Reviewerly/exHarmony huggingface dataset:
External memory BWT and LCP computation for sequence collections with applications

Sequencing technologies produce larger and larger collections of biosequences that have to be stored in compressed indices supporting fast search operations. Many compressed indices are based on the Burrows–Wheeler Transform (BWT) and the longest common prefix (LCP) array. Because of the sheer size of the input it is important to build these data structures in external memory and time using in the best possible way the available RAM. We propose a space-efficient algorithm to compute the BWT and LCP array for a collection of sequences in the external or semi-external memory setting. Our algorithm splits the input collection into subcollections sufficiently small that it can compute their BWT in RAM using an optimal linear time algorithm. Next, it merges the partial BWTs in external or semi-external memory and in the process it also computes the LCP values. Our algorithm can be modified to output two additional arrays that, combined with the BWT and LCP array, provide simple, scan-based, external memory algorithms for three well known problems in bioinformatics: the computation of maximal repeats, the all pairs suffix–prefix overlaps, and the construction of succinct de Bruijn graphs. We prove that our algorithm performs $${\mathcal {O}}(n\, \mathsf {maxlcp})$$ sequential I/Os, where n is the total length of the collection and $$\mathsf {maxlcp}$$ is the maximum LCP value. The experimental results show that our algorithm is only slightly slower than the state of the art for short sequences but it is up to 40 times faster for longer sequences or when the available RAM is at least equal to the size of the input.
An overview of word and sense similarity

Abstract Over the last two decades, determining the similarity between words as well as between their meanings, that is, word senses, has been proven to be of vital importance in the field of Natural Language Processing. This paper provides the reader with an introduction to the tasks of computing word and sense similarity. These consist in computing the degree of semantic likeness between words and senses, respectively. First, we distinguish between two major approaches: the knowledge-based approaches and the distributional approaches. Second, we detail the representations and measures employed for computing similarity. We then illustrate the evaluation settings available in the literature and, finally, discuss suggestions for future research.
A Virtual Tour of the Cell: Impact of Virtual Reality on Student Learning and Engagement in the STEM Classroom

This study assesses the educational impact of virtual reality (VR) on student learning and engagement in a sophomore-level cell biology course taught at Otterbein University. Using VR, students toured the cell and its environment within the human body. Next, students completed a team challenge where they worked as pairs to match images of cell components taken from the virtual reality application with the appropriate name. Finally, students were given a voluntary survey that presented questions about their perceptions of the VR experience and the associated cell sorting activity. Survey results revealed that a majority of students enjoyed the VR experience and felt that it had a positive impact on their education. These results indicate the potential supportive role that VR may have in a variety of undergraduate courses.
Graph and Autoencoder Based Feature Extraction for Zero-shot Learning

Zero-shot learning (ZSL) aims to build models to recognize novel visual categories that have no associated labelled training samples. The basic framework is to transfer knowledge from seen classes to unseen classes by learning the visual-semantic embedding. However, most of approaches do not preserve the underlying sub-manifold of samples in the embedding space. In addition, whether the mapping can precisely reconstruct the original visual feature is not investigated in-depth. In order to solve these problems, we formulate a novel framework named Graph and Autoencoder Based Feature Extraction (GAFE) to seek a low-rank mapping to preserve the sub-manifold of samples. Taking the encoder-decoder paradigm, the encoder part learns a mapping from the visual feature to the semantic space, while decoder part reconstructs the original features with the learned mapping. In addition, a graph is constructed to guarantee the learned mapping can preserve the local intrinsic structure of the data. To this end, an L21 norm sparsity constraint is imposed on the mapping to identify features relevant to the target domain. Extensive experiments on five attribute datasets demonstrate the effectiveness of the proposed model.
Learning Low-precision Neural Networks without Straight-Through Estimator (STE)

The Straight-Through Estimator (STE) is widely used for back-propagating gradients through the quantization function, but the STE technique lacks a complete theoretical understanding. We propose an alternative methodology called alpha-blending (AB), which quantizes neural networks to low precision using stochastic gradient descent (SGD). Our AB method avoids STE approximation by replacing the quantized weight in the loss function by an affine combination of the quantized weight w_q and the corresponding full-precision weight w with non-trainable scalar coefficient alpha and (1- alpha). During training, alpha is gradually increased from 0 to 1; the gradient updates to the weights are through the full precision term, (1-alpha) * w, of the affine combination; the model is converted from full-precision to low precision progressively. To evaluate the AB method, a 1-bit BinaryNet on CIFAR10 dataset and 8-bits, 4-bits MobileNet v1, ResNet_50 v1/2 on ImageNet are trained using the alpha-blending approach, and the evaluation indicates that AB improves top-1 accuracy by 0.9\%, 0.82\% and 2.93\% respectively compared to the results of STE based quantization.
Learning Dynamic Generator Model by Alternating Back-Propagation through Time

