Reviewerly/exHarmony huggingface dataset:
Learning to Plan Optimally with Flow-based Motion Planner

Sampling-based motion planning is the predominant paradigm in many real-world robotic applications, but its performance is immensely dependent on the quality of the samples. The majority of traditional planners are inefficient as they use uninformative sampling distributions as opposed to exploiting structures and patterns in the problem to guide better sampling strategies. Moreover, most current learning-based planners are susceptible to posterior collapse or mode collapse due to the sparsity and highly varying nature of C-Space and motion plan configurations. In this work, we introduce a conditional normalising flow based distribution learned through previous experiences to improve sampling of these methods. Our distribution can be conditioned on the current problem instance to provide an informative prior for sampling configurations within promising regions. When we train our sampler with an expert planner, the resulting distribution is often near-optimal, and the planner can find a solution faster, with less invalid samples, and less initial cost. The normalising flow based distribution uses simple invertible transformations that are very computationally efficient, and our optimisation formulation explicitly avoids mode collapse in contrast to other existing learning-based planners. Finally, we provide a formulation and theoretical foundation to efficiently sample from the distribution; and demonstrate experimentally that, by using our normalising flow based distribution, a solution can be found faster, with less samples and better overall runtime performance.
A new learning algorithm based on strengthening boundary samples for convolutional neural networks

CNN is an artificial neural network that can automatically extract features with relatively few parameters, which is the advantage of CNN in image classification tasks. The purpose of this paper is to propose a new algorithm to improve the classification performance of CNN by strengthening boundary samples. The samples with predicted values near the classification boundary are recorded as hard samples. In this algorithm, the errors of hard samples are added as a penalty term of the original loss function. Multi-classification and binary classification experiments were performed using the MNIST data set and three sub-data sets of CIFAR-10, respectively. The experimental results prove that the accuracy of the new algorithm is improved in both binary classification and multi-classification problems.
Full Face-and-Head 3D Model With Photorealistic Texture

In the recent period, significant progress has been achieved towards reconstructing the 3D face model from face image. With the support of the render engines and sufficient data, the reconstruction results are fine in detail. Nevertheless, the research on the 3D face reconstruction with texture from a single unrestricted face image is imperfect. The rebuild process lacks essential structure and texture information in the profile and the craniofacial region. To address this problem, we present a method of creating a 3D full face-and-head model with photorealistic texture from a single “in-the-wild” face image in this paper. To this end, we introduce a pipeline to integrate the highly-detailed face model into the basic model. Specifically, the basic model was built by multilinear optimization, and the highly-detailed face model which represents the facial features generated by constrained illumination distribution. Additionally, to infer the invisible region texture information corresponding to the input face image, we design an effective architecture with the generative adversarial network (GAN) for panoramic UV texture generation. The final results after UV texture mapping were visualized in the experiment, which demonstrates that the model faithfully recovers the photorealistic details in arbitrary perspective. Furthermore, compared to the state-of-the-art facial modeling techniques and existing commercial solutions, our method takes less input and performs better in surface detail.
Cross-Lingual Dependency Parsing by POS-Guided Word Reordering

We propose a novel approach to cross-lingual dependency parsing based on word reordering.The words in each sentence of a source language corpus are rearranged to meet the word order in a target language under the guidance of a part-of-speech based language model (LM).To obtain the highest reordering score under the LM, a population-based optimization algorithm and its genetic operators are designed to deal with the combinatorial nature of such word reordering.A parser trained on the reordered corpus then can be used to parse sentences in the target language.We demonstrate through extensive experimentation that our approach achieves better or comparable results across 25 target languages (1.73% increase in average), and outperforms a baseline by a significant margin on the languages that are greatly different from the source one.For example, when transferring the English parser to Hindi and Latin, our approach outperforms the baseline by 15.3% and 6.7% respectively.
Cascaded Semantic and Positional Self-Attention Network for Document Classification

