Reviewerly/exHarmony huggingface dataset:
Blockchain. Today Applicability and Implications

Blockchain is an emergent technology with very rapid evolution that seems to radically reshape industry, economy and society [2]. It seems that blockchain technology triggers the beginning of the second era of digital economy. First era of digital economy is the result of the convergence of computing and communications technologies, meanwhile its second era tends to be a combination of computer science, mathematics, cryptography and behavioral economics [10]. It started back in 2008 when it was introduced for the bone structure of cryptocurrencies by a person or a group of people known for the name Satoshi Nakamoto. This paper aims to be an overview of what Blockchain currently involves, also it discusses its potential applications in different industries and its implications for society and economy in the context of next generation of internet.
Fair Outlier Detection

An outlier detection method may be considered fair over specified sensitive attributes if the results of outlier detection are not skewed towards particular groups defined on such sensitive attributes. In this paper, we consider, for the first time to our best knowledge, the task of fair outlier detection. Our focus is on the task of fair outlier detection over multiple multi-valued sensitive attributes (e.g., gender, race, religion, nationality, marital status etc.), one that has broad applications across web data scenarios. We propose a fair outlier detection method, FairLOF, that is inspired by the popular LOF formulation for neighborhood-based outlier detection. We outline ways in which unfairness could be induced within LOF and develop three heuristic principles to enhance fairness, which form the basis of the FairLOF method. Being a novel task, we develop an evaluation framework for fair outlier detection, and use that to benchmark FairLOF on quality and fairness of results. Through an extensive empirical evaluation over real-world datasets, we illustrate that FairLOF is able to achieve significant improvements in fairness at sometimes marginal degradations on result quality as measured against the fairness-agnostic LOF method.
Linear time construction of indexable founder block graphs

We introduce a compact pangenome representation based on an optimal segmentation concept that aims to reconstruct founder sequences from a multiple sequence alignment (MSA). Such founder sequences have the feature that each row of the MSA is a recombination of the founders. Several linear time dynamic programming algorithms have been previously devised to optimize segmentations that induce founder blocks that then can be concatenated into a set of founder sequences. All possible concatenation orders can be expressed as a founder block graph. We observe a key property of such graphs: if the node labels (founder segments) do not repeat in the paths of the graph, such graphs can be indexed for efficient string matching. We call such graphs segment repeat-free founder block graphs. We give a linear time algorithm to construct a segment repeat-free founder block graph given an MSA. The algorithm combines techniques from the founder segmentation algorithms (Cazaux et al. SPIRE 2019) and fully-functional bidirectional Burrows-Wheeler index (Belazzougui and Cunial, CPM 2019). We derive a succinct index structure to support queries of arbitrary length in the paths of the graph.Experiments on an MSA of SARS-CoV-2 strains are reported. An MSA of size 410× 29811 is compacted in one minute into a segment repeat-free founder block graph of 3900 nodes and 4440 edges. The maximum length and total length of node labels is 12 and 34968, respectively. The index on the graph takes only 3% of the size of the MSA.
Robust Reasoning Over Heterogeneous Textual Information for Fact Verification

Automatic fact verification (FV) based on artificial intelligence is considered as a promising approach which can be used to identify misinformation distributed on the web. Even though previous FV using deep learning have made great achievements in single dataset (e.g., FEVER), the trained systems are unlikely to be capable of extracting evidence from heterogeneous web-sources and validating claims in accordance with evidence found on the Internet. Nevertheless, the heterogeneity covers abundant semantic information, which will help FV system identify misinformation in a more accurate way. The current work is the first attempt to make the combination of knowledge graph (KG) and graph neural network (GNN) to enhance the robustness of FV systems for heterogeneous information. As a result, it can be generalized to multi-domain datasets after training on a sufficient single one. To make information update and aggregate well on the collaborative graph, the present study proposes a double graph attention network (DGAT) framework which recursively propagates the embeddings from a node's neighbors to refine the node's embedding as well as applies an attention mechanism to classify the importance of the neighbors. We train and evaluate our system on FEVER, a single and benchmark dataset for FV, and then re-evaluate our system on UKP Snopes Corpus, a new richly annotated corpus for FV tasks on the basis of heterogeneous web sources. According to experimental results, although DGAT has no excellent advantages in a single dataset, it shows outstanding performance in more realistic and multi-domain datasets. Moreover, the current study also provides a feasible method for deep learning to have the ability to infer heterogeneous information robustly.
ALO-NMF: Accelerated Locality-Optimized Non-negative Matrix Factorization