This paper studies the dynamic generator model for spatialtemporal processes such as dynamic textures and action sequences in video data. In this model, each time frame of the video sequence is generated by a generator model, which is a non-linear transformation of a latent state vector, where the non-linear transformation is parametrized by a top-down neural network. The sequence of latent state vectors follows a non-linear auto-regressive model, where the state vector of the next frame is a non-linear transformation of the state vector of the current frame as well as an independent noise vector that provides randomness in the transition. The non-linear transformation of this transition model can be parametrized by a feedforward neural network. We show that this model can be learned by an alternating back-propagation through time algorithm that iteratively samples the noise vectors and updates the parameters in the transition model and the generator model. We show that our training method can learn realistic models for dynamic textures and action patterns.
The many shades of digital vigilantism. A typology of online self-justice

Digital vigilantism involves direct online actions of targeted surveillance, dissuasion or punishment, which tend to rely on public denunciation or on an excess of unsolicited attention, and are carried out in the name of justice, order or safety. Drawing on a diversity of case studies, this article seeks to provide a comprehensive picture of its manifestations, addressing both the social practices and digital media dynamics involved. It presents a typology which distinguishes between four ideal types of digital vigilantism: flagging, investigating, hounding and organised leaking.
Large-Scale Empirical Studies on Effort-Aware Security Vulnerability Prediction Methods

Security vulnerability prediction (SVP) can identify potential vulnerable modules in advance and then help developers to allocate most of the test resources to these modules. To evaluate the performance of different SVP methods, we should take the security audit and code inspection into account and then consider effort-aware performance measures (such as ACC and P <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">opt</sub> ). However, to the best of our knowledge, the effectiveness of different SVP methods has not been thoroughly investigated in terms of effort-aware performance measures. In this article, we consider 48 different SVP methods, of which 36 are supervised methods and 12 are unsupervised methods. For the supervised methods, we consider 34 software-metric-based methods and two text-mining-based methods. For the software-metric-based methods, in addition to a large number of classification methods, we also consider four state-of-the-art methods (i.e., EALR, OneWay, CBS, and MULTI) proposed in recent effort-aware just-in-time defect prediction studies. For text-mining-based methods, we consider the Bag-of-Word model and the term-frequency-inverse-document-frequency model. For the unsupervised methods, all the modules are ranked in the ascendent order based on a specific metric. Since 12 software metrics are considered when measuring extracted modules, there are 12 different unsupervised methods. To the best of our knowledge, over 40 SVP methods have not been considered in previous SVP studies. In our large-scale empirical studies, we use three real open-source web applications written in PHP as benchmark. These three web applications include 3466 modules and 223 vulnerabilities in total. We evaluate these SVP methods both in the within-project SVP scenario and the cross-project SVP scenario. Empirical results show that two unsupervised methods [i.e., lines of code (LOC) and Halstead's volume (HV)] and four recently proposed state-of-the-art supervised methods (i.e., MULTI, OneWay, CBS, and EALR) can achieve better performance than the other methods in terms of effort-aware performance measures. Then, we analyze the reasons why these six methods can achieve better performance. For example, when using 20% of the entire efforts, we find that these six methods always require more modules to be inspected, especially for unsupervised methods LOC and HV. Finally, from the view of practical vulnerability localization, we find that all the unsupervised methods and the OneWay method have high false alarms before finding the first vulnerable module. This may have an impact on developers' confidence and tolerance, and supervised methods (especially MULTI and text-mining-based methods) are preferred.
DeepDrawing: A Deep Learning Approach to Graph Drawing

Node-link diagrams are widely used to facilitate network explorations. However, when using a graph drawing technique to visualize networks, users often need to tune different algorithm-specific parameters iteratively by comparing the corresponding drawing results in order to achieve a desired visual effect. This trial and error process is often tedious and time-consuming, especially for non-expert users. Inspired by the powerful data modelling and prediction capabilities of deep learning techniques, we explore the possibility of applying deep learning techniques to graph drawing. Specifically, we propose using a graph-LSTM-based approach to directly map network structures to graph drawings. Given a set of layout examples as the training dataset, we train the proposed graph-LSTM-based model to capture their layout characteristics. Then, the trained model is used to generate graph drawings in a similar style for new networks. We evaluated the proposed approach on two special types of layouts (i.e., grid layouts and star layouts) and two general types of layouts (i.e., ForceAtlas2 and PivotMDS) in both qualitative and quantitative ways. The results provide support for the effectiveness of our approach. We also conducted a time cost assessment on the drawings of small graphs with 20 to 50 nodes. We further report the lessons we learned and discuss the limitations and future work.
JuICe: A Large Scale Distantly Supervised Dataset for Open Domain Context-based Code Generation

