Reviewerly/exHarmony huggingface dataset:
Heuristic Q-learning based on experience replay for three-dimensional path planning of the unmanned aerial vehicle

In order to solve the problem that the existing reinforcement learning algorithm is difficult to converge due to the excessive state space of the three-dimensional path planning of the unmanned aerial vehicle, this article proposes a reinforcement learning algorithm based on the heuristic function and the maximum average reward value of the experience replay mechanism. The knowledge of track performance is introduced to construct heuristic function to guide the unmanned aerial vehicles' action selection and reduce the useless exploration. Experience replay mechanism based on maximum average reward increases the utilization rate of excellent samples and the convergence speed of the algorithm. The simulation results show that the proposed three-dimensional path planning algorithm has good learning efficiency, and the convergence speed and training performance are significantly improved.
Design and Implementation of CNN Custom Processor Based on RISC-V Architecture

With the rapid development of CNN(convolutional neural networks), the traditional CPU platform can not make full use of the parallelism of CNN. We decide to adopt a new and popular processor architecture: the risc-v architecture for experimental design. In this paper, a new convolutional neural network processor is designed based on risc-v architecture. The processor can take advantage of the parallelism of CNN and is more flexible. This paper completely designed a CNN processor based on the risc-v architecture. The processor uses a classic five-stage pipeline structure, and implements instruction buffer memory and data buffer memory, and adds peripherals such as FLASH, SRAM, and SDRAM. And, this paper designed custom instructions. Given the convolution operation frequently occurring in CNN, vector store instruction, vector load instruction, vector addition instruction, and convolution operation instruction are designed to accelerate the execution of the convolution process. The design has passed the simulation experiment. It can not only complete the general instructions but also run the custom instructions. The final simulation test verified the correctness of the design.
End-to-end Learning Method for Self-Driving Cars with Trajectory Recovery Using a Path-following Function

We propose an end-to-end learning method for autonomous driving systems in this article. End-to-end model estimates an appropriate motor command from raw sensory signals. End-to-end model for autonomous driving systems has recently been based on neural networks, which are popular for their good recognition ability. A common problem is how to return a car to the driving lane when the car goes off the track. In our research, we collect recovery data based on the distance from a desired track (the nearest waypoint link) during a road test with a simulator. To train the recovery behavior, instead of collecting human driving data, we use a path-following module (which means the car automatically drives on a pre-decided route using the car's current position). Our proposed method is divided into three phases. In phase 1, we collect data only using a path-following module during 100 laps of driving. In phase 2, we generate driving behavior using a neural driving module trained by the data collected in phase 1. This includes switching between the accelerator, brake and steering based on a threshold. We collect further data on the recovery behavior using the path-following module during 100 laps of driving. In phase 3, we generate driving behavior using the neural driving module trained by the data collected in phases 1 and 2. To assess the proposed method, we compared the average distance from the nearest waypoint link and the average distance traveled per lap for datasets with no recovery, for datasets with random recovery, and for datasets for the proposed method with recovery. A model based on the proposed method drove well and paid more attention to the road rather than the sky and other unrelated objects automatically for both untrained and trained courses and weather.
Cognitive and Applied Linguistics Aspects of Using Social Media: The Impact of the Use of Facebook on Developing Writing Skills in Learning English as a Foreign Language

Facebook is undoubtedly the most popular social network site nowadays with over two billion users. Therefore, there has been research exploring its potential as a learning environment for various fields of education, including learning English as a foreign language (EFL). As the findings of research studies indicate, Facebook is especially used in developing productive language skills, such as writing, which is considered to be the least popular and the most difficult skill to master. Thus, the purpose of this article is to explore the impact of Facebook on developing writing skills in learning English as a foreign language. The methodology is based on the search for available studies on the research topic, i.e., the impact of Facebook on developing writing skills in EFL, in the world’s databases: Web of Science and Scopus. The search was not limited by any time period. The results of this review article reveal that using Facebook has a positive effect on developing writing skills in EFL classes, especially in shaping and organizing ideas, enhancing motivation, developing and supporting collaboration among peers, improving vocabulary, and reducing students’ shyness. However, more experimental research should be done in this area to reveal other aspects of the writing process, such as communicative language competence, which can be improved by using Facebook, as well as address researchers from continents other than Asia.
TA-BLSTM: Tag Attention-based Bidirectional Long Short-Term Memory for Service Recommendation in Mashup Creation

