Reviewerly/exHarmony huggingface dataset:
An improved TextRank keywords extraction algorithm

Keywords extraction is widely used in the field of natural language processing. How to quickly and accurately extract keywords has become the key issue in text processing. At present, there are many methods for keywords extraction, but the accuracy and versatility of the method still have much room for improvement. Thus, an improved TextRank keywords extraction algorithm is proposed in this paper. The algorithm uses the TF-IDF algorithm and the average information entropy algorithm to calculate the importance of words, and then calculates the comprehensive weight of words based on the calculation results in the text. The initial weight of the TextRank algorithm node and the node probability transfer matrix are improved by using the comprehensive weight of words, and the weights of all nodes are iteratively calculated until convergence. The weights of the nodes are sorted to obtain the weight information of the words, then the top N words are selected as the keywords.
Pre-Impact Fall Detection Using 3D Convolutional Neural Network

Early fall detection is an important issue during gait rehabilitation training. This paper proposes an approach for pre-impact fall detection during gait rehabilitation training based on a 3D convolutional neural network (CNN). Firstly, pre-training data is collected and used to pre-train the 3D CNN to differentiate between a normal walking and a fall based on their general spatio-temporal patterns. Secondly, fine-tuning data is created by combining the pre-training data with a 3second normal walking sample collected from a new trainee whose falls are to be detected. The pre-trained 3D CNN is further fine-tuned by the fine-tuning data to learn the spatiotemporal patterns of the new trainee. Finally, a temporal sliding window is used to feed video snippets into the fine-tuned 3D CNN for fall detection. To the best of our knowledge, this is the first pre-impact fall detection approach based on a 3D CNN using RGB images. Moreover, the training strategy used to train the 3D CNN can alleviate the generalization issue of the 3D CNN when only limited training data is available in gait rehabilitation training. With 225 testing trials from 5 trainees, the proposed pre-impact fall detection approach achieves a detection accuracy of 100% within 0.5 second after falls start. Experiment results show that this approach is efficient, accurate, and practical in achieving intelligent fall detection during gait rehabilitation training.
Adaptive Learning Knowledge Networks for Few-Shot Learning

In recent years, relying on training with thousands of labeled samples, deep learning has achieved remarkable success in the field of computer vision.However, in practice, annotating samples is a time-consuming and laborious task, which means that it is impractical to obtain thousands of labeled data.Humans have the ability to learn the knowledge of new concepts from only a handful of examples, which makes it easier to adapt to new environments.Inspired by this ability of human beings, few-shot learning aims at training a classifier that can learn to recognize new classes when only given a few labeled samples of these classes.In this paper, we propose a new framework called Adaptive Learning Knowledge Networks (ALKN) for few-shot learning.ALKN learns the knowledge of different classes from the features of labeled samples and store the learned knowledge into memory which will be dynamically updated during the learning process.We define the difficult knowledge and easy knowledge of each class so that when performing inference, our model can holistically leverage the memory of learned knowledge more efficiently.Considering the situations of standard few-shot learning and semi-supervised few-shot learning, we design different update strategies for the memory of learned knowledge.Extensive experiments are conducted on three datasets, Omniglot, Mini-Imagenet and CUB.Compared with the most existing approaches, our ALKN achieves superior results on those benchmarks.
Differential Evolutionary Multi-task Optimization

Evolutionary multi-task optimization (EMTO) studies on how to simultaneously solve multiple optimization problems, so-called component problems, via evolutionary algorithms, which has drawn much attention in the field of evolutionary computation. Knowledge transfer across multiple optimization problems (being solved) is the key to make EMTO to outperform traditional optimization paradigms. In this work, we propose a simple and effective knowledge transfer strategy which utilizes the best solution found so far for one problem to assist in solving the other problems during the optimization process. This strategy is based on random replacement. It does not introduce extra computational cost in terms of objective function evaluations for solving each component problem. However, it helps to improve optimization effectiveness and efficiency, compared to solving each component problem in a standalone way. This light-weight knowledge transfer strategy is implemented via differential evolution within a multi-population based EMTO paradigm, leading to a differential evolutionary multi-task optimization (DEMTO) algorithm. Experiments are conducted on the CEC'2017 competition test bed to compare the proposed DEMTO algorithm with five state-of-the-art EMTO algorithms, which demonstrate the superiority of DEMTO.
Finding key classes in object-oriented software systems by techniques based on static analysis

