Reviewerly/exHarmony huggingface dataset:
A Novel SINS/USBL Tightly Integrated Navigation Strategy Based on Improved ANFIS

Strapdown inertial navigation system (SINS)/ ultra- short baseline (USBL) integrated navigation system is widely used in navigation and positioning of underwater vehicle. In an unknown and complex underwater environment, unstable USBL measurement information can lead to decreased navigation accuracy. In the traditional method, the interference to USBL is uniformly modeled as outliers, but in practice, it is also represented as slowly varying errors, update irregular errors. The latter two errors are not easy to be detected at the beginning of occurrence. Therefore, a SINS/USBL integrated navigation technology based on novel adaptive neural fuzzy inference system (ANFIS) is proposed. This model can detect and compensate abnormal USBL information and maximize USBL information. In order to improve the prediction accuracy of ANFIS, an ANFIS algorithm based on variational Bayesian Kalman filter and principal component analysis (PCA) is proposed to avoid the decrease of positioning accuracy caused by false compensation. The simulation shows that the designed ANFIS algorithm can accurately predict the above anomalies. And it can overcome the problem that USBL measurement anomalies are difficult to be detected at the initial stage. Combined with the proposed integrated navigation technology, it obtain the same positioning accuracy in complex environment as in good environment. The effectiveness and robustness of the proposed method are verified by river experiment.
How Can Human Values Be Addressed in AgileMethods A Case Study on SAFe

Agile methods are predominantly focused on delivering business values. But can Agile methods be adapted to effectively address and deliver human values such as social justice, privacy, and sustainability in the software they produce Human values are what an individual or a society considers important in life. Ignoring these human values in software can pose difficulties or risks for all stakeholders (e.g., user dissatisfaction, reputation damage, financial loss). To answer this question, we selected the Scaled AgileFramework (SAFe), one of the most commonly used Agile methods in the industry, and conducted a qualitative case study to identify possible intervention points within SAFe that are the most natural to address and integrate human values in software. We present five high-level empirically-justified sets of interventions in SAFe: artefacts, roles, ceremonies, practices, and culture. We elaborate how some currentAgile artefacts (e.g., user story), roles (e.g., product owner), ceremonies (e.g., stand-up meeting), and practices (e.g., business-facing testing) in SAFe can be modified to support the inclusion of human values in software. Further, our study suggests new and exclusive values-based artefacts (e.g., legislative requirement), ceremonies (e.g., values conversation), roles (e.g., values champion), and cultural practices (e.g., induction and hiring) to be introduced in SAFe for this purpose. Guided by our findings, we argue that existingAgile methods can account for human values in software delivery with some evolutionary adaptations.
CT-Net: An Efficient Network for Low-Altitude Object Detection Based on Convolution and Transformer

With the growing popularity of civilian unmanned aerial vehicles (UAVs), unauthorized flights are on the rise accordingly. Therefore, it is critical to detect low-altitude UAVs for protecting personal privacy and public safety. Though substantial progress has been made in UAV detection, the existing detection methods still have problems in balancing the detection accuracy, the model size, and the detection speed. To address these limitations, this article proposes a novel deep learning method named convolution–transformer network (CT-Net). First, the attention-enhanced transformer block (AETB), which builds a feature-enhanced multihead self-attention (FEMSA), is introduced into the backbone of the network to improve the feature extraction ability of the model. Then, a lightweight bottleneck module (LBM) is utilized to control the computation load and reduce the parameters. Finally, we present a directional feature fusion structure (DFFS) to improve the accuracy of detection when processing multiscale objects, especially small-size objects. The proposed scheme achieves 0.966 mAP with the input size of 640 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\times640$ </tex-math></inline-formula> pixels on our low-altitude small-object dataset, and the performance is superior to YOLOv5. Furthermore, the experimental result on MS COCO shows that the CT-Net can outperform the current state-of-the-art detectors by a large margin. Thus, it is feasible to apply CT-Net to low-altitude small-object detection according to the experimental results.
Towards Robust Mispronunciation Detection and Diagnosis for L2 English Learners with Accent-Modulating Methods

With the acceleration of globalization, more and more people are willing or required to learn second languages (L2). One of the major remaining challenges facing current mispronunciation and diagnosis (MDD) models for use in computer-assisted pronunciation training (CAPT) is to handle speech from L2 learners with a diverse set of accents. In this paper, we set out to mitigate the adverse effects of accent variety in building an L2 English MDD system with end-to-end (E2E) neural models. To this end, we first propose an effective modeling framework that infuses accent features into an E2E MDD model, thereby making the model more accent-aware. Going a step further, we design and present disparate accent-aware modules to perform accent-aware modulation of acoustic features in a finer-grained manner, so as to enhance the discriminating capability of the resulting MDD model. Extensive sets of experiments conducted on the L2-ARCTIC benchmark dataset show the merits of our MDD model, in comparison to some existing E2E-based strong baselines and the celebrated pronunciation scoring based method.
Machine-learning Approach using Solidity Bytecode for Smart-contract Honeypot Detection in the Ethereum