The service-oriented architecture makes it possible for developers to create value-added Mashup applications by composing multiple available Web services. Due to the overwhelming number of Web services online, it is often hard and time-consuming for developers to find their desired ones from the entire service repository. In the past, various approaches aim at recommending Web services for automatic Mashup creation have been proposed, i.e., TFIDF, collaborative filtering and topic model-based methods, which rely on the original service descriptions given by service providers. However, most traditional methods fail to capture the function-related features of services since words contained in service descriptions usually correspond to different intent aspects (e.g., functional and non-functional related). To tackle this problem, we propose a tag attention-based recurrent neural networks model for Web service recommendation. The model consists of two Siamese bidirectional Long Short-Term Memory (LSTM) networks, which jointly learn two embeddings representing the functional features of Web services and the functional requirements of Mashups. In addition, by considering the tags of services as functional context information, the model can learn to assign attention scores to different words in service descriptions according to their intent importance, thus words used to reveal the functional properties of Web service will be given special attention. We compare our approach with the state-of-the-art methods (e.g., RTM, Word2vec, etc.) on a real-world dataset crawled from ProgrammableWeb, and the experimental results demonstrate the effectiveness of the proposed model.
A Hybrid Character Representation for Chinese Event Detection

For the Chinese language, event triggers in a sentence may appear inside or across words after word segmentation. Thus recent works on Chinese event detection often formulate the task as a character-wise sequence labeling problem instead of a word-wise one. Due to a limited amount of corpus, however, it is more difficult in practice to train character-wise models to capture the inner structure of event triggers and the semantics of sentence-level context compared with word-wise ones. In this paper, we propose to improve character-wise models by incorporating word information and language model representation into Chinese character representation. More specifically, the former consists of the position of the character inside a word and the word's embedding, which can aid structural pattern learning; the latter is obtained by BERT, which contains long-distance semantic information. We construct a sequence tagging model equipped with the hybrid representation and evaluate our model on ACE 2005 Chinese corpus. Experiment results show that both word information and language model representation are effective enhancements, and our model gains an increase of 4.5 (6.5%) and 6.1 (9.4%) in F1-score in event trigger identification task and classification task respectively over the state-of-the-art method.
Regularization and Iterative Initialization of Softmax for Fast Training of Convolutional Neural Networks

A softmax regularizer is proposed, a simple and elegant constraint on softmax weight distribution in the training process. Since the direct estimation of feature centers is neither memory efficient nor robust, the proposed regularizer utilizes the relations between feature centers and the classifier weights by adding constraints on the distances between softmax weight vectors. This apparently enlarges the distances between softmax weights to benefit the separation of different classes, and provides extra gradients for the optimization of softmax in order to speed up the training process.Furthermore, we argue that the massive amount of softmax parameters is the main cause that makes the network converge slowly, especial in the class classification tasks with large class number such as face recognition. Motivated by the analysis of the relations between deep features and softmax weights, a fast training process is presented, which splits the training into multiple stages and alternates training and initializing softmax weights for fast convergence when the class number is large. Since the softmax weights can be initialized with estimated deep feature centers, the scale of training data can be gradually increased along the stages. By this procedure, the total training computation cost can be reduced. To validate its effectiveness, our approach is applied on both face recognition and image classification tasks. It obtains comparable performance with the state-of-the-art methods while boasting a faster training process.
An Efficient and Dynamic Semantic-Aware Multikeyword Ranked Search Scheme Over Encrypted Cloud Data

Traditional searchable encryption schemes adopting the bag-of-words model occupy massive space to store the document set's index, where the dimension of the document vector is equal to the scale of the dictionary. The bag-of-words model also ignores the semantic information between keywords and documents, which could return non-relevant search results to users. The neutral-network based natural language processing method - Doc2Vec model use word's and paragraph's context information to extract documents' features. The features contain latent semantics information and can measure the similarity between documents. In this paper, we adopt the Doc2Vec model to achieve a semantic-aware multikeyword ranked search scheme. Doc2Vec model uses the distributed representation of words and documents with a modest dimensionality of vectors while trained on a dataset with a few hundred of millions of words. Documents' distributed representations are extracted as documents feature vector by Doc2Vec model and utilized as the search index. The features of the queried keywords are also extracted as the query feature vector, and the secure inner product operation is adopted to achieve privacy-preserving semantic search with the query feature vector and index. Our scheme can support dynamic update on the document set with Doc2Vec model. The experiment on a real-world dataset shows that the fixed-length feature vector can improve the time and space efficiency on the semantic-aware search.
Multiple Fake Classes GAN for Data Augmentation in Face Image Dataset