Transformers have shown great success in learning representations for language modelling.However, an open challenge still remains on how to systematically aggregate semantic information (word embedding) with positional (or temporal) information (word orders).In this work, we propose a new architecture to aggregate the two sources of information using cascaded semantic and positional self-attention network (CSPAN) in the context of document classification.The CSPAN uses a semantic self-attention layer cascaded with Bi-LSTM to process the semantic and positional information in a sequential manner, and then adaptively combine them together through a residual connection.Compared with commonly used positional encoding schemes, CSPAN can exploit the interaction between semantics and word positions in a more interpretable and adaptive manner, and the classification performance can be notably improved while simultaneously preserving a compact model size and high convergence rate.We evaluate the CSPAN model on several benchmark data sets for document classification with careful ablation studies, and demonstrate the encouraging results compared with state of the art.
Interstellar: Searching Recurrent Architecture for Knowledge Graph Embedding

Knowledge graph (KG) embedding is well-known in learning representations of KGs. Many models have been proposed to learn the interactions between entities and relations of the triplets. However, long-term information among multiple triplets is also important to KG. In this work, based on the relational paths, which are composed of a sequence of triplets, we define the Interstellar as a recurrent neural architecture search problem for the short-term and long-term information along the paths. First, we analyze the difficulty of using a unified model to work as the Interstellar. Then, we propose to search for recurrent architecture as the Interstellar for different KG tasks. A case study on synthetic data illustrates the importance of the defined search problem. Experiments on real datasets demonstrate the effectiveness of the searched models and the efficiency of the proposed hybrid-search algorithm.
Field-wise Learning for Multi-field Categorical Data

We propose a new method for learning with multi-field categorical data. Multi-field categorical data are usually collected over many heterogeneous groups. These groups can reflect in the categories under a field. The existing methods try to learn a universal model that fits all data, which is challenging and inevitably results in learning a complex model. In contrast, we propose a field-wise learning method leveraging the natural structure of data to learn simple yet efficient one-to-one field-focused models with appropriate constraints. In doing this, the models can be fitted to each category and thus can better capture the underlying differences in data. We present a model that utilizes linear models with variance and low-rank constraints, to help it generalize better and reduce the number of parameters. The model is also interpretable in a field-wise manner. As the dimensionality of multi-field categorical data can be very high, the models applied to such data are mostly over-parameterized. Our theoretical analysis can potentially explain the effect of over-parametrization on the generalization of our model. It also supports the variance constraints in the learning objective. The experiment results on two large-scale datasets show the superior performance of our model, the trend of the generalization error bound, and the interpretability of learning outcomes. Our code is available at this https URL.
Data Ownership Confirmation and Privacy-Free Search for Blockchain-Based Medical Data Sharing

With the development of data mining technology and the arrival of the era of big data, people can get more and more knowledge and information from the data. However, electronic medical data are stored in isolated data silos within each hospital due to the privacy and competition. Because of the complex interests of various groups, there is a lack of data flow between medical entities. On the one hand, this leads to difficulties for patients to obtain their own electronic medical data and to referral between different hospitals. On the other hand, it has led to serious lack of actual data for medical researchers. The realization of the determination of medical data ownership is the first condition for realizing medical data circulation. How to retrieve the required medical data while protecting privacy is the second problem to be solved. In this paper, we design the medical metadata as the smallest subunit of data ownership confirmation and propose a blockchain-based medical data sharing Framework MDSF, which can be used for electronic medical data ownership confirmation and data search while protecting patient privacy. We use the petri nets to verify the system and prove its reachability and boundedness, which means it can realize its designed function and operate safely. The system provides users with a convenient and safe way to obtain their past medical data, also enables research institutions engaged in medical data mining to easily search and obtain the actual medical data needed after desensitization. Thereby the circulation of medical data between different hospitals, individuals and research institutions is realized.
Subjective Sentiment Analysis for Arabic Newswire Comments

This paper presents an approach based on supervised machine learning methods to discriminate between positive, negative and neutral Arabic reviews in online newswire.The corpus is labeled for subjectivity and sentiment analysis (SSA) at the sentence-level.The model uses both count and TF-IDF representations and apply six machine learning algorithms; Multinomial Naïve Bayes, Support Vector Machines (SVM), Random Forest, Logistic Regression, Multi-layer perceptron and k-nearest neighbors using uni-grams, bi-grams features.With the goal of extracting users' sentiment from written text.Experimental results showed that n-gram features could substantially improve performance; and showed that the Multinomial Naïve Bayes approach is the most accurate in predicting topic polarity.Best results were achieved using count vectors trained by combination of word-based uni-gramsand bi-grams with an overall accuracy of 85.57% over two classes and 65.64% over three classes.
Counting points on genus-3 hyperelliptic curves with explicit real multiplication

