Reviewerly/exHarmony huggingface dataset:
The Scientific Landscape of November 23rd, 1980 Irpinia-Basilicata Earthquake: Taking Stock of (Almost) 40 Years of Studies

The November 23rd, 1980 Irpinia-Basilicata (Southern Italy) earthquake is one of the strongest earthquakes ever occurred in Italy. The earthquake was a natural laboratory for the scientific community, which was engaged highly and promptly in investigating the event, thus publishing a flood of papers in different research areas over time. Just these research outputs are the focus of the article, which examines, with a tailored methodological approach, the international and national (Italian) studies started and advanced since the occurrence of the earthquake. First, we built and analyzed statistically two bibliographic databanks regarding the earthquake studies: (a) the international version of IRpinia Bibliographic databASE (IR_BASE_ENG), selecting and standardizing the pertinent scientific documents extracted from Scopus, Web of Science, and other databases and (b) the national version of the database (IR_BASE_IT) using the Google Scholar search engine to search for the most relevant papers in Italian. Second, IR_BASE_ENG was analyzed in a bibliometric perspective through the data mining VOSviewer software (Waltman et al., 2010) that builds co-occurrence term maps useful in perspective of investigating the wide-ranging studies on the earthquake. Third, taking a cue from this network analysis, we recognized the main research topics and performed a minireview of the related international studies, integrating in it a quick reference to the literature in Italian. Finally, we associated the scientific outputs to each cluster/topic, also performing the frequency analysis of the published documents for each subject, thus gaining information on the temporal trends of studies and getting a more exhaustive evidence of the scientific landscape on the earthquake over the last 40 years.
DUT: Learning Video Stabilization by Simply Watching Unstable Videos

Previous deep learning-based video stabilizers require a large scale of paired unstable and stable videos for training, which are difficult to collect. Traditional trajectory-based stabilizers, on the other hand, divide the task into several sub-tasks and tackle them subsequently, which are fragile in textureless and occluded regions regarding the usage of hand-crafted features. In this paper, we attempt to tackle the video stabilization problem in a deep unsupervised learning manner, which borrows the divide-and-conquer idea from traditional stabilizers while leveraging the representation power of DNNs to handle the challenges in real-world scenarios. Technically, DUT is composed of a trajectory estimation stage and a trajectory smoothing stage. In the trajectory estimation stage, we first estimate the motion of keypoints, initialize and refine the motion of grids via a novel multi-homography estimation strategy and a motion refinement network, respectively, and get the grid-based trajectories via temporal association. In the trajectory smoothing stage, we devise a novel network to predict dynamic smoothing kernels for trajectory smoothing, which can well adapt to trajectories with different dynamic patterns. We exploit the spatial and temporal coherence of keypoints and grid vertices to formulate the training objectives, resulting in an unsupervised training scheme. Experiment results on public benchmarks show that DUT outperforms state-of-the-art methods both qualitatively and quantitatively. The source code is available at https://github.com/Annbless/DUTCode.
Object Detection for Unmanned Aerial Vehicle Camera via Convolutional Neural Networks

The object tracking alongside the image segmentation have recently become a particular significance in satellite and aerial imagery. The latest achievements in this field are closely related to the application of the deep-learning algorithms and, particularly, convolutional neural networks (CNNs). Supplemented by the sufficient amount of the training data, CNNs provide the advantageous performance in comparison to the classical methods based on Viola-Jones or support vector machines. However, the application of CNNs for the object detection on the aerial images faces several general issues that cause classification error. The first one is related to the limited camera shooting angle and spatial resolution. The second one arises from the restricted dataset for specific classes of objects that rarely appear in the captured data. This article represents a comparative study on the effectiveness of different deep neural networks for detection of the objects with similar patterns on the images within a limited amount of the pretrained datasets. It has been revealed that YOLO ver. 3 network enables better accuracy and faster analysis than region convolution neural network (R-CNN), Fast R-CNN, Faster R-CNN, and SSD architectures. This has been demonstrated on the example of “Stanford dataset,” “DOTA v-1.5,” and “xView 2018 Detection” datasets. The following metrics on the accuracy have been obtained for the YOLO ver. 3 network: 89.12 mAP (Stanford dataset), 80.20 mAP (DOTA v-1.5), and 78.29 (xView 2018) for testing; and 85.51 mAP (Stanford dataset), 79.28 (DOTA v-1.5), and 79.92 (xView 2018) on validation with the analysis speed of 26.82 frames/s.
Sparse Learning-Based Correlation Filter for Robust Tracking