Smart contracts based on the Ethereum blockchain network have attracted attention from finance, media, and academic domains. As a result, smart contracts have been targeted by cyber attackers for the purpose of cryptocurrency theft. The smart contract honeypot is a commonly used attack method. An attacker who makes a honeypot lures other weak attackers who target vulnerable contracts by seeming to have exploitable flaws. The honeypot attacker then steals cryptocurrency from the weak attackers using a hidden trap. In this paper, we propose a machine-learning model that can detect such honeypots with high performance and prevent theft before it occurs. We use a term-frequency inverse document-frequency method to extract feature words and word2vec to learn distributed representations for the Solidity bytecode. As a result, we achieved higher PR-AUC scores in honeypot detection compared with previous efforts. Based on this, we demonstrate that the smart contract code contains useful information for honeypot detection. Furthermore, our proposed method works without using features that become available after theft. Hence, the method enables us to predict incidents and reduce the number of honeypot victims.
Multi-step-ahead solar irradiance modeling employing multi-frequency deep learning models and climatic data

In this paper two enhanced long-short-term memory (LSTM) models of sequenced-LSTM (SLSTM) and wavelet-LSTM (WLSTM), provided for multi-step-ahead simulation of solar irradiance of six stations, located in Iran and USA. In this respect, twenty-year recorded solar irradiance and climate data were employed. The proposed multi-frequency models serve all the capabilities of classic LSTM network and also handle its weakness in detecting and modeling multi-frequency information that often included in natural datasets. The suggested methodology improved the long-short auto-regressive term of climate-solar irradiance data by including very long frequencies of time series. The results revealed that the suggested multi-frequency LSTM methods could exceed the feed forward neural network and classic LSTM network in test phase up to 23% and 13%, respectively.
Scene Graph Refinement Network for Visual Question Answering

Visual Question Answering aims to answer the free-form natural language question based on the visual clues in a given image. It is a difficult problem as it requires understanding the fine-grained structured information of both language and image for compositional reasoning. To establish the compositional reasoning, recent works attempt to introduce the scene graph in VQA. However, as the generated scene graphs are usually quite noisy, it greatly limits the performance of question answering. Therefore, this paper proposes to refine the scene graphs for improving the effectiveness. Specifically, we present a novel <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">S</b> cene <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">G</b> raph <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">R</b> efinement network ( <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">SGR</b> ), which introduces a transformer-based refinement network to enhance the object and relation features for better classification. Moreover, as the question provides valuable clues for distinguishing whether the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\left\langle \mathit{subject, predicate, object} \right\rangle$</tex-math></inline-formula> triplets are helpful or not, the SGR network exploits the semantic information presented in the questions to select the most relevant relations for question answering. Extensive experiments are conducted on the GQA benchmark demonstrate the effectiveness of our method.
A Novel Convolutional Neural Network Model for Malaria Cell Images Classification

Infectious diseases are an imminent danger that faces human beings around the world.Malaria is considered a highly contagious disease.The diagnosis of various diseases, including malaria, was performed manually, but it required a lot of time and had some human errors.Therefore, there is a need to investigate an efficient and fast automatic diagnosis system.Deploying deep learning algorithms can provide a solution in which they can learn complex image patterns and have a rapid improvement in medical image analysis.This study proposed a Convolutional Neural Network (CNN) model to detect malaria automatically.A Malaria Convolutional Neural Network (MCNN) model is proposed in this work to classify the infected cases.MCNN focuses on detecting infected cells, which aids in the computation of parasitemia, or infection measures.The proposed model achieved 0.9929, 0.9848, 0.9859, 0.9924, 0.0152, 0.0141, 0.0071, 0.9890, 0.9894, and 0.9780 in terms of specificity, sensitivity, precision, accuracy, F1-score, and Matthews Correlation Coefficient, respectively.A comparison was carried out between the proposed model and some recent works in the literature.This comparison demonstrates that the proposed model outperforms the compared works in terms of evaluation metrics.
Cognitive Modeling With Representations From Large-Scale Digital Data

