Reviewerly/exHarmony huggingface dataset:
Real-Time Video Stitching for Mine Surveillance Using a Hybrid Image Registration Method

Video stitching technology provides an effective solution for a wide viewing angle monitoring mode for industrial applications. At present, the observation angle of a single camera is limited, and the monitoring network composed of multiple cameras will have many overlapping images captured. Monitoring surveillance cameras can cause the problems of viewing fatigue and low video utilization rate of involved personnel. In addition, current video stitching technology has poor adaptability and real-time performance. We propose an effective hybrid image feature detection method for fast video stitching of mine surveillance video using the effective information of the surveillance video captured from multiple cameras in the actual conditions in the industrial coal mine. The method integrates the Moravec corner point detection and the scale-invariant feature transform (SIFT) feature extractor. After feature extraction, the nearest neighbor method and the random sampling consistency (RANSAC) algorithm are used to register the video frames. The proposed method reduces the image stitching time and solves the problem of feature re-extraction due to the change of observation angle, thus optimizing the entire video stitching process. The experimental results on the real-world underground mine videos show that the optimized stitching method can stitch videos at a speed of 21 fps, effectively meeting the real-time requirement, while the stitching effect has a good stability and applicability in real-world conditions.
Some modified Hestenes-Stiefel conjugate gradient algorithms with application in image restoration

It is efficient to use the Hestenes–Stiefe (HS) conjugate gradient algorithm in solving large-scale complex smooth optimization problems because of its simplicity and low calculation requirements. Additionally, this algorithm has been used to simultaneously solve large-scale nonsmooth problems, nonlinear equations, and practical application. In this paper, the authors propose some modified HS conjugate gradient algorithms that not only address large-scale nonlinear equations and nonsmooth convex problems but also have the following properties. i) The algorithms portray a decreasing trait and trust-region property without any additional condition. ii) It combines the steepest descent algorithm with the conjugate gradient algorithm. iii) They employ the global convergence theory and iv) can be successfully used to solve nonlinear optimization problems and image restoration.
Application of data mining technology in alarm analysis of communication network

Nowadays, with the continuous development of science and technology, the social demand for the network is more and more, and with the continuous expansion of the network scale, the complexity of the network is increasing day by day. How to locate the fault accurately and quickly from a large number of alarm information has become a problem. To solve this problem, we must use computer technology to study the automatic analysis technology of alarm correlation. Therefore, this paper applies data mining technology to communication network alarm analysis, and based on fuzzy theory, it can accurately describe the relationship between network alarm and fault reason, and combines data mining technology with fuzzy theory to form the network alarm correlation analysis method of fuzzy association rule mining. On this basis, in order to mine the fuzzy association rules of the fuzzy alarm database directly and avoid the interference in the process of converting the alarm database to the transaction database, this paper proposes a dynamic time window fuzzy association rule mining algorithm. Through the simulation analysis, compared with the traditional data mining technology, the fuzzy association rules mining method combined with the fuzzy theory has better performance, and the further proposed dynamic time window fuzzy association rules mining algorithm greatly improves the accuracy of the association rules confidence, and has good application performance in the network alarm correlation analysis.
Imbalance measure and proactive channel rebalancing algorithm for the Lightning Network

Making a payment in a privacy-aware payment channel network can be achieved by trying out several payment paths until one succeeds. With a large network, such as the Lightning Network, a completion of a single payment can take up to several minutes. We introduce a network imbalance measure and formulate the optimization problem of improving the balance of the network as a sequence of rebalancing operations of the funds within the channels along circular paths within the network. As the funds and balances of channels are not globally known, we introduce a greedy heuristic that improves every node's local balance despite the uncertainty. In an empirical simulation on a snapshot of the Lightning Network we demonstrate that the imbalance distribution of the network has a Kolmogorov-Smirnoff distance of 0.74 in comparison to the imbalance distribution after the heuristic is applied. We further show that the success rate of a single unit payment increases from 11.2% on the imbalanced network to 98.3% in the balanced network. Similarly, the median possible payment size across all pairs of participants increases from 0 to 0.5 mBTC for initial routing attempts on the cheapest possible path. Executing 4 different strategies for selecting rebalancing cycles lead to similar results indicating that a collaborative approach within the friend of a friend network might be preferable from a practical point of view.
End-to-End Speech Recognition Models for a Low-Resourced Indonesian Language

