Reviewerly/exHarmony huggingface dataset:
Fast Multiscale Neighbor Embedding

Dimension reduction (DR) computes faithful low-dimensional (LD) representations of high-dimensional (HD) data. Outstanding performances are achieved by recent neighbor embedding (NE) algorithms such as t -SNE, which mitigate the curse of dimensionality. The single-scale or multiscale nature of NE schemes drives the HD neighborhood preservation in the LD space (LDS). While single-scale methods focus on single-sized neighborhoods through the concept of perplexity, multiscale ones preserve neighborhoods in a broader range of sizes and account for the global HD organization to define the LDS. For both single-scale and multiscale methods, however, their time complexity in the number of samples is unaffordable for big data sets. Single-scale methods can be accelerated by relying on the inherent sparsity of the HD similarities they involve. On the other hand, the dense structure of the multiscale HD similarities prevents developing fast multiscale schemes in a similar way. This article addresses this difficulty by designing randomized accelerations of the multiscale methods. To account for all levels of interactions, the HD data are first subsampled at different scales, enabling to identify small and relevant neighbor sets for each data point thanks to vantage-point trees. Afterward, these sets are employed with a Barnes-Hut algorithm to cheaply evaluate the considered cost function and its gradient, enabling large-scale use of multiscale NE schemes. Extensive experiments demonstrate that the proposed accelerations are, statistically significantly, both faster than the original multiscale methods by orders of magnitude, and better preserving the HD neighborhoods than state-of-the-art single-scale schemes, leading to high-quality LD embeddings. Public codes are freely available at https://github.com/cdebodt.
Hidden Markov model‐based control for networked fuzzy Markov jump systems against randomly occurring multichannel attacks

Abstract This paper gives full consideration to the hidden Markov model‐based control problem for nonlinear Markov jump systems under multichannel random attacks via Takagi–Sugeno fuzzy model. The system state is transmitted to controller through the multiple channels in which each channel obtains transmission permission based on a dispatcher and suffers from random attacks described by the Bernoulli variables. The hidden Markov model is utilized to describe the asynchronous mode phenomenon between controller and system. By choosing a compatible Lyapunov function, some sufficient conditions are developed to achieve stochastic stability of the closed‐loop system with a prescribed performance. In the end, two examples are provided to illuminate the correctness of the devised technique.
A Weighted Mean Absolute Error Metric for Image Quality Assessment

Pixel-wise image quality assessment (IQA) algorithms, such as mean square error (MSE), mean absolute error (MAE) and peak signal-to-noise ratio (PSNR) correlate well with perceptual quality when dealing with images sharing the same distortion type but not well when processing images in different distortion types, which is inconsistent with human visual system (HVS). Although a large number of metrics based on image error has been proposed, there are still difficulties and limitations. To solve this problem, a full reference image quality assessment (FR-IQA) method based on MAE is proposed in this paper. The metric divides the image error (difference between distorted image and reference image) map into smooth region and texture-edge region, calculates their mean values respectively, and then gives them different weights considering the masking effect. The key innovation of this paper is to propose a distortion significance measurement, which is a visual quality coefficient that can effectively indicate the influence of different distortion types on perceptual quality and unify them with HVS. The segmented image error maps are weighted by the distortion significance coefficient. The experimental results on four largest benchmark databases show that the most of the distortions are successfully evaluated and the results are consistent with HVS.
Reliable Counterfactual Explanations for Autoencoder based Anomalies

Autoencoders have been used successfully for tackling the problem of anomaly detection in an unsupervised setting, and are often known to give better results than traditional approaches such as clustering and subspace-based (linear) methods. A data point is flagged as anomalous by an autoencoder if its reconstruction loss is higher than an appropriate threshold. However, as with other deep learning models, the increased accuracy offered by autoencoders comes at the cost of interpretability. Explaining an autoencoder’s decision to flag a particular data point as an anomaly is greatly important, since a human-friendly explanation would be necessary for a domain expert tasked with evaluating the model’s decisions. We consider the problem of finding counterfactual explanations for autoencoder anomalies, which address the question of what needs to be minimally changed in a given anomalous data point to make it non-anomalous. We present an algorithm that generates a diverse set of proximate counterfactual explanations for a given autoencoder anomaly. We also introduce the notion of reliability of a counterfactual, and present techniques to find reliable counterfactual explanations.
Mental and Physical Health Management System Using ML, Computer Vision and IoT Sensor Network

