Reviewerly/exHarmony huggingface dataset:
DRFD-Net: Using Dual Receptive Field Descriptors for Multitemporal Optical Remote Sensing Image Registration

Multitemporal optical remote sensing image registration is still a challenging problem for current feature-based image registration algorithms due to the complex nonlinear discrepancies arising from diverse factors, including illumination, weather, and surface condition changes. To address the issue, this article attempts to combine the dual receptive field descriptors (DRFDs) constructed by a novel deep convolutional network. In addition, a novel inner loss function (ILF) that imposes constraints on the intermediate descriptors is adopted in order to consolidate the distinguishability of the descriptors when the overlapping areas of the input image patches are large. Subsequently, the dual feature distance maps (DFDMs) are built on the basis of the DRFDs and combined with features from accelerated segment test (FAST) key points for efficient and accurate correspondence establishment across the source image and the target image. Eventually, an iterative algorithm is proposed to remove the possible outliers. Experiments show that the combination of DRFDs trained with the ILF performs better than current learnable local descriptors, such as L2-Net, HardNet, and SOSNet. The image registration results using our method are more accurate than the methods based on learnable descriptors, such as L2-Net, HardNet, and SOSNet, and handcrafted descriptors, such as scale-invariant feature transform (SIFT), SURF, and ORB.
TOMN: Constituent-Based Tagging Scheme

The characteristics of time expressions drive us to design a learning-based method named TOMN to model time expressions. TOMN defines a constituent-based tagging scheme named TOMN scheme with four tags, namely T, O, M, and N, indicating the constituents of time expression, namely Time token, Modifier, Numeral, and the words Outside time expression. In modeling, TOMN assigns a word with a TOMN tag under conditional random fields with minimal features. Essentially, our constituent-based TOMN scheme overcomes the problem of inconsistent tag assignment that is caused by the conventional position-based tagging schemes (e.g., BIO scheme and BILOU scheme). Evaluation shows that TOMN is equally or more effective than state-of-the-art methods on various datasets, and much more robust on cross-datasets.
Rapid Blockchain Scaling with Efficient Transaction Assignment

The abrupt development of cryptocurrencies and blockchain technology has pointed to the importance of deploying large scale, highly robust Byzantine fault-tolerant schemes to handle critical distributed failures in system networks. Although traditional wisdom is to build systems by following synchronous protocols such as PBFT consensus, these protocols strongly rely on network time limitations and only guarantee liveness when the nodes behave arbitrary. This paper presents the design and implementation of Adrastus, a scalable blockchain system. The main contributions of Adrastus are the use of a consistent hashing mechanism which solves load balancing problems and the efficient assignment of transactions on parallel regions of single-chain consensus systems, referred to as zones, without introducing unnecessary overhead. We claim that the Adrastus blockchain system scales linearly without compromising system security. We present theoretical analysis, discuss our solution, and examine the conditions to meet both safety and liveness of our fault-tolerant system.
Bayesian Causal Inference for Real World Interactive Systems

Machine learning has allowed many systems that we interact with to improve performance and personalize. Recommender systems in particular are one of the largest users of machine learning in production environments that have improved performance of real-world systems. Learning in these interactive systems requires models that combine very diverse signals, including the logs of the interactive system (indicating if the intervention succeeded or failed) augmented with other data sources including: collaborative filtering, text, and image data. Bayesian inference is a compelling method to combine these diverse signals in a principled manner, but deployment of systems based on Bayesian principles remain challenging. The reward signal in the system logs is often uneven. Accurate estimation of reward is possible for exploiting actions, but often poor for other actions (exploration). Non-Bayesian methods such as inverse propensity score methods, the reinforce algorithm, and other heuristic-based approaches currently dominate practice. These commonly-used heuristics are often ineffective at leveraging diverse data. In contrast, Bayesian methods offer a principled, robust framework for learning from uneven signals and combining different types of information. Drawing upon the bandit and reinforcement learning community, in this workshop we will explore innovations in Bayesian inference for real world interactive systems, and consider advantages and limitations of the Bayesian approach.
Towards a <scp>data‐driven</scp> framework for optimizing <scp>security‐efficiency</scp> tradeoff in <scp>QUIC</scp>