Deep-learning methods can extract high-dimensional feature vectors for objects, concepts, images, and texts from large-scale digital data sets. These vectors are proxies for the mental representations that people use in everyday cognition and behavior. For this reason, they can serve as inputs into computational models of cognition, giving these models the ability to process and respond to naturalistic prompts. Over the past few years, researchers have applied this approach to topics such as similarity judgment, memory search, categorization, decision making, and conceptual knowledge. In this article, we summarize these applications, identify underlying trends, and outline directions for future research on the computational modeling of naturalistic cognition and behavior.
Impact of Immersive Virtual Reality Content Using 360-Degree Videos in Undergraduate Education

This article investigated the impact of immersive virtual reality (VR) content, using 360-degree videos, in undergraduate education. To improve the delivery and reality of 360-degree VR content, we filmed the video in the third person so that the viewers could feel like they were in the environment where the lecture was conducted. To verify the educational effects, 33 university students participated in our experiment. We conducted pretest learning, using 360-degree videos, and posttest learning via conventional 2-D videos for statistical analysis. A paired <i>t</i>-test was used to compare the means of the pretest and the posttest. In addition, learning via 360-degree videos was assessed for its effectiveness through questionnaires consisting of five measurement elements&#x2014;engagement, immersion, motivation, cognitive benefits, and perceived learning effectiveness&#x2014;and comparing them to the existing 2-D video method, based on e-learning. From the results, we confirmed that the teaching material delivered through 360-degree VR content allows students to be more focused, immersed, and interested than 2-D learning modes. Furthermore, the high scores of cognitive and perceived learning elements imply that VR-based 360-degree educational content can encourage more active participation than traditional lectures and can improve the ability to analyze and organize study lessons.
An End-to-End No-Reference Video Quality Assessment Method With Hierarchical Spatiotemporal Feature Representation

In this paper, we propose a deep neural network-based no-reference (NR) video quality assessment (VQA) method with spatiotemporal feature fusion and hierarchical information integration to evaluate the perceptual quality of videos. First, a feature extraction model is proposed by using 2D and 3D convolutional layers to gradually extract spatiotemporal features from raw video clips. Second, we design a hierarchical branching network to fuse multiframe features, and the feature vectors at each hierarchical level are comprehensively considered during the process of network optimization. Finally, these two modules and quality regression are synthesized into an end-to-end architecture. Experimental results obtained on benchmark VQA databases demonstrate the superiority of our method over other state-of-the-art algorithms. The source code is available online. <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><xref ref-type="fn" rid="fn1">1</xref></sup>
Learning Residue-Aware Correlation Filters and Refining Scale Estimates with the GrabCut for Real-Time UAV Tracking

Unmanned aerial vehicle (UAV)-based tracking is attracting increasing attention and developing rapidly in applications such as agriculture, aviation, navigation, transportation and public security. Recently, discriminative correlation filters (DCF)-based trackers have stood out in UAV tracking community for their high efficiency and appealing robustness on a single CPU. However, due to limited onboard computation resources and other challenges the efficiency and accuracy of existing DCF-based approaches is still not satisfying. In this paper, inspired by residue representation, we exploit the residue nature inherent to videos and propose residue-aware correlation filters that show better convergence properties in filter learning. Moreover, we explore using segmentation by the GrabCut to improve the wildly adopted discriminative scale estimation in DCF-based trackers, which, as a mater of fact, greatly impacts the precision and accuracy of the trackers since accumulated scale error degrades the appearance model as online updating goes on. Extensive experiments are conducted on four UAV benchmarks, namely, UAV123@10fps, DTB70, UAVDT and Vistrone2018 (VisDrone2018-test-dev). The results show that our method achieves state-of-the-art performance.
Quantum simulation of chiral phase transitions

The Nambu-Jona-Lasinio (NJL) model has been widely studied for investigating the chiral phase structure of strongly interacting matter. The study of the thermodynamics of field theories within the framework of Lattice Field Theory is limited by the sign problem, which prevents Monte Carlo evaluation of the functional integral at a finite chemical potential. Using the quantum imaginary time evolution (QITE) algorithm, we construct a quantum simulation for the $(1+1)$ dimensional NJL model at finite temperature and finite chemical potential. We observe consistency among digital quantum simulation, exact diagonalization, and analytical solution, indicating further applications of quantum computing in simulating QCD thermodynamics.
Retrieval-based Spatially Adaptive Normalization for Semantic Image Synthesis