Software maintenance is burdened by program comprehension activities which consume a big part of project resources. Program comprehension is difficult because the code to be analyzed is very large and the documentation may not be well structured to help navigating through the code. Tools should support the early stages of program comprehension. Our goal is to build tools that analyze the code and filter this large amount of information such that only the most important information is presented to the software maintenance team. In the case of object-oriented systems, finding the important information means finding the most important classes, also called the key classes of the system. In this work, we formulate and explore several hypotheses regarding which are the class attributes that characterize important classes. By class attributes, we understand here different metrics that quantify properties of the class such as its connections and relationships with other classes. All the necessary input data for computing class attributes are extracted from code by static analysis. We experimentally investigate which attributes are best suited to rank classes according to their importance, doing an extensive empirical study on fifteen software systems. Attributes from the categories of direct connections and network centrality are the best for finding key classes. We identified three class attributes which are best as class ranking criteria: PR-U2-W and CONN-TOTAL-W when the target set of key classes is small and CONN-TOTAL when the target set has a large and variable size. We show that the method of ranking classes based on these attributes outperforms known related work approaches of finding key classes. Our method allows us to build easy-to-use fully automatic tools which find almost instantly the key classes of a software system starting from its code.
Translate and Label! An Encoder-Decoder Approach for Cross-lingual Semantic Role Labeling

We propose a Cross-lingual Encoder-Decoder model that simultaneously translates and generates sentences with Semantic Role Labeling annotations in a resource-poor target language.Unlike annotation projection techniques, our model does not need parallel data during inference time.Our approach can be applied in monolingual, multilingual and cross-lingual settings and is able to produce dependencybased and span-based SRL annotations.We benchmark the labeling performance of our model in different monolingual and multilingual settings using well-known SRL datasets.We then train our model in a cross-lingual setting to generate new SRL labeled data.Finally, we measure the effectiveness of our method by using the generated data to augment the training basis for resource-poor languages and perform manual evaluation to show that it produces high-quality sentences and assigns accurate semantic role annotations.Our proposed architecture offers a flexible method for leveraging SRL data in multiple languages.
Robust weighted SVD-type latent factor models for rating prediction

Recommending system is a popular tool in many commercial or social platforms which finds interesting products for users based on their preference history. Predicting the ratings of items, such as movies, plays an essential role in the recommending system. In this context, we develop a new type of latent factor models by attaching weights to the entries of the incomplete ratings matrix. The weights are computed after estimating the user/item mean errors caused by the basic SVD model under the low-rank assumption on the ratings matrix. To accelerate the optimization process of our proposed models and other existing SVD-type models, a special design of the initial guess is suggested. In the experiments on real-world datasets, the proposed weighted models outperform other SVD-type methods, and the usage of the special initial guess improves the optimization significantly, obtaining lower MRSEs within fixed number of iterations, in comparison with the random initial guess. Furthermore, artificially noised datasets are taken to evaluate the methods, where the weighted models still perform better than other SVD-type models, implying their effectiveness and robustness in noised environment.
Eye Gaze-based Early Intent Prediction Utilizing CNN-LSTM

In assistive technologies designed for patients with extremely limited motor or communication capabilities, it is of significant importance to accurately predict the intention of the user, in a timely manner. This paper presents a new framework for the early prediction of the user's intent via their eye gaze. The seen objects in the displayed images, and the order of their selection are identified from the spatial and temporal information of the gaze. By employing a combination of convolution neuronal network (CNN) and long short term memory (LSTM), early prediction of the user's intention is enabled. The proposed framework is tested using experimental data obtained from eight subjects. Results demonstrate an average accuracy of 82.27% across all considered intended tasks for early prediction, confirming the effectiveness of the proposed method.
Local Region Proposing for Frame-Based Vehicle Detection in Satellite Videos

Current new developments in remote sensing imagery enable satellites to capture videos from space. These satellite videos record the motion of vehicles over a vast territory, offering significant advantages in traffic monitoring systems over ground-based systems. However, detecting vehicles in satellite videos are challenged by the low spatial resolution and the low contrast in each video frame. The vehicles in these videos are small, and most of them are blurred into their background regions. While region proposals are often generated for efficient target detection, they have limited performance on satellite videos. To meet this challenge, we propose a Local Region Proposing approach (LRP) with three steps in this study. A video frame is segmented into semantic regions first and possible targets are then detected in these coarse scale regions. A discrete Histogram Mixture Model (HistMM) is proposed in the third step to narrow down the region proposals by quantifying their likelihoods towards the target category, where the training is conducted on positive samples only. Experiment results demonstrate that LRP generates region proposals with improved target recall rates. When a slim Fast-RCNN detector is applied, LRP achieves better detection performance over the state-of-the-art approaches tested.
A game engine to make games as multi-agent systems