Abstract Advances in computing and compression technology, coupled with high‐speed networks, has beaconed an era of video streaming on the Internet. This has led to a need to enhance the security of communications transporting data without degrading its performance. The transport layer security (TLS) protocol negotiates configurations for securing communication channels. Such conversations adversely impact latency, thereby presenting a fundamental tradeoff between security and efficiency. In this work, we present a conceptual framework, called SEC‐QUIC (Secure and Efficient Configurations for QUIC), that focuses on optimizing this tradeoff specifically for video transmissions by investigating various factors in Quick UDP Internet Connections (QUIC). Transport‐layer‐related elements, such as maximum transmission unit (MTU) sizes, cipher suites, and ACK timer, are examined to evaluate the impact on the security‐efficiency tradeoff in QUIC‐based video transmissions using platform‐based experiments. Subsequently, we develop a conceptual framework to leverage QUIC's dynamics based on the context of a connection to optimize the security‐efficiency tradeoff. Our findings demonstrate the need to alter default configurations based on the contextual factors of a connection (eg, resource constraints and network conditions) in QUIC‐based video transmissions to balance the tradeoff. Experiments reveal an MTU of 1400 bytes is found to have 60% better throughput compared to an MTU of 1200 bytes while also 4% less CPU usage on average for the transmission of 100 MB video files. Overall, our experiments suggest that fine‐tuning performance and security related configurations is an effective approach to optimizing the security‐efficiency tradeoff in video transmissions.
Unsupervised Segmentation Framework with Active Contour Models for Cine Cardiac MRI

Deep learning methods have made remarkable progress in medical image segmentation tasks, but these methods require enough labeled data, which tends to be difficult for medical tasks. To tack this issue, we propose an unsupervised segmentation framework by combining deep learning networks with the active contour model. We design an iterative loop process that the network can be trained with outputs from the active contour model and the active contour model can be initialized by coarse predictions from the network. In this way, our approach can train the segmentation networks iterative with no annotations but only one initialization for the active contour model at the beginning. We evaluate our approach in the task of cine cardiac MRI segmentation and get very competitive results.
Curiously Effective Features For Image Quality Prediction

The performance of visual quality prediction models is commonly assumed to be closely tied to their ability to capture perceptually relevant image aspects. Models are thus either based on sophisticated feature extractors carefully designed from extensive domain knowledge or optimized through feature learning. In contrast to this, we find feature extractors constructed from random noise to be sufficient to learn a linear regression model whose quality predictions reach high correlations with human visual quality ratings, on par with a model with learned features. We analyze this curious result and show that besides the quality of feature extractors also their quantity plays a crucial role - with top performances only being achieved in highly overparameterized models.
SardineDB: A Distributed Database on the Edge of the Network

In the past few years, the number of sensors is rapidly increasing on the edge of the network. IoT(Internet of things) devices play not only data producers but also data consumers. It is valuable to deploy a distributed database on the edge of the network. However, flash memory is the mainstream storage medium on the edge, which is different from cloud environment. Flash memory can wear out through repeated writes while large amount of data are written on the edge per day. Thus, in this paper, SardineDB is presented, which is a decentralized distributed database optimized for edge. The engine of SardineDB is SardineCore, which is a flash-optimized key-value separation storage based on LevelDB. SardineCore has low GC (garbage collection) burden, which can be used to low the write amplification and improve the write performance on the edge. From evaluation results, the write performance and random read performance of SardineDB have great advantages compared with existing distributed databases on the edge. As a result, SardineDB is very suitable for edge because it has high write performance, low GC burden and low write amplification.
Metaheuristic Algorithms: Theory and Applications

Metaheuristic is a collective concept of a series of intelligent strategies to enhance the efficiency of heuristic procedures. Metaheuristic algorithms are becoming an important part of modern optimization. A wide range of metaheuristic algorithms have emerged over the last two decades, and are becoming increasingly popular. This article presents a brief overview of the scientific research on new metaheuristic algorithms, as well as their modifications and hybridizations and its various fields of application. The results presented are limited to those proposed by scientists from the Bulgarian Academy of Sciences for the last 20 years.
Semantic Segmentation in Depth Data: A Comparative Evaluation of Image and Point Cloud Based Methods

The problem of semantic segmentation from depth images can be addressed by segmenting directly in the image domain or at 3D point cloud level. In this paper, we attempt for the first time to provide a study and experimental comparison of the two approaches. Through experiments on three datasets, namely SUN RGB-D, NYUdV2 and TICaM, we extensively compare various semantic segmentation algorithms, the input to which includes images and point clouds derived from them. Based on this, we offer analysis of the performance and computational cost of these algorithms that can provide guidelines on when each method should be preferred.
Development of a Linguistic Approach to the Problem of the Computer Electrocardiogram’s Classifications