Many objective tracking methods are based on the framework of correlation filtering (CF) due to its high efficiency. In this paper, we propose a l2 -norm based sparse response regularization term to restrain unexpected crests in response for CF framework. CF trackers learn online to regress the region of interest into a Gaussian response. However, due to the uncertain transformations of tracked object, there are many unexpected crests in the response map. When the response of tracked object is corrupted by other crests, the tracker will lost the object. Therefore, the sparse response is used to increase the robustness to transformations of tracked object. Since the novel term is directly incorporated into the objective function of the CF framework, it can be used to improve the performance of many methods which are based on this framework. Moreover, from the solutions we derive, the new method will not increase the computational complexity. Through the experiments on benchmarks of OTB-100, TempleColor, VOT2016 and VOT2017, the proposed regularization term can improve the tracking performance of various CF trackers, including those based on standard discriminative CF framework and those based on context-aware CF framework. We also embed the sparse response regularization term in the state-of-the-art integrated tracker MCCT to test its generalization performance. Although MCCT is an expert integrated tracker and owns an exquisite algorithm for selecting experts, the experimental results show that our method can still improve its long-term tracking performance without increasing computational complexity.
Streaming End-to-End Multi-Talker Speech Recognition

End-to-end multi-talker speech recognition is an emerging research trend in the speech community due to its vast potential in applications such as conversation and meeting transcriptions. To the best of our knowledge, all existing research works are constrained in the offline scenario. In this work, we propose the Streaming Unmixing and Recognition Transducer (SURT) for end-to-end multi-talker speech recognition. Our model employs the Recurrent Neural Network Transducer (RNN-T) as the backbone that can meet various latency constraints. We study two different model architectures that are based on a speaker-differentiator encoder and a mask encoder respectively. To train this model, we investigate the widely used Permutation Invariant Training (PIT) approach and the Heuristic Error Assignment Training (HEAT) approach. Based on experiments on the publicly available LibriSpeechMix dataset, we show that HEAT can achieve better accuracy compared with PIT, and the SURT model with 150 milliseconds algorithmic latency constraint compares favorably with the offline sequence-to-sequence based baseline model in terms of accuracy.
NoiseRank: Unsupervised Label Noise Reduction with Dependence Models

Label noise is increasingly prevalent in datasets acquired from noisy channels. Existing approaches that detect and remove label noise generally rely on some form of supervision, which is not scalable and error-prone. In this paper, we propose NoiseRank, for unsupervised label noise reduction using Markov Random Fields (MRF). We construct a dependence model to estimate the posterior probability of an instance being incorrectly labeled given the dataset, and rank instances based on their estimated probabilities. Our method i) does not require supervision from ground-truth labels or priors on label or noise distribution, ii) is interpretable by design, enabling transparency in label noise removal, iii) is agnostic to classifier architecture/optimization framework and content modality. These advantages enable wide applicability in real noise settings, unlike prior works constrained by one or more conditions. NoiseRank improves state-of-the-art classification on Food101-N ( $$\sim $$ 20% noise), and is effective on high noise Clothing-1M ( $$\sim $$ 40% noise).
Extracting Family History of Patients From Clinical Narratives: Exploring an End-to-End Solution With Deep Learning Models

Background Patients’ family history (FH) is a critical risk factor associated with numerous diseases. However, FH information is not well captured in the structured database but often documented in clinical narratives. Natural language processing (NLP) is the key technology to extract patients’ FH from clinical narratives. In 2019, the National NLP Clinical Challenge (n2c2) organized shared tasks to solicit NLP methods for FH information extraction. Objective This study presents our end-to-end FH extraction system developed during the 2019 n2c2 open shared task as well as the new transformer-based models that we developed after the challenge. We seek to develop a machine learning–based solution for FH information extraction without task-specific rules created by hand. Methods We developed deep learning–based systems for FH concept extraction and relation identification. We explored deep learning models including long short-term memory-conditional random fields and bidirectional encoder representations from transformers (BERT) as well as developed ensemble models using a majority voting strategy. To further optimize performance, we systematically compared 3 different strategies to use BERT output representations for relation identification. Results Our system was among the top-ranked systems (3 out of 21) in the challenge. Our best system achieved micro-averaged F1 scores of 0.7944 and 0.6544 for concept extraction and relation identification, respectively. After challenge, we further explored new transformer-based models and improved the performances of both subtasks to 0.8249 and 0.6775, respectively. For relation identification, our system achieved a performance comparable to the best system (0.6810) reported in the challenge. Conclusions This study demonstrated the feasibility of utilizing deep learning methods to extract FH information from clinical narratives.
Service skill improvement for home robots: Autonomous generation of action sequence based on reinforcement learning