Class-imbalanced datasets often contain one or more class that are under-represented in a dataset.In such a situation, learning algorithms are often biased toward the majority class instances.Therefore, some modification to the learning algorithm or the data itself is required before attempting a classification task.Data augmentation is one common approach used to improve the presence of the minority class instances and rebalance the dataset.However, simple augmentation techniques such as applying some affine transformation to the data, may not be sufficient in extreme cases, and often do not capture the variance present in the dataset.In this paper, we propose a new approach to generate more samples from minority class instances based on Generative Adversarial Neural Networks (GAN).We introduce a new Multiple Fake Class Generative Adversarial Networks (MFC-GAN) and generate additional samples to rebalance the dataset.We show that by introducing multiple fake class and oversampling, the model can generate the required minority samples.We evaluate our model on face generation task from attributes using a reduced number of samples in the minority class.Results obtained showed that MFC-GAN produces plausible minority samples that improve the classification performance compared with state-of-the-art AC-GAN generated samples.
A Window-Based Self-Attention approach for sentence encoding

Recently, much progress has been made in the representation of sentences for the task of natural language processing (NLP). Most of existing methods utilize deep learning methods with RNN/CNN to capture contextual information. These models typically treat each word in a sentence equally, which ignores the fact that keywords always play a leading role in expressing the sentence semantic. Especially, on the tasks of sentence classification and semantic relatedness prediction, we can judge through the information of several keywords, no need for the whole sentence. To this end, we propose a window-based intra-weighing approach to weigh words in the sentence. In calculating attentive weights, we take as input multi-window n-grams and use max-pooling for feature extraction. To evaluate our model, we conduct experiments on 6 benchmark data sets. The experimental results demonstrate the superiority of our model in calculating word importance. Although our model has fewer parameters and much lower computational complexity than state-of-the-art models, it achieves comparable results with them.
A Distributed Approach for High-Dimensionality Heterogeneous Data Reduction

The recent explosion of data size in number of records and attributes has triggered the development of a number of Big Data analytics as well as parallel data processing methods and algorithms. At the same time though, it has pushed for usage of data Dimensionality Reduction (DR) procedures. Indeed, more is not always better. Large amounts of data might sometimes produce worse performance in data analytics applications, and this may be caused by the presence of missing data. These latter are a common occurrence and can have a significant effect on the conclusions that can be drawn from the data. In this work, we propose a new distributed statistical approach for high-dimensionality reduction of heterogeneous data that is based on the MapReduce paradigm, limits the curse of dimensionality and deals with missing values. To handle these latter, we propose to use the Random Forest imputation's method. The main purpose here is to extract useful information and reduce the search space to facilitate the data exploration process. Several illustrative numeric examples using data coming from publicly available machine learning repositories are also included. The experimental component of the study shows the efficiency of the proposed analytical approach.
Adversarial Framework for Unsupervised Learning of Motion Dynamics in Videos

Human behavior understanding in videos is a complex, still unsolved problem and requires to accurately model motion at both the local (pixel-wise dense prediction) and global (aggregation of motion cues) levels. Current approaches based on supervised learning require large amounts of annotated data, whose scarce availability is one of the main limiting factors to the development of general solutions. Unsupervised learning can instead leverage the vast amount of videos available on the web and it is a promising solution for overcoming the existing limitations. In this paper, we propose an adversarial GAN-based framework that learns video representations and dynamics through a self-supervision mechanism in order to perform dense and global prediction in videos. Our approach synthesizes videos by (1) factorizing the process into the generation of static visual content and motion, (2) learning a suitable representation of a motion latent space in order to enforce spatio-temporal coherency of object trajectories, and (3) incorporating motion estimation and pixel-wise dense prediction into the training procedure. Self-supervision is enforced by using motion masks produced by the generator, as a co-product of its generation process, to supervise the discriminator network in performing dense prediction. Performance evaluation, carried out on standard benchmarks, shows that our approach is able to learn, in an unsupervised way, both local and global video dynamics. The learned representations, then, support the training of video object segmentation methods with sensibly less (about 50%) annotations, giving performance comparable to the state of the art. Furthermore, the proposed method achieves promising performance in generating realistic videos, outperforming state-of-the-art approaches especially on motion-related metrics.
Visual Saliency Detection via Kernelized Subspace Ranking With Active Learning