Non-negative Matrix Factorization (NMF) is a key kernel for unsupervised dimension reduction used in a wide range of applications, including graph mining, recommender systems and natural language processing. Due to the compute-intensive nature of applications that must perform repeated NMF, several parallel implementations have been developed. However, existing parallel NMF algorithms have not addressed data locality optimizations, which are critical for high performance since data movement costs greatly exceed the cost of arithmetic/logic operations on current computer systems. In this paper, we present a novel optimization method for parallel NMF algorithm based on the HALS (Hierarchical Alternating Least Squares) scheme that incorporates algorithmic transformations to enhance data locality. Efficient realizations of the algorithm on multi-core CPUs and GPUs are developed, demonstrating a new Accelerated Locality-Optimized NMF (ALO-NMF) that obtains up to 2.29x lower data movement cost and up to 4.45x speedup over existing state-of-the-art parallel NMF algorithms.
New Optimal Sets of Perfect Polyphase Sequences Based on Circular Florentine Arrays

Families of periodic sequences with some desirable auto-correlation and cross-correlation properties have applications in communications and radar systems for identification, synchronization, ranging, or interference mitigation. A sequence is said to be a polyphase sequence if all the coordinates are n-th roots of unity. In this paper, we develop a connection between generalised Frank sequences and well-studied combinatorial objects: circular Florentine arrays. From this connection, we can derive an optimal set of perfect polyphase sequences with respect to the Sarvate bound. Furthermore, the size of the optimal set is determined by the existence of circular Florentine arrays. As a result, the size of an optimal set of perfect sequences is increased, compared with the previous results, where the size depends on the smallest prime divisor of the period.
Video Snow Removal Based on Self-Adaptation Snow Detection and Patch-Based Gaussian Mixture Model

Video desnowing has become a challenging research topic in computer vision in recent years. Existing methods cannot remove most of the snow in heavy snow scenes and will cause the deformation of moving objects when used for snowy videos that include moving objects. These methods have poor generalizability, exhibiting poor performance when removing snow from videos with different resolutions. In this paper, we propose a new video snow removal method based on self-adaptation snow detection and a patch-based Gaussian mixture model (VSRSG). First, an optical flow estimation method and a support vector machine (SVM) are used to detect snowflakes, and a self-adaptation threshold process is used to remove dense snowflakes in the snowflake detection map to obtain a sparse snowflake detection map. Then, a patch-based Gaussian mixture model (PBGMM), which can remove moving objects and both sparse and dense snowflakes from videos and restore a clear video background, is applied for background modeling. A Markov random field (MRF) and self-adaptation threshold processing are used to extract sparse snowflakes and moving objects and combine them with the background to form an input video without dense snowflakes. Finally, a similar block matching method is employed to fill in the detected snowflake pixels with the information from adjacent frames to remove the sparse snowflakes in the near range. This method can also remove snowflakes in front of moving objects. Experiments show that the proposed method can simultaneously remove sparse snowflakes, dense snowflakes and snowflakes in front of moving objects and outperforms other state-of-the-art methods.
Supervised Machine-Learning Algorithms in Real-time Prediction of Hypotensive Events

Hypotension is common in critically ill patients. Early prediction of hypotensive events in the Intensive Care Units (ICUs) allows clinicians to pre-emptively treat the patient and avoid possible organ damage. In this study, we investigate the performance of various supervised machine-learning classification algorithms along with a real-time labeling technique to predict acute hypotensive events in the ICU. It is shown that logistic regression and SVM yield a better combination of specificity, sensitivity and positive predictive value (PPV). Logistic regression is able to predict 85% of events within 30 minutes of their onset with 81% PPV and 96% specificity, while SVM results in 96% specificity, 83% sensitivity and 82% PPV. To further reduce the false alarm rate, we propose a high-level decision-making algorithm that filters isolated false positives identified by the machine-learning algorithms. By implementing this technique, 24% of the false alarms are filtered. This saves 21 hours of medical staff time through 2,560 hours of monitoring and significantly reduces the disturbance caused by alarming monitors.
Approximation Algorithms for <i>D</i>-optimal Design

Experimental design is a classical statistics problem, and its aim is to estimate an unknown vector from linear measurements where a Gaussian noise is introduced in each measurement. For the combinatorial experimental design problem, the goal is to pick a subset of experiments so as to make the most accurate estimate of the unknown parameters. In this paper, we will study one of the most robust measures of error estimation—the D-optimality criterion, which corresponds to minimizing the volume of the confidence ellipsoid for the estimation error. The problem gives rise to two natural variants depending on whether repetitions of experiments are allowed or not. We first propose an approximation algorithm with a 1/e-approximation for the D-optimal design problem with and without repetitions, giving the first constant-factor approximation for the problem. We then analyze another sampling approximation algorithm and prove that it is asymptotically optimal. Finally, for D-optimal design with repetitions, we study a different algorithm proposed by the literature and show that it can improve this asymptotic approximation ratio. All the sampling algorithms studied in this paper are shown to admit polynomial-time deterministic implementations.
An IF-RCNN Algorithm for Pedestrian Detection in Pedestrian Tunnels