Semantic image synthesis is a challenging task with many practical applications. Albeit remarkable progress has been made in semantic image synthesis with spatiallyadaptive normalization, existing methods usually normalize the feature activations under the coarse-level guidance (e.g., semantic class). However, different parts of a semantic object (e.g., wheel and window of car) are quite different in structures and textures, making blurry synthesis results usually inevitable due to the missing of fine-grained guidance. In this paper, we propose a novel normalization module, termed as REtrieval-based Spatially Adaptive normaLization (RESAIL), for introducing pixel level fine- grained guidance to the normalization architecture. Specifically, we first present a retrieval paradigm by finding a content patch of the same semantic class from training set with the most similar shape to each test semantic mask. Then, the retrieved patches are composited into retrieval-based guidance, which can be used by RESAIL for pixel level fine-grained modulation on feature activations, thereby greatly mitigating blurry synthesis results. Moreover, distorted ground-truth images are also utilized as alternatives of retrieval-based guidance for feature normalization, further benefiting model training and improving visual quality of generated images. Experiments on several challenging datasets show that our RESAIL performs favorably against state-of-the-arts in terms of quantitative metrics, visual quality, and subjective evaluation. The source code is available at https://github.com/Shi-Yupeng/RESAIL-For-SIS.
GISDCN: A Graph-Based Interpolation Sequential Recommender with Deformable Convolutional Network

Sequential recommendation systems aim to predict users' next actions based on the preferences learned from their historical behaviors. There are still fundamental challenges for sequential recommender. First, with the popularization of online services, recommender needs to serve both the warm- and cold-start users. However, most existing models depending on user-item interactions lose merits due to the difficulty of learning sequential dependencies with limited interactions. Second, users' behaviors in their historical sequences are often implicit and complex due to the objective variability of reality and the subjective randomness of users' intentions. It is difficult to capture the dynamic transition patterns from these user-item interactions. In this work, we propose a graph-based interpolation enhanced sequential recommender with deformable convolutional network (GISDCN). For cold-start users, we re-construct item sequences into a graph to infer users' possible preferences. To capture the complex sequential dependencies, we employ the deformable convolutional network to generate more robust and flexible filters. Finally, we conduct comprehensive experiments and verify the effectiveness of our model. The experimental results demonstrate that GISDCN outperforms most of the state-of-the-art models at cold-start conditions.
Left, Right, and Gender: Exploring Interaction Traces to Mitigate Human Biases

Human biases impact the way people analyze data and make decisions. Recent work has shown that some visualization designs can better support cognitive processes and mitigate cognitive biases (i.e., errors that occur due to the use of mental "shortcuts"). In this work, we explore how visualizing a user's interaction history (i.e., which data points and attributes a user has interacted with) can be used to mitigate potential biases that drive decision making by promoting conscious reflection of one's analysis process. Given an interactive scatterplot-based visualization tool, we showed interaction history in real-time while exploring data (by coloring points in the scatterplot that the user has interacted with), and in a summative format after a decision has been made (by comparing the distribution of user interactions to the underlying distribution of the data). We conducted a series of in-lab experiments and a crowd-sourced experiment to evaluate the effectiveness of interaction history interventions toward mitigating bias. We contextualized this work in a political scenario in which participants were instructed to choose a committee of 10 fictitious politicians to review a recent bill passed in the U.S. state of Georgia banning abortion after 6 weeks, where things like gender bias or political party bias may drive one's analysis process. We demonstrate the generalizability of this approach by evaluating a second decision making scenario related to movies. Our results are inconclusive for the effectiveness of interaction history (henceforth referred to as interaction traces) toward mitigating biased decision making. However, we find some mixed support that interaction traces, particularly in a summative format, can increase awareness of potential unconscious biases.
VILA: Improving Structured Content Extraction from Scientific PDFs Using Visual Layout Groups

Abstract Accurately extracting structured content from PDFs is a critical first step for NLP over scientific papers. Recent work has improved extraction accuracy by incorporating elementary layout information, for example, each token’s 2D position on the page, into language model pretraining. We introduce new methods that explicitly model VIsual LAyout (VILA) groups, that is, text lines or text blocks, to further improve performance. In our I-VILA approach, we show that simply inserting special tokens denoting layout group boundaries into model inputs can lead to a 1.9% Macro F1 improvement in token classification. In the H-VILA approach, we show that hierarchical encoding of layout-groups can result in up to 47% inference time reduction with less than 0.8% Macro F1 loss. Unlike prior layout-aware approaches, our methods do not require expensive additional pretraining, only fine-tuning, which we show can reduce training cost by up to 95%. Experiments are conducted on a newly curated evaluation suite, S2-VLUE, that unifies existing automatically labeled datasets and includes a new dataset of manual annotations covering diverse papers from 19 scientific disciplines. Pre-trained weights, benchmark datasets, and source code are available at https://github.com/allenai/VILA.