Recent automatic speech recognition (ASR) is commonly developed using deep learning (DL), instead of the Hidden Markov Model (HMM). Many researchers show that DL is much better than HMM in noisy environments. However, DL needs a huge speech corpus but does not require any dictionary as well as the concept of either phonemes or syllables. Many DL-based tools are developed and claimed as a language-independent ASR, such as Mozma DeepSpeech (MDS) and Kaituoxu SpeechTransformer (KST). Both MDS and KST are classified as End-to-End ASR (E2EASR), but MDS uses a Recurrent Neural Network (RNN) while KST exploits a Transformer Network. In this paper, two Indonesian ASR (INASR) are developed using both MDS and KST to see their performances to handle a low-resourced language. Evaluation using a small speech corpus of Bahasa Indonesia containing 40 k utterances shows that KST is slightly better than MDS, where it gives a word error rate (WER) of 22.00% while MDS produces a WER of 23.10%.
Drivocopter: A concept Hybrid Aerial/Ground vehicle for long-endurance mobility

Aerial robots show promise for increased capabilities in exploring unstructured and challenging environments. However, they are limited by payload capacity, flight times, and susceptibility to damage in case of collision. On the other hand, ground robots are able to carry larger payloads and have a lower cost of transport, at the price of limited mobility over challenging terrain. This paper presents a hybrid aerial/ground vehicle that combines the capabilities of both types of vehicles to enable multi-modal mobility in diverse and challenging environments, a lower cost of transport compared to purely aerial vehicles, increased payload capacity, and a design that is more robust to collisions and physical interaction within potentially cluttered and narrow spaces. The design consists of a UAV with four independently actuated spherical wheels which, in addition to providing traction for ground mobility, protect the propellers in collision. In comparison to hybrid vehicles with passive wheels presented in other designs, actuated wheels mitigates perception degradation in dusty environments caused by downwash from thrusting close to the ground. In addition, the integration of an end-to-end autonomy stack is presented which enables the control, planning, and autonomous navigation of the hybrid vehicle in unknown environments. The controls framework employs a geometric tracking controller for aerial trajectories and a cascaded position and velocity controller for ground mobility. We leverage motion primitives to locally plan collision-free paths and a differential flatness mapping to generate kinodynamically feasible trajectories for both terrestrial and aerial modalities in a unified manner. Lastly, we utilize a grid based A* search and probabilistic 3D mapping based on octrees to plan geometric aerial/ground paths to a goal. With this framework, we hope to demonstrate the capabilities of this hybrid aerial/ground vehicle in challenging unknown environments and improved energy efficiency for hybrid mobility over purely flying.
Real-time simulation of (2+1)-dimensional lattice gauge theory on qubits

We study the quantum simulation of Z2 lattice gauge theory in 2+1 dimensions. The dual variable formulation, the so-called Wegner duality, is utilized for reducing redundant gauge degrees of freedom. The problem of artificial charge unconservation is resolved for any charge distribution. As a demonstration, we simulate the real-time evolution of the system with two static electric charges, i.e., with two temporal Wilson lines. Some results obtained by the simulator (with no hardware noise) and the real device (with sizable hardware noise) of a quantum computer are shown.
Testing Contextualized Word Embeddings to Improve NER in Spanish Clinical Case Narratives

In the Big Data era, there is an increasing need to fully exploit and analyze the huge quantity of information available about health. Natural Language Processing (NLP) technologies can contribute by extracting relevant information from unstructured data contained in Electronic Health Records (EHR) such as clinical notes, patients' discharge summaries and radiology reports. The extracted information can help in health-related decision making processes. The Named Entity Recognition (NER) task, which detects important concepts in texts (e.g., diseases, symptoms, drugs, etc.), is crucial in the information extraction process yet has received little attention in languages other than English. In this work, we develop a deep learning-based NLP pipeline for biomedical entity extraction in Spanish clinical narratives. We explore the use of contextualized word embeddings, which incorporate context variation into word representations, to enhance named entity recognition in Spanish language clinical text, particularly of pharmacological substances, compounds, and proteins. Various combinations of word and sense embeddings were tested on the evaluation corpus of the PharmacoNER 2019 task, the Spanish Clinical Case Corpus (SPACCC). This data set consists of clinical case sections extracted from open access Spanish-language medical publications. Our study shows that our deep-learning-based system with domain-specific contextualized embeddings coupled with stacking of complementary embeddings yields superior performance over a system with integrated standard and general-domain word embeddings. With this system, we achieve performance competitive with the state-of-the-art.
AutoML Pipeline Selection: Efficiently Navigating the Combinatorial Space