Interactive programming with interleaved code snippet cells and natural language markdown is recently gaining popularity in the form of Jupyter notebooks, which accelerate prototyping and collaboration.To study code generation conditioned on a long context history, we present JuICe, a corpus of 1.5 million examples with a curated test set of 3.7K instances based on online programming assignments.Compared with existing contextual code generation datasets, JuICe provides refined human-curated data, open-domain code, and an order of magnitude more training data.Using JuICe, we train models for two tasks:(1) generation of the API call sequence in a code cell, and (2) full code cell generation, both conditioned on the NL-Code history up to a particular code cell.Experiments using current baseline code generation models show that both context and distant supervision aid in generation, and that the dataset is challenging for current systems.
A Summarization System for Scientific Documents

We present a novel system providing summaries for Computer Science publications.Through a qualitative user study, we identified the most valuable scenarios for discovery, exploration and understanding of scientific documents.Based on these findings, we built a system that retrieves and summarizes scientific documents for a given information need, either in form of a free-text query or by choosing categorized values such as scientific tasks, datasets and more.Our system ingested 270,000 papers, and its summarization module aims to generate concise yet detailed summaries.We validated our approach with human experts.
No Army, No Navy: BERT Semi-Supervised Learning of Arabic Dialects

We present our deep leaning system submitted to MADAR shared task 2 focused on twitter user dialect identification.We develop tweetlevel identification models based on GRUs and BERT in supervised and semi-supervised settings.We then introduce a simple, yet effective, method of porting tweet-level labels at the level of users.Our system ranks top 1 in the competition, with 71.70% macro F 1 score and 77.40% accuracy.
Deep Neural Networks for CSI-Based Authentication

From the viewpoint of physical-layer authentication, spoofing attacks can be foiled by checking channel state information (CSI). Existing CSI-based authentication algorithms mostly require a deep knowledge of the channel variation to deliver decent performance. In this paper, we investigate CSI-based authenticators that can spare the effort to predetermine channel properties by utilizing deep neural networks (DNNs). First, we propose a convolutional neural network (CNN)-enabled authenticator that is able to extract the local features in CSI. Next, the recurrent neural network (RNN) is employed to capture the dependencies between different frequencies in CSI. In addition, we propose to use the convolutional recurrent neural network (CRNN)-a combination of the CNN and the RNN-to learn local and contextual information in CSI for user authentication. Finally, experiments based on Universal Software Radio Peripherals (USRPs) are conducted to demonstrate the performance of the proposed methods on real-world channel estimates. According to the experimental results, the proposed DNNs-enabled schemes can significantly outperform the dynamic time warping (DTW) technique and a heuristic Neyman-Pearson (NP) test in the aspects of false alarm and miss detection. Besides, the hybrid of the CNN and the RNN can further promote the authentication accuracy.
TIGEr: Text-to-Image Grounding for Image Caption Evaluation

This paper presents a new metric called TIGEr for the automatic evaluation of image captioning systems.Popular metrics, such as BLEU and CIDEr, are based solely on text matching between reference captions and machinegenerated captions, potentially leading to biased evaluations because references may not fully cover the image content and natural language is inherently ambiguous.Building upon a machine-learned text-image grounding model, TIGEr allows to evaluate caption quality not only based on how well a caption represents image content, but also on how well machine-generated captions match human-generated captions.Our empirical tests show that TIGEr has a higher consistency with human judgments than alternative existing metrics.We also comprehensively assess the metric's effectiveness in caption evaluation by measuring the correlation between human judgments and metric scores.
Edge-Guided Output Adaptor: Highly Efficient Adaptation Module for Cross-Vendor Medical Image Segmentation

Supervised convolutional neural networks (CNNs) have demonstrated state-of-art performance in medical image segmentation tasks. However, the performance of a well-trained CNN on an independent dataset (e.g., different vendors, sequences) relies strongly on the distribution similarity, and may drop unexpectedly in case of distribution shift. To obtain a large amount of annotation from each new dataset for re-training the CNN is expensive and impractical. Adaptation algorithms to improve the CNN generalizability from source domain to target domain has significant practical value. In this work, we propose a highly efficient end-to-end domain adaptation approach, with left ventricle segmentation from cine MRI sequences as an example. We propose to perform domain adaptation in the output space where different domains share the strongest similarities. The core of this algorithm is a flexible and light output adaption module based on adversarial learning. Moreover, Canny edge detector is introduced to enhance model's attention to edges during adversarial learning. Comparative experiments were carried out using images from three major MR vendors (Philips, Siemens, and GE) as three domains. Our results demonstrated that the proposed method substantially improved the generalization of the trained CNN model from one vendor to other vendors without any additional annotation. Moreover, the ablation study proved that introducing Canny edge detector further refined the edge detection in segmentation. The proposed adaption is generic can be extended to other medical image segmentation problems.
Object Recognition in Very Low Resolution Images Using Deep Collaborative Learning