Video games are applications that present design patterns that resemble multi-agent systems. Game objects or actors are like autonomous agents that interact with each other to describe complex systems. The purpose of this work is to develop a game engine to build games as multi-agent systems. The actors or game engine agents have a set of properties and behaviour rules with the end to interact with the environment of the game. The behaviour definition is established through a formal semantic based on predicate logic. The proposed engine tries to fulfil the basic requirements of the multi-agent systems, by adjusting the characteristics of the system, without affecting its potential. Finally, a set of games are introduced to validate the operation and possibilities of the engine.
News literacy and fake news curriculum: School librarians’ perceptions of pedagogical practices

The high profile of fake news reveals underlying trends in the production and consumption of news.While news literacy is a lifelong skill, the logical time to start teaching such literacy is in K-12 educational settings, so that all people have the opportunity to learn and practice news literacy.School librarians can play a critical role in helping students gain news literacy competence.This study investigated the needs for K-12 students to be news literate and their current level of skills as perceived by in-service teachers and school librarians in California.Respondents thought that their students were most competent at distinguishing advertisements and least proficient at discerning the trustworthiness of photographs.Concurrently, news literacy was seldom integrated systematically into the curriculum.The findings supported the need for developing news media literacy curriculum, including visual and media literacy, that could be implemented by K-12 teachers and school librarians.
DistTC: High Performance Distributed Triangle Counting

We describe a novel multi-machine multi-GPU implementation of triangle counting which exploits a novel application-agnostic graph partitioning strategy that eliminates almost all inter-host communication during triangle counting. Experimental results show that this distributed triangle counting implementation can handle very large graphs such as clueweb12, which has almost one billion vertices and 37 billion edges, and it is up to 1.6× faster than TriCore, the 2018 Graph Challenge champion.
Deep Learning Based Irradiance Mapping Model for Solar PV Power Forecasting Using Sky Image

The entrenched instability of solar power output throttles its further integration into power grids worldwide. Thus the precise solar power forecasting (SPF) is helpful for the improvement of power grid stability and better exploitation of clean solar energy. As an important role of ultra-short-term SPF, sky images always contain volatile clouds, which results in tempestuous variation of the output of PV plants. Therefore, an accurate model that can capture the mapping relationship between sky image data and solar irradiance data is significant for fulfilling the ultra-short-term SPF. To fill the gap in the content of this research field, this paper proposes two end to end irradiance mapping models based on deep learning technologies, namely convolutional neural network (CNN) and long short-term memory (LSTM) neural network. Then the mapping performance of the above two mapping models is compared to that of traditional artificial neural network (ANN) based model. In all the aforementioned models, it should be noted that the solar irradiance data output by mapping models is in one-to-one correspondence with the input sky image data in time. The deterministic and probability methods are applied to statistically evaluate the mapping result of CNN and LSTM models. Our case study shows that deep learning architectures, especially the CNN based model, are good at mapping sky images to corresponding surface solar irradiance.
Understanding Neural Machine Translation by Simplification: The Case of Encoder-free Models

In this paper, we try to understand neural machine translation (NMT) via simplifying NMT architectures and training encoder-free NMT models.In an encoderfree model, the sums of word embeddings and positional embeddings represent the source.The decoder is a standard Transformer or recurrent neural network that directly attends to embeddings via attention mechanisms.Experimental results show (1) that the attention mechanism in encoder-free models acts as a strong feature extractor, (2) that the word embeddings in encoder-free models are competitive to those in conventional models, (3) that non-contextualized source representations lead to a big performance drop, and (4) that encoder-free models have different effects on alignment quality for German→English and Chinese→English.
A Novel Selection Approach for Genetic Algorithms for Global Optimization of Multimodal Continuous Functions