Previously, healthcare management systems were particularly focused on physical health and disregarded the mental health. With technology that encompass all spheres of life, the benefits of technology in healthcare remain unfathomable. A person's emotion, body temperature and heart rate can provide insightful information on both mind and body. This paper demonstrates a complete healthcare management system that considers both physical as well as mental health. IoT sensor network and Arduino UNO is used to collect a person's body temperature and heart rate and simultaneously stored in a database. The paper also includes an emotion recognition unit that is built using the MiniXception convolutional neural network architecture to keep a track of the person's emotion. A natural language processing chatbot has been employed to enable a virtual doctor for the user and help the user to provide a preliminary diagnosis. Furthermore, the tools like HTMI5 and CSS have been employed to build the user interface. The data obtained is then stored using MySQL database service. The results of this implementation indicates that the proposed system can reduce the workload for doctors, to be more aware of their physical and mental health and lastly the proposed methodology will make healthcare to be more accessible.
NetKI: A kirchhoff index based statistical graph embedding in nearly linear time

Recent advancements in learning from graph-structured data have shown promising results on the graph classification task. However, due to their high time complexities, making them scalable on large graphs, with millions of nodes and edges, remains a challenge. In this paper, we propose NetKI, an algorithm to extract sparse representation from a given graph with n nodes and m edges in O(m∊-2log4n) time. Our approach follows the notion of Kirchhoff index that encodes the structure of the graph by estimating effective resistance - relying on this approach yields nearly linear time graph representation method that allows scalability on sufficiently large graphs. Through extensive experiments, we show that NetKI provides improved results in terms of running time on large networks and the classification accuracy is within range 2% from the state-of-the-art results.
<scp>CSS</scp> : Handling imbalanced data by improved clustering with stratified sampling

Summary The traditional support vector machine technique (SVM) has drawbacks in dealing with imbalanced data. To address this issue, in this paper we propose an algorithm of improved clustering with stratified sampling technique (CSS) to improve the classification performance of SVMs on imbalanced datasets. Instead of applying a single type of sampling method as used in the literature, our algorithm treats different type of classes with different sampling methods. For minority classes, the algorithm uses oversampling method by adding noise which obeys normal distribution around every support vector to generate new samples. For majority classes, samples are first divided into different clusters by applying first the improved clustering by fast search to find of density peaks (CFSFDP) to obtain latent structure information in each majority class and then stratified sampling method is applied to extract samples from each subcluster of the majority class. Moreover, we further extend this method into an ensemble classifiers that use multiple base SVM classifiers for prediction. The experimental results of classification on several imbalanced classification datasets show that our CSS is more effective than the state‐of‐the‐art sampling methods.
<scp>HFS‐LightGBM</scp>: A machine learning model based on hybrid feature selection for classifying <scp>ICU</scp> patient readmissions

Abstract Compared to patients readmitted to general wards, readmitted patients in the intensive care unit (ICU) are exposed to higher mortality rates and prolonged hospital stays. Moreover, the readmission of ICU patients brings pressing challenges for ICU management. Most models are devoted to identifying the risk factors and developing classification models that can predict whether ICU patients will be readmitted. Though these models are prominent, they do not provide estimates for the frequency of readmissions. This paper establishes a prediction model, hybrid feature selection‐LightGBM (HFS‐LightGBM), to evaluate the probability and frequency of ICU patient readmissions empirically. In terms of feature selection, a hybrid feature selection (HFS) algorithm for LightGBM combines the filter and wrapper methods. Pearson's correlation coefficient is employed in the filter procedure. Then we adopt the targeted LightGBM classifier along with the recursive feature elimination and cross‐validated (RFECV) to produce the optimal feature subset. Additionally, the hyperparameters of the HFS‐LightGBM are optimized. The HFS‐LightGBM is employed on the real‐world ICU dataset containing 1722 patients' electronic health records. This model outperforms the current prevailing readmission models. The identified frequency can assist doctors in making specific interventions for patients to reduce the ICU readmission rate.
KS@LTH at SemEval-2020 Task 12: Fine-tuning Multi- and Monolingual Transformer Models for Offensive Language Detection

This paper describes the KS@LTH system for SemEval-2020 Task 12 OffensEval2: Multilingual Offensive Language Identification in Social Media.We compare mono-and multilingual models based on fine-tuning pre-trained transformer models for offensive language identification in Arabic, Greek, English and Turkish.For Danish, we explore the possibility of fine-tuning a model pre-trained on a similar language, Swedish, and additionally also cross-lingual training together with English.Overall we find that monolingual models achieve higher macro-averaged F1 score.With cross-lingual training of Danish together with English, we achieve better results than by training on the small Danish dataset alone.For Arabic, Danish, English, Greek, and Turkish, we obtained macro-averaged F1 scores of 0.890, 0.775, 0.916, 0.848, and 0.
Secure Massively Parallel Computation for Dishonest Majority