We propose a Las Vegas probabilistic algorithm to compute the zeta function of a genus-3 hyperelliptic curve defined over a finite field $\mathbb F_q$, with explicit real multiplication by an order $\mathbb Z[η]$ in a totally real cubic field. Our main result states that this algorithm requires an expected number of $\widetilde O((\log q)^6)$ bit-operations, where the constant in the $\widetilde O()$ depends on the ring $\mathbb Z[η]$ and on the degrees of polynomials representing the endomorphism $η$. As a proof-of-concept, we compute the zeta function of a curve defined over a 64-bit prime field, with explicit real multiplication by $\mathbb Z[2\cos(2π/7)]$.
Fewer is More: A Deep Graph Metric Learning Perspective Using Fewer Proxies

Deep metric learning plays a key role in various machine learning tasks. Most of the previous works have been confined to sampling from a mini-batch, which cannot precisely characterize the global geometry of the embedding space. Although researchers have developed proxy- and classification-based methods to tackle the sampling issue, those methods inevitably incur a redundant computational cost. In this paper, we propose a novel Proxy-based deep Graph Metric Learning (ProxyGML) approach from the perspective of graph classification, which uses fewer proxies yet achieves better comprehensive performance. Specifically, multiple global proxies are leveraged to collectively approximate the original data points for each class. To efficiently capture local neighbor relationships, a small number of such proxies are adaptively selected to construct similarity subgraphs between these proxies and each data point. Further, we design a novel reverse label propagation algorithm, by which the neighbor relationships are adjusted according to ground-truth labels, so that a discriminative metric space can be learned during the process of subgraph classification. Extensive experiments carried out on widely-used CUB-200-2011, Cars196, and Stanford Online Products datasets demonstrate the superiority of the proposed ProxyGML over the state-of-the-art methods in terms of both effectiveness and efficiency. The source code is publicly available at this https URL.
High-Quality Many-to-Many Voice Conversion Using Transitive Star Generative Adversarial Networks with Adaptive Instance Normalization

This paper proposes a novel high-quality nonparallel many-to-many voice conversion method based on transitive star generative adversarial networks with adaptive instance normalization (Trans-StarGAN-VC with AdaIN). First, we improve the structure of generator with TransNets to make full use of hierarchical features associated with speech naturalness. In TransNets, many shortcut connections share hierarchical features between encoding and decoding part to capture sufficient linguistic and semantic information, which helps to provide natural sounding converted speech and accelerate the convergence of training process. Second, by incorporating AdaIN for style transfer, we enable the generator to learn sufficient speaker characteristic information directly from speech instead of using attribute labels, which also provides a promising framework for one-shot VC. Objective and subjective experiments with nonparallel training data show that our method significantly outperforms StarGAN-VC in both speech naturalness and speaker similarity. The mean values of mean opinion score (MOS) and ABX are increased by 24.5% and 10.7%, respectively. The comparison of spectrogram also shows that our method can provide more complete harmonic structures and details, and effectively bridge the gap between converted speech and target speech.
Research on Virtual Path Planning Based on Improved DQN

An end-to-end approach based on the theory of Deep Reinforcement Learning has been proven to be able to meet or exceed human-level strategic capabilities. Applying this learning algorithm to path planning methods can make robots self-contained learning ability and environment interaction ability, and increased generalization ability. In this paper, Deep Q Network (DQN) as the typical Deep Reinforcement Learning method is improved. Improvement points can be divided into two steps. Firstly, the two steps of the selection of actions in the current network and how to calculate the target Q value are decoupled to eliminate overestimation caused by the rapid optimization of Q value in the possible direction. Then, considering that the action value function can bring benefits in addition to the action with the greatest value made by the agent, the static environment also has certain influence, the final result is a linear combination of two parts, which is to estimate the value functions of the upper, lower, left and right actions of the neural network output and the value of the environment state itself. Under the same experimental conditions, the improved DQN network is compared with the original DQN network, the result shows that the estimated final target value function of improved DQN network is more accurate and effective for virtual path planning tasks.
New Work in Education and Teaching