Genetic algorithms (GAs) are stochastic-based heuristic search techniques that incorporate three primary operators: selection, crossover, and mutation. These operators are supportive in obtaining the optimal solution for constrained optimization problems. Each operator has its own benefits, but selection of chromosomes is one of the most essential operators for optimal performance of the algorithms. In this paper, an improved genetic algorithm-based novel selection scheme, i.e., stairwise selection (SWS) is presented to handle the problems of exploration (population diversity) and exploitation (selection pressure). For its global performance, we compared with several other selection schemes by using ten well-known benchmark functions under various dimensions. For a close comparison, we also examined the significance of SWS based on the statistical results. Chi-square goodness of fit test is also used to evaluate the overall performance of the selection process, i.e., mean difference between observed and expected number of offspring. Hence, the overall empirical results along with graphical representation endorse that the SWS outperformed in terms of robustness, stability, and effectiveness other competitors through authentication of performance index (PI).
Different features of real-world objects are represented in a dependent manner in long-term memory.

In the present study, we examined how real-world objects are represented in long-term memory. Two contrasting views exist with regard to this question: one argues that real-world objects are represented as a set of independent features, and the other argues that they form bound integrate representations. In 5 experiments, we tested the different predictions of each view, namely whether the different features of real-world items are remembered and forgotten independently from each other, in a feature-based manner, or conversely are stored and lost in an object-based manner, with all features depending upon each other. Across various stimuli, learning tasks (incidental or explicit), experimental setups (within- or between-subjects design), feature-dimensions, and encoding times, we consistently found that information is forgotten in an object-based manner. When an object ceases to be fully remembered, all of its features are lost, instead of only some of the object's features being lost whereas other features are still remembered. Furthermore, we found support for a strong form of dependency among the different features, namely a hierarchical structure. We conclude that visual long-term memory is object-based, challenging previous findings. (PsycInfo Database Record (c) 2020 APA, all rights reserved).
Query-efficient Meta Attack to Deep Neural Networks

Black-box attack methods aim to infer suitable attack patterns to targeted DNN models by only using output feedback of the models and the corresponding input queries. However, due to lack of prior and inefficiency in leveraging the query and feedback information, existing methods are mostly query-intensive for obtaining effective attack patterns. In this work, we propose a meta attack approach that is capable of attacking a targeted model with much fewer queries. Its high query-efficiency stems from effective utilization of meta learning approaches in learning generalizable prior abstraction from the previously observed attack patterns and exploiting such prior to help infer attack patterns from only a few queries and outputs. Extensive experiments on MNIST, CIFAR10 and tiny-Imagenet demonstrate that our meta-attack method can remarkably reduce the number of model queries without sacrificing the attack performance. Besides, the obtained meta attacker is not restricted to a particular model but can be used easily with a fast adaptive ability to attack a variety of models. Our code will be released to the public.
Single-Image Fence Removal Using Deep Convolutional Neural Network

In public spaces such as zoos and sports facilities, the presence of fences often annoys tourists and professional photographers. There is a demand for a post-processing tool to produce a non-occluded view from an image or video. This “de-fencing” task is divided into two stages: one to detect fence regions and the other to fill the missing part. For over a decade, various methods have been proposed for video-based de-fencing. However, only a few single-image-based methods are proposed. In this paper, we focus on single-image fence removal. Conventional approaches suffer from inaccurate and non-robust fence detection and inpainting due to less content information. To solve these problems, we combine novel methods based on a deep convolutional neural network (CNN) and classical domain knowledge in image processing. In the training process, we are required to obtain both fence images and corresponding non-fence ground truth images. Therefore, we synthesize natural fence images from real images. Moreover, spacial filtering processing (e.g. a Laplacian filter and a Gaussian filter) improves the performance of the CNN for detection and inpainting. Our proposed method can automatically detect a fence and generate a clean image without any user input. Experimental results demonstrate that our method is effective for a broad range of fence images.
Teaching Narrative Writing Using Freaky Fables Game: An Experimentation

This study is an experimentation of the idea of using game to teach narrative writing in one of private primary schools in Bandar Lampung. 21 students were grouped according to their class (6A & 6B) into experimental and control.  The two groups were given pretest and post-test.  The experimental group was taught narrative writing using game called Freaky Fables, the control group was taught using the conventional method.  The experimental group also responded to a questionnaire which sought their reactions to the use of game in teaching narrative writing.  Data collected were analyzed.  Results shown that the experimental group performed significantly better than the control group due to treatment (the use of Freaky Fables game to teach narrative writing).  Consequently, the experimental group and the teachers involved showed favorable disposition to the continuous use of the method .