This work concerns secure protocols in the massively parallel computation (MPC) model, which is one of the most widely-accepted models for capturing the challenges of writing protocols for the types of parallel computing clusters which have become commonplace today (MapReduce, Hadoop, Spark, etc.). Recently, the work of Chan et al. (ITCS ’20) initiated this study, giving a way to compile any MPC protocol into a secure one in the common random string model, achieving the standard secure multi-party computation definition of security with up to 1/3 of the parties being corrupt. We are interested in achieving security for much more than 1/3 corruptions. To that end, we give two compilers for MPC protocols, which assume a simple public-key infrastructure, and achieve semi-honest security for all-but-one corruptions. Our first compiler assumes hardness of the learning-with-errors (LWE) problem, and works for any MPC protocol with “short” output—that is, where the output of the protocol can fit into the storage space of one machine, for instance protocols that output a trained machine learning model. Our second compiler works for any MPC protocol (even ones with a long output, such as sorting) but assumes, in addition to LWE, indistinguishability obfuscation and a circular secure variant of threshold FHE. Both protocols allow the attacker to choose corrupted parties based on the trusted setup, an improvement over Chan et al., whose protocol requires that the CRS is chosen independently of the attacker’s choices.
Customized frequent patterns mining algorithms for enhanced Top-Rank-K frequent pattern mining

Mining frequent patterns (FP) is an essential task in data mining. The parameter required for this task is typically the minimum support threshold. Tuning this parameter to a suitable value is a difficult task, especially for inexperienced users. Thus, the Top-Rank-K frequent patterns mining problem was introduced. It requires the user to input an easily-evaluated parameter, K, in order to obtain the set of all frequent patterns from the most frequent to the Kth rank of frequency. In this paper, we customize three general Frequent Pattern Mining (FPM) algorithms, namely FIN, PrePost, and PrePost+, to develop specialized Top-Rank-K FP mining algorithms: TK_FIN, TK_PrePost, and TK_PrePost+. The Dynamic Minimum Support Raising strategy is applied on these algorithms to ensure efficiency. Experimentally, we evaluate the performance of these algorithms against an original, efficient, Top-Rank-K algorithm, BTK. The three presented algorithms perform 90% better than BTK in most of the experiments, with respect to runtime. Between the three Top-Rank-K FPM algorithms we present, TK_FIN achieves the best performance from both runtime and memory consumption perspectives.
Simulating Aggregation Algorithms for Empirical Verification of Resilient and Adaptive Federated Learning

Federated learning (FL) environments are dynamic: Users are recruited to participate, may drop out of participation, may vary in their availability and speed, and may suffer bandwidth variations. As such, production environments demand a resilient and adaptive design. Insisting on synchronous FL, with potentially millions or hundreds of thousands of clients, will lead to various issues-bursts in processing and communication loads, complicated procedures to handle laggards, and special protocols to manage client failures. In this paper, we focus on asynchronous, adaptive, and resilient operating environments for FL. We develop a simulation scheme and a set of associated aggregation algorithms as a method for investigating the soundness of asynchronous and adaptive system designs and operational principles. Our simulation model can capture the statistical impact of FL clients' intermittent operations. We also propose an aggregation algorithm usable when clients' participation and refresh rates vary. We suggest an approach for identifying valid and adaptive operating configurations including those that can be used to trade-off computational load and convergence speeds in FL programs.
An Estimation of Distribution Algorithm With Filtering and Learning

Estimation of distribution algorithm (EDA) is an efficient population-based stochastic search technique. Since it was proposed, many attempts have been made to improve its performance in the context of nonlinear continuous optimization. However, the success of EDA depends on the accuracy of modeling, the effectiveness of sampling, and the ability of exploration. An effective EDA often needs to take some measures to adjust the model and to guide sampling. In this article, we propose a novel EDA which applies the idea of Kalman filtering to revise the modeling data and a learning strategy to improve sampling. The filtering scheme modifies the modeling data set using an estimation error matrix based on historic solution data. During the sampling process, the learning strategy determines the region to sample next based on the sampling outcomes so far, instead of completely random sampling. The proposed EDA also employs a multivariate probabilistic model based on copula function and can quickly reach the promising area in which the optimal solution is likely to be located. A collection of general benchmark functions are used to test the performance of the proposed algorithm. Computational experiments show that the EDA is effective. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —In many process industries, there exist black-box operation optimization problems and large-scale nonlinear optimization problems with variable coupling. For these problems, it is difficult to establish mechanism models between input and output. However, real-time data can be measured from the system through sensors. We can utilize this process information to optimize the system so as to attain the desired objective. In this article, we propose a novel estimation of distribution algorithm (EDA) which applies a filtering scheme to revise the modeling data and a learning strategy to improve sampling, which can solve the problems with the characteristics of nonlinearity, variable coupling, and large scale. Computational experiments show that the EDA is effective. In the future, the proposed algorithm can be applied to some practical optimization problems such as operation optimization in blast furnace, which is considered as a continuous production process with variable coupling. The algorithm has the potential to help optimizing the process control parameters.
ECC Coprocessor Over a NIST Prime Field Using Fast Partial Montgomery Reduction