Saliency detection task has witnessed a booming interest for years, due to the growth of the computer vision community. In this paper, we introduce a new saliency model that performs active learning with kernelized subspace ranker (KSR) referred to as KSR-AL. This pool-based active learning algorithm ranks the informativeness of unlabeled data by considering both uncertainty sampling and information density, thereby minimizing the cost of labeling. The informative images are selected to train the KSR iteratively and incrementally. The learning model of this algorithm is designed on object-level proposals and region-based convolutional neural network (R-CNN) features, by jointly learning a Rank-SVM classifier and a subspace projection. When the active learning process meets its stopping criteria, the saliency map of each image is generated by a weight fusion of its top-ranked proposals, whose ranking scores are graded by the learned ranker. We show that the KSR-AL achieves a reduction in annotation, as well as improvement in performance, compared with the supervised learning scheme. Besides, the proposed algorithm also outperforms the state-of-the-art methods. These improvements are demonstrated by extensive experiments on six publicly available benchmark datasets.
Analysis of Users' Keyboard Handwriting based on Gaussian Reference Signals

This article analyzes the methods of keyboard users handwriting by using standard Gaussian signals. To construct the access control systems, the authors actualize the use of identification and authentication biometric methods. The detailed review of the analysis methods of handwriting keyboard users is provided. Further, the paper analyzes the research of famous Russian scientists in this field. We present and analyze statistical analyzer circuit keyboard handwriting on the basis of standard Gaussian signals. The density distribution graph of slots pressing and releasing the user key is provided as an example. The conclusion is further drawn about the possibility of the effective use of the methods considered in the identification and authentication systems.
Unpaired Synthetic Image Generation in Radiology Using GANs

In this work, we investigate approaches to generating synthetic Computed Tomography (CT) images from the real Magnetic Resonance Imaging (MRI) data. Generating the radiological scans has grown in popularity in the recent years due to its promise to enable single-modality radiotherapy planning in clinical oncology, where the co-registration of the radiological modalities is cumbersome. We rely on the Generative Adversarial Network (GAN) models with cycle consistency which permit unpaired image-to-image translation between the modalities. We also introduce the perceptual loss function term and the coordinate convolutional layer to further enhance the quality of translated images. The Unsharp masking and the Super-Resolution GAN (SRGAN) were considered to improve the quality of synthetic images. The proposed architectures were trained on the unpaired MRI-CT data and then evaluated on the paired brain dataset. The resulting CT scans were generated with the mean absolute error (MAE), the peak signal-to-noise ratio (PSNR) and the structural similarity (SSIM) scores of 60.83 HU, 17.21 dB, and 0.8, respectively. DualGAN with perceptual loss function term and coordinate convolutional layer proved to perform best. The MRI-CT translation approach holds potential to eliminate the need for the patients to undergo both examinations and to be clinically accepted as a new tool for radiotherapy planning.
Blockchain and Its Potential Applications in Food Supply Chain Management in Ecuador

In recent years, attention has been directed to the problem of food loss and waste. On the one hand, there is an increasing demand for higher quality fresh produce and products. On the other hand, studies suggest that at least one-third of food production is lost along its supply chain. The most crucial part is constituted by suppliers-retailers-consumers, since it presents the highest percentages of loss or waste. Several works in the literature suggest that the exchange of information is one of the most important means to reduce waste. Shared information can improve decisions regarding the quantity in supplier's orders and vendor's inventory allocation among retailers. Based on these needs, the Blockchain technology, developed in recent years to generate secure transactions on different sectors, has the characteristics of decentralization, information security, and reliability. If this technology can be applied as an underlying basis of a supply chain, it could improve exchanges of information and products among all parts of the system. In this context, the current article presents a systematic review on Blockchain in food supply chain management (FSCM) in Ecuador, its main contributions, and potential benefits. Findings indicate that Blockchain in FSCM is a recent research area whose importance is rapidly growing. However, little is known about Ecuadorian studies applying Blockchain. Additionally, no studies have reported the combination of Blockchain and Internet of Things technologies, one of the most prominent and complementary applications of Blockchain.
Optimizing and evaluating transient gradual typing

Gradual typing enables programmers to combine static and dynamic typing in the same language. However, ensuring sound interaction between the static and dynamic parts can incur runtime cost. In this paper, we analyze the performance of the transient design for gradual typing in Reticulated Python, a gradually typed variant of Python. This approach inserts lightweight checks throughout a program rather than installing proxies on higher order values. We show that, when using CPython as a host, performance decreases as programs evolve from dynamic to static types, up to a 6x slowdown compared to equivalent Python programs.