Introduction. The linguistic approach, based on the transition from the observed cyclic signal to a sequence of symbols (codeword), which characterize the dynamics of indicators from cycle to cycle, makes it possible to use the procedures of mathematical linguistics to increase the reliability of decisions. The purpose of the article is to expand the diagnostic capabilities of the linguistic approach to the analysis and interpretation of electrocardiograms (ECG). Methods. Each ECG cycle is encoded with one of four symbols characterizing changes in two indicators: traditional (cycle duration) and original (symmetry of the repolarization area). Results. Based on the processing of real clinical data of verified patients and healthy volunteers, standards of patients with chronic coronary artery disease (СAD) and healthy patients. The standards are developed using computational procedures of mathematical linguistics – the Levenshtein distance, which is the minimum number of editing operations (insertion, deletion and replacement of a character), ensuring the transition from one word to another and the frequency of occurrence of substrings in the analyzed word. On the basis of these procedures, decision rules that have been developed allow making diagnostic decisions based on the Levenshtein distance to the standards and the frequency of occurrence of one-, two- and three-symbol patterns in code words. It was found that the combination of these two methods expands the diagnostic capabilities of the linguistic approach to the analysis and interpretation of the ECG. Conclusions. It has been shown that using of the developed decision rules makes it possible to increase the sensitivity and specificity of diagnostics even in cases when the ECG does not show traditional electrocardiological signs of myocardial ischemia.
Classification in Non-stationary Environments Using Coresets over Sliding Windows

In non-stationary environments, several constraints require algorithms to be fast, memory-efficient, and highly adaptable. While there are several classifiers of the family of lazy learners and tree classifiers in the streaming context, the application of prototype-based classifiers has not found much attention. Prototype-based classifiers however have some interesting characteristics, which are also useful in streaming environments, in particular being highly interpretable. Hence, we propose a new prototype-based classifier, which is based on Minimum Enclosing Balls over sliding windows. We propose this algorithm as a linear version as well as kernelized. Our experiments show, that this technique can be useful and is comparable in performance to another popular prototype-based streaming classifier – the Adaptive Robust Soft Learning Vector Quantization but with an additional benefit of having a configurable window size to catch rapidly changing drift and the ability to use the internal mechanics for drift detection.
Distributing intersection and union types with splits and duality (functional pearl)

Subtyping with intersection and union types is nowadays common in many programming languages. From the perspective of logic, the subtyping problem is essentially the problem of determining logical entailment : does a logical statement follow from another one? Unfortunately, algorithms for deciding subtyping and logical entailment with intersections, unions and various distributivity laws can be highly non-trivial. This functional pearl presents a novel algorithmic formulation for subtyping (and logical entailment) in the presence of various distributivity rules between intersections, unions and implications (i.e. function types). Unlike many existing algorithms which first normalize types and then apply a subtyping algorithm on the normalized types, our new subtyping algorithm works directly on source types. Our algorithm is based on two recent ideas: a generalization of subtyping based on the duality of language constructs called duotyping ; and splittable types , which characterize types that decompose into two simpler types. We show that our algorithm is sound, complete and decidable with respect to a declarative formulation of subtyping based on the minimal relevant logic B + . Moreover, it leads to a simple and compact implementation in under 50 lines of functional code.
Research on PMF Model Based on BP Neural Network Ensemble Learning Bagging and Fuzzy Clustering

Probability matrix factorization model can be used to solve the problem of high-dimensional sparsity of user and rating data in the recommender systems. However, most of the existing methods use the user to model the item rating, ignoring the relationship between the user and the item, so the accuracy of user-item rating prediction is still low. Therefore, this paper proposes a probabilistic matrix factorization model based on BP neural network ensemble learning, bagging, and fuzzy clustering. Firstly, the membership function of fuzzy clustering and the selection of cluster center are used to calculate the user-item rating matrix; secondly, BP neural network trains the user-item scoring matrix after clustering, further improving the accuracy of scoring prediction; finally, the bagging method in ensemble learning is introduced, which takes the number of user-item scores as the base learner, trains the base learner through BP neural network, and finally obtains the score prediction through the voting results, which improves the stability of the model. Compared with the existing PMF models, the root mean square error of the PMF model after fuzzy clustering is increased by 9.27% and 3.95%, and the average absolute error is increased by 21.14% and 1.11%, respectively; then, the performance of the first mock exam is introduced. The root mean square error of the ensemble method is increased by 4.02% and 0.42%, respectively, compared with the existing single model. Finally, the weights of BP neural network training based learner are introduced to improve the accuracy of the model, which also verifies the universality of the model.
Depth-Assisted Joint Detection Network For Monocular 3d Object Detection