Data scientists seeking a good supervised learning model on a dataset have many choices to make: they must preprocess the data, select features, possibly reduce the dimension, select an estimation algorithm, and choose hyperparameters for each of these pipeline components. With new pipeline components comes a combinatorial explosion in the number of choices! In this work, we design a new AutoML system TensorOboe to address this challenge: an automated system to design a supervised learning pipeline. TensorOboe uses low rank tensor decomposition as a surrogate model for efficient pipeline search. We also develop a new greedy experiment design protocol to gather information about a new dataset efficiently. Experiments on large corpora of real-world classification problems demonstrate the effectiveness of our approach.
Image-based Encrypted Traffic Classification with Convolution Neural Networks

Network traffic classification plays an important part in the network management and network monitoring. It can help administrators to understand the constitution of network traffic, facilitate administrators to manage the network and provide differentiated service quality and security monitoring. However, the widespread usage of the encryption techniques and dynamic ports policy make encrypted traffic classification become a great challenge for traditional traffic classification methods. In this paper, we propose an image-based method that can classify encrypted network traffic with a high accuracy. The basic idea of the method is to convert the first few nonzero payload sizes of session to gray images, and classify the converted gray images with convolutional neural network to achieve the goal of categorizing the encrypted network traffic. This method is very light-weight and it can automatically extract features, select features and classify encrypted network traffic to categories. We use the public dataset ISCX VPN-nonVPN to validate our proposed method. The experimental results show that our proposed approach achieves F1 score of 97.73% on the conventional encrypted traffic classification and F1 score of 99.55% on the virtual private network traffic(VPN) classification.
Malicious Attacks against Deep Reinforcement Learning Interpretations

The past years have witnessed the rapid development of deep reinforcement learning (DRL), which is a combination of deep learning and reinforcement learning (RL). However, the adoption of deep neural networks makes the decision-making process of DRL opaque and lacking transparency. Motivated by this, various interpretation methods for DRL have been proposed. However, those interpretation methods make an implicit assumption that they are performed in a reliable and secure environment. In practice, sequential agent-environment interactions expose the DRL algorithms and their corresponding downstream interpretations to extra adversarial risk. In spite of the prevalence of malicious attacks, there is no existing work studying the possibility and feasibility of malicious attacks against DRL interpretations. To bridge this gap, in this paper, we investigate the vulnerability of DRL interpretation methods. Specifically, we introduce the first study of the adversarial attacks against DRL interpretations, and propose an optimization framework based on which the optimal adversarial attack strategy can be derived. In addition, we study the vulnerability of DRL interpretation methods to the model poisoning attacks, and present an algorithmic framework to rigorously formulate the proposed model poisoning attack. Finally, we conduct both theoretical analysis and extensive experiments to validate the effectiveness of the proposed malicious attacks against DRL interpretations.
Drug dealers gone digital: using signalling theory to analyse criminal online personas and trust

Online and digital platforms play a central role in today’s illegal activities and related networks. Communicating through these channels makes creating an online criminal identity crucial to establish oneself as trustworthy and meet the needs of potential buyers, especially when reaching out to strangers in a market. A highly needed skill is balancing the signals of attracting wanted attention and of avoiding possible risks. This article explores how social media drug dealers present themselves in 173 Facebook profiles. The analysis of these online criminal personas is applied in two stages: a) a broader content analysis of the profile content and b) by using signalling theory to understand the overall visual presentation of these online criminal personas. I find that sellers fall into three main persona types: the professional dealer, private dealer, and cultural dealer. These personas signal trust in different ways through their choice of pictures, text, and personal information.
GrokNet

In this paper, we present GrokNet, a deployed image recognition system for commerce applications. GrokNet leverages a multi-task learning approach to train a single computer vision trunk. We achieve a 2.1x improvement in exact product match accuracy when compared to the previous state-of-the-art Facebook product recognition system. We achieve this by training on 7 datasets across several commerce verticals, using 80 categorical loss functions and 3 embedding losses. We share our experience of combining diverse sources with wide-ranging label semantics and image statistics, including learning from human annotations, user-generated tags, and noisy search engine interaction data. GrokNet has demonstrated gains in production applications and operates at Facebook scale.
Investigating the all-sky surface solar radiation and its influencing factors in the Yangtze River Basin in recent four decades