In order to deal with the problems of background mixing, pedestrian blur and pedestrian multi-scale in pedestrian tunnels, we proposed an improved faster region based convolution neural network (IF-RCNN) pedestrian detection method, which uses deep CNN to automatically extract features from pictures instead of traditional manual design features.In this paper, an improved region proposal network (RPN) structure is proposed to solve the multi-scale problem of pedestrians in tunnel.The anchor size in RPN network is further improved in the face of pedestrian images in tunnels with small total pixels.Meanwhile, feature fusion technology is introduced to the algorithm to output the features of different convolution layers.The image is fused to enhance the detection performance of blurred and occluded pedestrians in tunnel.Experimental results show that IF-RCNN algorithm has better detection performance in tunnel data set and VOC2007 data set.
Automated Filtering of Eye Gaze Metrics from Dynamic Areas of Interest

Eye-tracking experiments usually involves areas of interests (AOIs) for the analysis of eye gaze data as they could reveal potential cognitive load, and attentional patterns yielding interesting results about participants. While there are tools to define AOIs to extract eye movement data for the analysis of gaze measurements, they may require users to draw boundaries of AOIs on eye tracking stimuli manually or use markers to define AOIs in the space to generate AOI-mapped gaze locations. In this paper, we introduce a novel method to dynamically filter eye movement data from AOIs for the analysis of advanced eye gaze metrics. We incorporate pre-trained object detectors for offline detection of dynamic AOIs in dynamic eye-tracking stimuli such as video streams. We present our implementation and evaluation of object detectors to find the best object detector to be integrated in a real-time eye movement analysis pipeline to filter eye movement data that falls within the polygonal boundaries of detected dynamic AOIs. Our results indicate the utility of our method by applying it to a publicly available dataset.
Surface chemical trapping of optical cycling centers

Quantum information processors are proposed, based on optical cycling centers trapped attached to a surface.
Big data audit based on financial sharing service model

As the COVID-19 epidemic continues to spread, the government has managed to prevent people from gathering. The audit work can only be carried out through the network, which puts forward higher requirements for the accuracy and effectiveness of the audit work. Under the background of the continuous development of big data and other information technologies, big data audit has gained important technical support and played an increasingly important role. Units at all levels gradually attach importance to the enterprise management mode based on the financial sharing service mode. This paper analyzes the related problems of big data audit under the financial sharing service mode, involving big data flow, big data preprocessing, big data audit process and other issues, in order to provide useful reference for the implementation of big data audit by using the financial sharing service mode under the influence of COVID-19.
On Frequency Estimation and Detection of Heavy Hitters in Data Streams

A stream can be thought of as a very large set of data, sometimes even infinite, which arrives sequentially and must be processed without the possibility of being stored. In fact, the memory available to the algorithm is limited and it is not possible to store the whole stream of data which is instead scanned upon arrival and summarized through a succinct data structure in order to maintain only the information of interest. Two of the main tasks related to data stream processing are frequency estimation and heavy hitter detection. The frequency estimation problem requires estimating the frequency of each item, that is the number of times or the weight with which each appears in the stream, while heavy hitter detection means the detection of all those items with a frequency higher than a fixed threshold. In this work we design and analyze ACMSS, an algorithm for frequency estimation and heavy hitter detection, and compare it against the state of the art ASketch algorithm. We show that, given the same budgeted amount of memory, for the task of frequency estimation our algorithm outperforms ASketch with regard to accuracy. Furthermore, we show that, under the assumptions stated by its authors, ASketch may not be able to report all of the heavy hitters whilst ACMSS will provide with high probability the full list of heavy hitters.
Anomaly detection based on multivariate data for the aircraft hydraulic system

It is almost impossible to detect the health status of the aircraft hydraulic system via a single variable, because of the complexity and the coupling relationship between components of the system. To serve the purpose, a novel anomaly detection method considering multivariate monitoring data is proposed in this article. The unsupervised auto-encoder model with the long short-term memory layers is used to reconstruct multivariate time series data, and a new comprehensive decision-making index based on two conventional ones is proposed to measure the difference between the observation and the reconstruction. Then, the health threshold of the decision-making index can be calculated by the kernel density estimation. The flight data are divided into several samples, and the anomaly detection of each sample is determined by the specific rule. The healthy status of each flight is determined by voting based on the detection results of all samples included in the flight. The performance of the proposed method is validated on the real continuous monitoring data, and the results confirm that the proposed model overcomes the problems of multistage and multivariate parameters in the anomaly detection of the aircraft system and improves the detection efficiency.
The Tensorflow Machine Learning Library