It still remains a challenge for robots to obtain knowledge automatically for performing home services. In the human learning process, natural languages act as an outline in guiding human beings complete tasks. From this point, a conditional generation method transforming textual manipulation instructions into action sequences is proposed, to provide home robots with knowledge automatically and improve the service skills finally. Due to the limited learning ability of the generation model on understanding complex semantic information, we present a two-phase conditional generation strategy in which the action space is reduced at the syntax level before generating action sequences semantically. For representing action sequences effectively, functional labels (FLs) are designed according to the requirements of performing home services, to identify six relationships about objects and actions. In action sequence generation, reinforcement learning is employed to guide the action sequence generation by introducing hierarchical rewards related to a priori knowledge, semantic similarity, and action logic. Based on statistic learning, a priori knowledge is constructed by modeling the relationship about object co-occurrence, action collaboration, and action–object correlation. The semantic similarity with Semantic Role Labeling enables the similarity evaluation between textual sentences (inputs) and produced sequences (outputs). And action logic, represented by the verb sequence in instructions, guides the production of action sequences logically. Experimental results demonstrate that the proposed method can produce competitive action sequences from textual instructions, and produced action sequences can be applied to robot for performing services.
Visual Memorability for Robotic Interestingness via Unsupervised Online Learning

In this paper, we explore the problem of interesting scene prediction for mobile robots. This area is currently underexplored but is crucial for many practical applications such as autonomous exploration and decision making. Inspired by industrial demands, we first propose a novel translation-invariant visual memory for recalling and identifying interesting scenes, then design a three-stage architecture of long-term, short-term, and online learning. This enables our system to learn human-like experience, environmental knowledge, and online adaption, respectively. Our approach achieves much higher accuracy than the state-of-the-art algorithms on challenging robotic interestingness datasets.
Tight State-Restoration Soundness in the Algebraic Group Model

Most efficient zero-knowledge arguments lack a concrete security analysis, making parameter choices and efficiency comparisons challenging. This is even more true for non-interactive versions of these systems obtained via the Fiat-Shamir transform, for which the security guarantees generically derived from the interactive protocol are often too weak, even when assuming a random oracle. This paper initiates the study of state-restoration soundness in the algebraic group model (AGM) of Fuchsbauer, Kiltz, and Loss (CRYPTO '18). This is a stronger notion of soundness for an interactive proof or argument which allows the prover to rewind the verifier, and which is tightly connected with the concrete soundness of the non-interactive argument obtained via the Fiat-Shamir transform. We propose a general methodology to prove tight bounds on state-restoration soundness, and apply it to variants of Bulletproofs (Bootle et al., S&P '18) and Sonic (Maller et al., CCS '19). To the best of our knowledge, our analysis of Bulletproofs gives the first non-trivial concrete security analysis for a non-constant round argument combined with the Fiat-Shamir transform.
Cross-Stack Workload Characterization of Deep Recommendation Systems

Deep learning based recommendation systems form the backbone of most personalized cloud services. Though the computer architecture community has recently started to take notice of deep recommendation inference, the resulting solutions have taken wildly different approaches - ranging from near memory processing to at-scale optimizations. To better design future hardware systems for deep recommendation inference, we must first systematically examine and characterize the underlying systems-level impact of design decisions across the different levels of the execution stack. In this paper, we characterize eight industry-representative deep recommendation models at three different levels of the execution stack: algorithms and software, systems platforms, and hardware microarchitectures. Through this cross-stack characterization, we first show that system deployment choices (i.e., CPUs or GPUs, batch size granularity) can give us up to 15x speedup. To better understand the bottlenecks for further optimization, we look at both software operator usage breakdown and CPU frontend and backend microarchitectural inefficiencies. Finally, we model the correlation between key algorithmic model architecture features and hardware bottlenecks, revealing the absence of a single dominant algorithmic component behind each hardware bottleneck.
TTL-IQA: Transitive Transfer Learning Based No-Reference Image Quality Assessment

Image quality assessment (IQA) based on deep learning faces the overfitting problem due to limited training samples available in existing IQA databases. Transfer learning is a plausible solution to the problem, in which the shared features derived from the large-scale Imagenet source domain could be transferred from the original recognition task to the intended IQA task. However, the Imagenet source domain and the IQA target domain as well as their corresponding tasks are not directly related. In this paper, we propose a new transitive transfer learning method for no-reference image quality assessment (TTL-IQA). First, the architecture of the multi-domain transitive transfer learning for IQA is developed to transfer the Imagenet source domain to the auxiliary domain, and then to the IQA target domain. Second, the auxiliary domain and the auxiliary task are constructed by a new generative adversarial network based on distortion translation (DT-GAN). Furthermore, a TTL network of the semantic features transfer (SFTnet) is proposed to optimize the shared features for the TTL-IQA. Experiments are conducted to evaluate the performance of the proposed method on various IQA databases, including the LIVE, TID2013, CSIQ, LIVE multiply distorted and LIVE challenge. The results show that the proposed method significantly outperforms the state-of-the-art methods. In addition, our proposed method demonstrates a strong generalization ability.
An Automated Contact Tracing Approach for Controlling Covid-19 Spread Based on Geolocation Data From Mobile Cellular Networks