This study uses the second Modern-Era Retrospective Analysis for Research and Applications (MERRA-2) reanalysis product to analyze the all-sky surface solar radiation (SSR) over the Yangtze River basin (YRB) and the quantitative impact of main factors from 1980 to 2018. Radiation data and multiple atmospheric factors were inputted into a multiple linear regression model. The regression results showed that aerosols and total clouds were the dominant factors affecting annual SSR variation. The aerosol radiative effect (ARE) and cloud radiative effect (CRE) were then calculated to quantify the effects of aerosols and clouds on SSR based on the MERRA-2 dataset. The average ARE and CRE in the YRB was −10.70 ± 2.01 and −47.92 ± 2.31 W/m2, respectively, which means that clouds attenuate SSR more than aerosols. SSR showed a general decline throughout the region (−0.156 W/m2 per year) and decreased significantly in the Sichuan Basin (SB, −0.242 W/m2 per year). The enhanced weakening of the radiative cooling effect of aerosols and clouds determines the decrease in the SSR. In Central China (CC), ARE had significantly enhanced (−0.297 W/m2 per year) and CRE had significantly weakened (0.207 W/m2 per year). From 1980 to 1990, the SSR in the Western Sichuan Plateau (WSP) increased by 0.329 W/m2 per year, affected by the decrease in aerosol and cloud, in which low clouds and dust aerosols played an important role. The SSR in SB was reduced due to the significant increase in sulfate, black carbon, and organic carbon aerosols. And aerosols in Yangtze River Delta (YRD) have important effects on SSR, while clouds were dominant for CC. From 1991 to 2018, the dominant factor influencing the SSR in WSP, SB, and CC was cloud, while the SSR in YRD was mainly determined by aerosols. From 2008 to 2018, the SSR in WSP significantly decreased by −1.188 W/m2 per year, mainly due to the increase in clouds. During this period, the SSR in YRD increases by 0.220 W/m2 per year, largely from the decrease of aerosols.
FAIR digital objects in environmental and life sciences should comprise workflow operation design data and method information for repeatability of study setups and reproducibility of results

Abstract Repeatability of study setups and reproducibility of research results by underlying data are major requirements in science. Until now, abstract models for describing the structural logic of studies in environmental sciences are lacking and tools for data management are insufficient. Mandatory for repeatability and reproducibility is the use of sophisticated data management solutions going beyond data file sharing. Particularly, it implies maintenance of coherent data along workflows. Design data concern elements from elementary domains of operations being transformation, measurement and transaction. Operation design elements and method information are specified for each consecutive workflow segment from field to laboratory campaigns. The strict linkage of operation design element values, operation values and objects is essential. For enabling coherence of corresponding objects along consecutive workflow segments, the assignment of unique identifiers and the specification of their relations are mandatory. The abstract model presented here addresses these aspects, and the software DiversityDescriptions (DWB-DD) facilitates the management of thusly connected digital data objects and structures. DWB-DD allows for an individual specification of operation design elements and their linking to objects. Two workflow design use cases, one for DNA barcoding and another for cultivation of fungal isolates, are given. To publish those structured data, standard schema mapping and XML-provision of digital objects are essential. Schemas useful for this mapping include the Ecological Markup Language, the Schema for Meta-omics Data of Collection Objects and the Standard for Structured Descriptive Data. Data pipelines with DWB-DD include the mapping and conversion between schemas and functions for data publishing and archiving according to the Open Archival Information System standard. The setting allows for repeatability of study setups, reproducibility of study results and for supporting work groups to structure and maintain their data from the beginning of a study. The theory of ‘FAIR++’ digital objects is introduced.
An Efficient Method of Training Small Models for Regression Problems with Knowledge Distillation

Compressing deep neural network (DNN) models becomes a very important and necessary technique for real-world applications, such as deploying those models on mobile devices. Knowledge distillation is one of the most popular methods for model compression, and many studies have been made on developing this technique. However, those studies mainly focused on classification problems, and very few attempts have been made on regression problems, although there are many application of DNNs on regression problems. In this paper, we propose a new formalism of knowledge distillation for regression problems. First, we propose a new loss function, teacher outlier rejection loss, which rejects outliers in training samples using teacher model predictions. Second, we consider a multi-task network with two outputs: one estimates training labels which is in general contaminated by noisy labels; And the other estimates teacher model's output which is expected to modify the noise labels following the memorization effects. By considering the multi-task network, training of the feature extraction of student models becomes more effective, and it allows us to obtain a better student model than one trained from scratch. We performed comprehensive evaluation with one simple toy model: sinusoidal function, and two open datasets: MPIIGaze, and Multi-PIE. Our results show consistent improvement in accuracy regardless of the annotation error level in the datasets.