To recap what was stated in an earlier chapter, Python has a huge variety of machine learning libraries that can be implemented in a program. These libraries serve various purposes—mathematical, scientific, graphical, and so on. Depending on the nature and the need of the program we are developing, we can call these libraries into our program.
Mitigation of Privacy Threats due to Encrypted Traffic Analysis through a Policy-Based Framework and MUD Profiles

It has been proven in research literature that the analysis of encrypted traffic with statistical analysis and machine learning can reveal the type of activities performed by a user accessing the network, thus leading to privacy risks. In particular, different types of traffic (e.g., skype, web access) can be identified by extracting time based features and using them in a classifier. Such privacy attacks are asymmetric because a limited amount of resources (e.g., machine learning algorithms) can extract information from encrypted traffic generated by cryptographic systems implemented with a significant amount of resources. To mitigate privacy risks, studies in research literature have proposed a number of techniques, but in most cases only a single technique is applied, which can lead to limited effectiveness. This paper proposes a mitigation approach for privacy risks related to the analysis of encrypted traffic which is based on the integration of three main components: (1) A machine learning component which proactively analyzes the encrypted traffic in the network to identify potential privacy threats and evaluate the effectiveness of various mitigation techniques (e.g., obfuscation), (2) a policy based component where policies are used to enforce privacy mitigation solutions in the network and (3) a network node profile component based on the Manufacturer Usage Description (MUD) standard to enable changes in the network nodes in the cases where the first two components are not effective in mitigating the privacy risks. This paper describes the different components and how they interact in a potential deployment scenario. The approach is evaluated on the public dataset ISCXVPN2016 and the results show that the privacy threat can be mitigated significantly by removing completely the identification of specific types of traffic or by decreasing the probability of their identification as in the case of VOIP by 50%, Chat by 40% and Browsing by 33%, thus reducing significantly the privacy risk.
Dividing the Ontology Alignment Task with Semantic Embeddings and Logic-Based Modules

Large ontologies still pose serious challenges to state-of-the-art ontology alignment systems. In this paper we present an approach that combines a neural embedding model and logic-based modules to accurately divide an input ontology matching task into smaller and more tractable matching (sub)tasks. We have conducted a comprehensive evaluation using the datasets of the Ontology Alignment Evaluation Initiative. The results are encouraging and suggest that the proposed method is adequate in practice and can be integrated within the workflow of systems unable to cope with very large ontologies.
An Efficient Accelerator Design Methodology For Deformable Convolutional Networks

Deformable convolutional networks have demonstrated outstanding performance in object recognition tasks with an effective feature extraction. Unlike standard convolution, the deformable convolution decides the receptive field size using dynamically generated offsets, which leads to an irregular memory access. Especially, the memory access pattern varies both spatially and temporally, making static optimization ineffective. Thus, a naive implementation would lead to an excessive memory footprint. In this paper, we present a novel approach to accelerate deformable convolution on FPGA. First, we propose a novel training method to reduce the size of the receptive field in the deformable convolutional layer without compromising accuracy. By optimizing the receptive field, we can compress the maximum size of the receptive field by 12.6 times. Second, we propose an efficient systolic architecture to maximize its efficiency. We then implement our design on FPGA to support the optimized dataflow. Experimental results show that our accelerator achieves up to 17.25 times speedup over the state-of-the-art accelerator.
Environment Prediction from Sparse Samples for Robotic Information Gathering

Robots often require a model of their environment to make informed decisions. In unknown environments, the ability to infer the value of a data field from a limited number of samples is essential to many robotics applications. In this work, we propose a neural network architecture to model these spatially correlated data fields based on a limited number of spatially continuous samples. Additionally, we provide a method based on biased loss functions to suggest future areas of exploration to minimize reconstruction error. We run simulated robotic information gathering trials on both the MNIST hand written digits dataset and a Regional Ocean Modeling System (ROMS) ocean dataset for ocean monitoring. Our method outperforms Gaussian process regression in both environments for modeling the data field and action selection.
Quantum Borrmann effect for dissipation-immune photon-photon correlations

We study theoretically the second-order correlation function $g^{(2)}(t)$ for photons transmitted through a periodic Bragg-spaced array of superconducting qubits, coupled to a waveguide. We demonstrate that photon bunching and anti-bunching persist much longer than both radiative and non-radiative lifetimes of a single qubit. The photon-photon correlations become immune to non-radiative dissipation due to the Borrmann effect, that is a strongly non-Markovian collective feature of light-qubit coupling inherent to the Bragg regime. This persistence of quantum correlations opens new avenues for enhancing the performance of setups of waveguide quantum electrodynamics.