The coronavirus (COVID-19) has appeared as the greatest challenge due to its continuous structural evolution as well as the absence of proper antidotes for this particular virus. The virus mainly spreads and replicates itself among mass people through close contact which unfortunately can happen in many unpredictable ways. Therefore, to slow down the spread of this novel virus, the only relevant initiatives are to maintain social distance, perform contact tracing, use proper safety gears, and impose quarantine measures. But despite being conceptually possible, these approaches are very difficult to uphold in densely populated countries and areas. Therefore, to control the virus spread, researchers and authorities are considering the use of smartphone based mobile applications (apps) to identify the likely infected persons as well as the highly risky zones to maintain isolation and lockdown measures. However, these methods heavily depend on advanced technological features and expose significant privacy loopholes. In this article, we propose a new method for COVID-19 contact tracing based on mobile phone users' geolocation data. The proposed method will help the authorities to identify the number of probable infected persons without using smartphone based mobile applications. In addition, the proposed method can help people take the vital decision of when to seek medical assistance by letting them know whether they are already in the list of exposed persons. Numerical examples demonstrate that the proposed method can significantly outperform the smartphone app-based solutions.
Architecture to Enhance Transparency in Supply Chain Management using Blockchain Technology

Precise information flow in a supply chain (SC) improves profitability. Distorted information in SC results in coordination and transaction issues that diminish trust between SC partners. The prime objective of the present study is to enhance transparency in SC for both suppliers and consumers and boost legitimate coordination within the SC network using Blockchain Technology (BT). BT is a tool having an open-source, decentralized, and distributed database for storing transaction information. It allows two parties to perform direct transactions using their distributed ledgers without the interference of a centralized third party to achieving more transparent transactions than traditional systems. Consequently, the architecture of BT in SC has been developed based on factors identified from the literature review that leads to offering tracking and monitoring of entire SC. Subsequently, the blockchain architecture has been tested considering transactions between manufacturer, distributor, retailer, and customer to accomplish real-time transparency. Accordingly, it has been observed that transaction errors have been minimized, transparency has been improved in SC of the manufacturing industry using BT. Moreover, this paper provides a productive relationship between BT and SC management.
Fuzzy KNN Method With Adaptive Nearest Neighbors

Due to its strong performance in handling uncertain and ambiguous data, the fuzzy k -nearest-neighbor method (FKNN) has realized substantial success in a wide variety of applications. However, its classification performance would be heavily deteriorated if the number k of nearest neighbors was unsuitably fixed for each testing sample. This study examines the feasibility of using only one fixed k value for FKNN on each testing sample. A novel FKNN-based classification method, namely, fuzzy KNN method with adaptive nearest neighbors (A-FKNN), is devised for learning a distinct optimal k value for each testing sample. In the training stage, after applying a sparse representation method on all training samples for reconstruction, A-FKNN learns the optimal k value for each training sample and builds a decision tree (namely, A-FKNN tree) from all training samples with new labels (the learned optimal k values instead of the original labels), in which each leaf node stores the corresponding optimal k value. In the testing stage, A-FKNN identifies the optimal k value for each testing sample by searching the A-FKNN tree and runs FKNN with the optimal k value for each testing sample. Moreover, a fast version of A-FKNN, namely, FA-FKNN, is designed by building the FA-FKNN decision tree, which stores the optimal k value with only a subset of training samples in each leaf node. Experimental results on 32 UCI datasets demonstrate that both A-FKNN and FA-FKNN outperform the compared methods in terms of classification accuracy, and FA-FKNN has a shorter running time.
The emerging role of deep learning in cytology

Deep learning (DL) is a component or subset of artificial intelligence. DL has contributed significant change in feature extraction and image classification. Various algorithmic models are used in DL such as a convolutional neural network (CNN), recurrent neural network, restricted Boltzmann machine, deep belief network and autoencoders. Of these, CNN is the most commonly used algorithm in the field of pathology for feature extraction and building neural network models. DL may be useful for tumour diagnosis, classification of the tumour and grading of the tumour in cytology. In this brief review, the basic concept of the DL and CNN are described. The application, prospects and challenges of the DL in the cytology are also discussed.