According to UNESCO, the outbreak of COVID-19 has disrupted the education of 1.5 billion learners across the world, this is 91% of the world’s student population. In the university and college sector, institutions have been forced to close the doors to their campuses. Students studying practical courses have had to put their learning on hold. Those studying theoretical courses have had to shift to learning at home, with teaching moving online (IAU, 2020). This is a level of disruption the likes of which we have never seen before. Many scientists and experts believe that degree of online classes at universities and colleges will increase in the future even more. The questions is, what will the coronavirus pandemic mean for higher education in the coming years? For historical and functional reasons, universities are at odds with social developments. On the one hand, universities have a decisive influence on these developments; on the other hand, they are also shaped by them, but must not simply adapt to them. By generating and imparting knowledge, universities provide the chance that the problem horizons of the societies around them generated by everyday theory are exceeded. They do justice to their role as actors in the dialogue that builds on this, especially when they prospectively take up the foreseeable changes in social framework conditions. At present, modernization processes can already be observed or foreseen, which pose considerable challenges to the ability of societies and states to cope with social change. The challenges result from the fact that societies can only adapt to the upcoming changes through mobilization long unnecessary resources of conflict management, differentiation and innovation processing will succeed. The dimensions of these modernization processes are knowledge society, globalization, Europeanization, regionalization and demographic developments. The mobilization of appropriate coping resources requires democratization processes, changes in state tasks and developments in labour markets and work requirements. Figure 14.1 shows trends in the educational and teaching area. A trend towards greater privatization or partial privatization of education at all educational levels is an expression of education and training that is more focused on application and practical relevance, as well as the simultaneous limitation of state funding in Europe. Companies set up their own educational institutions or cooperate specifically with state educational institutions. Private schools of all levels are gaining popularity compared to state schools. Private financing shares are increasing. Financial stress is an issue in all systems of tertiary education. The answers to this are, on the one hand, the search for efficiency reserves, and on the other hand, the shifting of costs to the demand for education. A distinction must be made between financing the educational processes themselves and financing. At universities e.g. the educational process is 85% publicly funded and 15% privately; In the case of livelihood it is the other way round: 78% are financed privately and only 22% by the state.
Encoded Deep Features for Visual Place Recognition

In this work, a new VPR approach that uses the features extracted from a Convolutional Neural Network (CNN) architecture that will be encoded by the Fisher Vector (FV) is introduced. As the main aim of this work is to develop a robust approach that can meet real-life challenges, the deep features are encoded with FV, which as shown in the experiments section, can lead to getting more robust features. Our approach was evaluated using two classifiers, Dynamic Time Warping (DTW) and Support Vector Machine (SVM) in particular. Using both classifiers, the FV-based encoded features have outperformed the non-encoded features.
WITHDRAWN: Deep analysis of quality of primary studies on assessing the impact of refactoring on software quality

Software refactoring targets code smells to improve the software quality as well as developer’s productivity. Numerous research has been conducted to empirically examine the actual effect of refactoring on quality attributes. This study aims to demonstrate the application of modified quality assessment framework on such existing research works which were selected as primary studies (PSs) in a published systematic mapping study. The modified framework is executed on 142 empirical PSs to determine their quality on the basis of their design and conduct, analysis and conclusion. The acquired outcomes assisted us in figuring out the weakness of existing studies and also highlighting the important issues that needed to be addressed with higher priority while conducting future empirical studies.
A new inconsistent context fusion algorithm based on BP neural network and modified DST

As the number of various sensors grows fast in real applications such as smart city and intelligent agriculture, context-aware systems would acquire raw context information from dynamic, asynchronous and heterogeneous context providers, but multi-source information usually leads to the situation uncertainty of the system entities involved, which is harmful to appropriate services, and specially the inconsistency is a kind of main uncertainty problems and should be processed properly. A new inconsistent context fusion algorithm based on back propagation (BP) neural network and modified Dempster-Shafer theory (DST) combination rule is proposed in this paper to eliminate the inconsistency to the greatest extent and obtain accurate recognition results. Through the BP neural network, the situations of entities can be recognized effectively, and based on the modified combination rule, the recognition results can be fused legitimately and meaningfully. In order to verify the performance of the proposed algorithm, several experiments under different error rates of context information sources are conducted in the personal identity verification (PIV) application scenario. The experimental results show that the proposed BP neural network and modified DST based inconsistent context fusion algorithm can obtain good performance in most cases.