic forgetting. In Online Recurring Concept Meta-Learning (ORCML), we propose a bi-level learning strategy, emphasizing the hidden representation learning of different concept drift in model-level learning, and obtaining a set of shared parameters through the global meta-learning strategy. Through extensive experiments, we demonstrate that our proposed framework has significant improvements over the state-of-art methods.
Sentiment Analysis of Chinese Microblog Comments –Comparison between SVM and LSTM

Text sentiment analysis is an important branch of natural language processing. This technology is widely used in public opinion analysis and web surfing recommendations. At present, the mainstream sentiment analysis methods include three parts: sentiment analysis based on a sentiment dictionary, based on traditional machine learning, and based on deep learning. This paper mainly analyzes and compares the advantages and disadvantages of the SVM method of traditional machine learning and the Long Short-term Memory (LSTM) method of deep learning in the field of Chinese sentiment analysis, using Chinese comments on Sina Microblog as the data set. Firstly, this paper classifies and adds labels to the original comment dataset obtained by the web crawler, and then uses Jieba word segmentation to classify the original dataset and remove stop words. After that, this paper extracts text feature vectors and builds document word vectors to facilitate the training of the model. Finally, SVM and LSTM models are trained respectively. After accuracy calculation, it can be obtained that the accuracy of the LSTM model is 85.80%, while the accuracy of SVM is 91.07%. But at the same time, LSTM operation only needs 2.57 seconds, SVM model needs 6.06 seconds. Therefore, this paper concludes that: compared with the SVM model, the LSTM model is worse in accuracy but faster in processing speed.