In the past few years, monocular 3D object detection has attracted increasing attention due to the merit of low cost and wide range of applications. In this paper, a depth-assisted joint detection network (MonoDAJD) is proposed for monocular 3D object detection. Specifically, a consistency-aware joint detection mechanism is proposed to jointly detect objects in the image and depth map, and exploit the localization information from the depth detection stream to optimize the detection results. To obtain more accurate 3D bounding boxes, an orientation-embedded NMS is designed by introducing the orientation confidence prediction and embedding the orientation confidence into the traditional NMS. Experimental results on the widely used KITTI benchmark demonstrate that the proposed method achieves promising performance compared with the state-of-the-art monocular 3D object detection methods.
A minimalistic toolbox for extracting features from sport activity files

Nowadays, professional, as well as, amateur athletes are monitoring their sport activities/training using modern sport trackers. These devices allow athletes to capture many indicators of sport training, e.g. location of training, duration of training, distance of training, consumption of calories. Until recently, not enough devotion was given to those indicators that are not visible directly, but can be obtained as the result of extensive data analysis, e.g. information extracted from topographic maps, weather conditions, and interval data. In line with this, the present paper is dedicated to describing the new toolbox for extracting features hidden in sports activity files. The results of the extraction serve as entry points for deep data analysis, that allows us to build intelligent systems for training support.
Towards Robust Perception Depth Information For Collision Avoidance

Early detection of the obstacles and accurate estimation of the object’s distance helps avoid fatal accidents. However, the existing object detection ignores debris and other object classes not included in the training process. On the other hand, the driving area is monitored and recognized using active sensors like LiDAR, RADAR, but expensive. This study introduces a modified architecture to estimate the depth map based on an unsupervised learning framework. Furthermore, understanding the color-encoded depth map helps identify the risk of collision. The decoding of the color-encoded depth map provides information about the distance of the object. Thus, we presented an efficient and robust algorithm to predict a potential collision in real-time based on the estimated depth map using predefined threshold values. This approach emphasizes integrating the estimated depth map with the level of comprehension of the situation awareness to enhance the ability to recognize and process predicted uncertainties in the environment. The better results are achieved for modified architecture in terms of ARD, RMSE, RMSE (log), accuracy, and other evaluation metrics achieved lower but comparable results to the state-of-the-art techniques with a maximum depth of 80 meters. The integrated collision avoidance algorithm with the depth estimation architecture achieved a performance of 25 FPS on RTX 2080Ti GPU.
Research on Optimization of Cigarette Delivery Rout Based on Differential Evolution Algorithm

This paper presents a differential evolutionary algorithm to solve the tobacco distribution problem. On the basis of inheriting many advantages of the traditional genetic algorithm to solve the path optimization problem, the differential evolutionary algorithm has stronger robustness and more significant approximation effect. Due to the time and precision requirements of tobacco distribution problem, differential evolution algorithm has significant advantages in solving this problem.
Efficient Encoding of Constrained Block Codes

We present coding methods for generating <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\ell$ </tex-math></inline-formula> -symbol constrained codewords taken from a set, <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mathcal{\mathcal { S }}$ </tex-math></inline-formula> , of allowed codewords. In standard practice, the size of the set <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mathcal{S}$ </tex-math></inline-formula> , denoted by <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$M=|\mathcal{S}|$ </tex-math></inline-formula> , is truncated to an integer power of two, which may lead to a serious waste of capacity. We present an efficient and low-complexity coding method for avoiding the truncation loss, where the encoding is accomplished in two steps: first, a series of binary input (user) data is translated into a series of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$M$ </tex-math></inline-formula> -ary symbols in the alphabet <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mathbb{M}=\{0, \ldots, M-1\}$ </tex-math></inline-formula> . Then, in the second step, the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$M$ </tex-math></inline-formula> -ary symbols are translated into a series of admissible <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\ell$ </tex-math></inline-formula> -symbol words in <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mathcal{S}$ </tex-math></inline-formula> by using a small look-up table. The presented construction of Pearson codes and fixed-weight codes offers a rate close to capacity. For example, a 255B320B balanced code, where 255 source bits are translated into 32 10-bit balanced codewords, has a rate 0.1% below capacity.