Although recent studies on object recognition using deep neural networks have reported remarkable performance, they have usually assumed that adequate object size and image resolution are available, which may not be guaranteed in real applications.This paper proposes a framework for recognizing objects in very low resolution images through the collaborative learning of two deep neural networks: image enhancement network and object recognition network.The proposed image enhancement network attempts to enhance extremely low resolution images into sharper and more informative images with the use of collaborative learning signals from the object recognition network.The object recognition network with trained weights for high resolution images actively participates in the learning of the image enhancement network.It also utilizes the output from the image enhancement network as augmented learning data to boost its recognition performance on very low resolution objects.Through experiments on various low resolution image benchmark datasets, we verified that the proposed method can improve the image reconstruction and classification performance.
Enhanced phase-coding method for three-dimensional shape measurement with half-period codeword

The phase-coding method has been widely used for 3D shape measurement, which uses sinusoidal phase-shifting patterns to recover the wrapped phase and the stair phase-coding patterns to determine the fringe order. However, due to random noises and image blurring, the fringe order is always misaligned with the wrapped phase, which will lead to fringe order errors. This paper presents an enhanced phase-coding method to address this misalignment problem by using half-period codewords, in which each codeword is aligned to the half-period of the sinusoidal patterns. Then, two complementary fringe orders with half-period dislocation can be calculated, which can effectively eliminate the fringe order errors. To extend the coding range of stair phase, this paper further develops a computational scheme based on the geometric constraint method. Simulations and experiments have been carried out, and their results confirm that the enhanced method can reliably recover the 3D shape of the measured objects.
Generative Adversarial Attacks Against Deep-Learning-Based Camera Model Identification

Recently, deep learning techniques have gained popularity in multimedia forensics research designed to accomplish tasks such as camera model identification. However, despite the success of deep learning techniques, research has shown that they are vulnerable to adversarial perturbations. These adversarial perturbations can cause deep learning classifiers to misclassify images even though the perturbations are imperceptible to human eyes. To understand the vulnerabilities of deep-learning-based forensic algorithms, we propose a novel anti-forensic framework inspired by generative adversarial networks that is capable of falsifying an image&#x2019;s source camera model. To accomplish this, we design a generator to anti-forensically falsify camera model traces in an image without introducing visually perceptible changes or artifacts. We propose two techniques to adversarially train this generator depending on the knowledge available to the attacker. In a white-box scenario when complete knowledge of an investigator&#x2019;s camera model identification network is available to an attacker, we directly incorporate the network into our generator&#x2019;s adversarial training strategy. In a black-box scenario when no internal details of the camera model classifier are available to the attacker, we construct a substitute network to mimic its decisions, then utilize this substitute network to adversarially train our generator. We conduct a series of experiments to evaluate the performance of our attack against several well-known CNNbased camera model classifiers. Experimental results show that our attack can successfully fool these CNNs in both white-box and black-box scenarios. Furthermore, our attack maintains high image quality and can be generalized to attack images from arbitrary source camera models.
Swarm Robot Task Planning Based on Air and Ground Coordination for Disaster Search and Rescue

摘要: 面向地震场景,提出了一种有效的空地协同搜救框架,高空侦察机获取地面受损建筑物位置信息传递给无人机搜索群体,无人机根据此信息做分布式任务规划,到达目标建筑对受灾人群做进一步侦察,并将伤员密度分布信息传递给地面无人车群体。无人车做集中式规划,到达伤员地点执行救援任务。针对无人机群体的任务规划,根据其小型、廉价、可大规模部署以及通信能力弱的特点,改进传统的拍卖任务规划方法,提出了一种新的基于通信保持的拍卖方法(CMBA);救援无人车群体虽然载荷能力强,但是在灾区恶劣道路环境条件下,无法大规模部署,必须发挥其执行任务的最大效用,传统遗传算法适用于中心化的精确任务规划,但是存在易陷入局部最优解的缺点,提出了一种自适应反馈调节遗传算法(AFBA-GA)改进这一缺点。通过在机器人仿真器中和标准数据集中的测试,验证了任务规划方法的有效性。
Collaborative Multi-Agent Dialogue Model Training Via Reinforcement Learning

We present the first complete attempt at concurrently training conversational agents that communicate only via self-generated language.Using DSTC2 as seed data, we trained natural language understanding (NLU) and generation (NLG) networks for each agent and let the agents interact online.We model the interaction as a stochastic collaborative game where each agent (player) has a role ("assistant", "tourist", "eater", etc.) and their own objectives, and can only interact via natural language they generate.Each agent, therefore, needs to learn to operate optimally in an environment with multiple sources of uncertainty (its own NLU and NLG, the other agent's NLU, Policy, and NLG).In our evaluation, we show that the stochastic-game agents outperform deep learning based supervised baselines.