Montgomery reduction is a well-known optimization technique for modular reduction over prime fields. However, it has rarely been used for national institute of standards and technology (NIST) prime fields, because the special structure of NIST primes enables fast reduction that requires fewer computations than Montgomery reduction. In this study, we rediscover the Montgomery reduction for NIST primes and propose a new modular reduction method by applying partial modular reduction to Montgomery reduction. Using this new modular reduction method and other optimization techniques, such as pipelining and parallel processing, we propose an efficient elliptic curve cryptography (ECC) coprocessor over NIST prime fields. The implementation results indicate that the proposed coprocessor can perform one elliptic curve point multiplication in 0.055 ms with only 194.7 k gate counts. This is a considerably higher speed per area compared to previous research.
Dynamic Multi-objective Scheduling of Microservices in the Cloud

For many applications, a microservices architecture promises better performance and flexibility compared to a conventional monolithic architecture. In spite of the advantages of a microservices architecture, deploying microservices poses various challenges for service developers and providers alike. One of these challenges is the efficient placement of microservices on the cluster nodes. Improper allocation of microservices can quickly waste resource capacities and cause low system throughput. In the last few years, new technologies in orchestration frameworks, such as the possibility of multiple schedulers for pods in Kubernetes, have improved scheduling solutions of microservices but using these technologies needs to involve both the service developer and the service provider in the behavior analysis of workloads. Using memory and CPU requests specified in the service manifest, we propose a general microservices scheduling mechanism that can operate efficiently in private clusters or enterprise clouds. We model the scheduling problem as a complex variant of the knapsack problem and solve it using a multi-objective optimization approach. Our experiments show that the proposed mechanism is highly scalable and simultaneously increases utilization of both memory and CPU, which in turn leads to better throughput when compared to the state-of-the-art.
Model Checking Algorithms for Hyperproperties (Invited Paper)

Hyperproperties generalize trace properties by expressing relations between multiple computations. Hyperpropertes include policies from information-flow security, like observational determinism or non-interference, and many other system properties including promptness and knowledge. In this paper, we give an overview on the model checking problem for temporal hyperlogics. Our starting point is the model checking algorithm for HyperLTL, a reduction to B\"uchi automata emptiness. This basic construction can be extended with propositional quantification, resulting in an algorithm for HyperQPTL. It can also be extended with branching time, resulting in an algorithm for HyperCTL*. However, it is not possible to have both extensions at the same time: the model checking problem of HyperQCTL* is undecidable. An attractive compromise is offered by MPL[E], i.e., monadic path logic extended with the equal-level predicate. The expressiveness of MPL[E] falls strictly between that of HyperCTL* and HyperQCTL*. MPL[E] subsumes both HyperCTL* and HyperKCTL*, the extension of HyperCTL* with the knowledge operator. We show that the model checking problem for MPL[E] is still decidable.
Understanding the impact of mistakes on background regions in crowd counting

In crowd counting we often observe wrong predictions on image regions not containing any person. But how often do these mistakes happen and how much do they affect the overall performance? In this paper we analyze this problem in depth and present an extensive analysis on five of the most important crowd counting datasets. We present this analysis in two parts. First, we quantify the number of mistakes made. Our results show that (i) mistakes on back-ground are substantial and they are responsible for 18-49% of the total error, (ii) models do not generalize well to different kinds of backgrounds and perform poorly on completely background images, and (iii) models make many more mistakes than those captured by the standard Mean Absolute Error (MAE) metric, as counting on background compensates substantially for misses on foreground. And second, we quantify the performance change gained by helping the model better deal with this problem. We enrich a popular crowd counting network with a segmentation branch trained to suppress background predictions. This simple addition (i) reduces background error by 10-83%, (ii) reduces fore-ground error by up to 26% and (iii) improves overall crowd counting performance up to 20%. When compared against the literature, this simple technique achieves very competitive results on all datasets, showing the importance of tack-ling the background problem.