Reviewerly/exHarmony huggingface dataset:
DTS: A Simulator to Estimate the Training Time of Distributed Deep Neural Networks

Deep Neural Networks (DNNs) process big datasets achieving high accuracy on incredibly complex tasks. However, this progress has led to a scalability impasse, as DNNs require massive amounts of processing power and local memory to be trained, making them impossible or impractical to be used on a single device. This situation has led to the design of distributed training architectures, where the DNN and the training data can be split among multiple processors. How to choose the appropriate distributed training architecture, however, remains an open question. To help bring insights into this debate, in this work we design a Distributed Training Simulator (DTS) that estimates the training time of a DNN in a distributed architecture through a mathematical model of the distributed architecture and resource-allocation heuristics. We illustrate the power of the proposed DTS through the implementation of five different distributed architectures, Pipeline Learning, Federated Learning, Split Learning, Parallel Split Learning, and Federated Split Learning, and we validate the accuracy of the training estimates using three different datasets of varying complexity and two different DNNs. Finally, we present a trade-off analysis to demonstrate the coherence of DTS estimates for diverse high-performance computing scenarios by comparing these estimates with the behaviors of a real computer cluster.
Just-in-Time Aggregation for Federated Learning

The increasing number and scale of federated learning (FL) jobs necessitates resource efficient scheduling and management of aggregation to make the economics of cloud-hosted aggregation work. Existing FL research has focused on the design of FL algorithms and optimization, and less on aggregation efficacy. In this paper, we propose a new FL aggregation paradigm - “just-in-time” (JIT) aggregation that leverages unique properties of FL jobs, especially the periodicity of model updates, to defer aggregation as much as possible and free compute resources for other FL jobs or other datacenter workloads. We describe a novel way to prioritize FL jobs for aggregation, and demonstrate using multiple datasets, models and FL aggregation algorithms that our techniques can reduce resource usage by 60+% when compared to eager aggregation used in existing FL platforms. We demonstrate that using JIT aggregation has negligible overhead and impact on the latency of the FL job.
On the Impact of Internal Webpage Selection when Evaluating Ad Blocker Performance

Not all ad blockers achieve the same blocking success and, depending on their implementation, they can either improve or hurt the web performance experienced by users. Borgolte and Feamster (2020) recently provided the first extensive evaluation of how privacy-focused browser extensions affect a user's web performance. However, while their work provides a nice comparison of the performance impact that different extensions may have, their evaluation only considered landing pages of a single set of websites. In this paper, we focus specifically on performance comparisons when considering different sets of webpages. For example, we study the impact of whether a page is a landing page or an internal page, whether a page is popular or less popular, as well as the impact of in which country/region the company registering the website is operating (used as a proxy for the primary target market). For our evaluations, we use pairs of webpages carefully selected from the recently proposed Hispar list (Aqeel et al. 2020) and compare the performance of the most popular blocking extensions (Ad block Plus, uBlock Origin, Ghostery, and Private Badger) considered by Borgolte and Feamster with a baseline case in which we do not use any extension. While we observe clear differences in the distribution statistics of the metrics considered, several observations were consistent across all dimensions, including whether we consider landing pages or internal pages. The paper highlights some of these invariants and discusses their implications. In addition, our measurements (and the differences observed by different ad-blockers) also reveal new insights into how internal vs. landing pages of different web page categories (e.g., based on popularity or region) differ in their composition and resource usage.
Forecasting Lightpath Quality of Transmission and Implementing Uncertainty in the Forecast Models

The recent popularity of using deep learning models for the forecasting of time series calls for methods to not only predict the target but also measure the uncertainty of the prediction accurately. Working with time series requires reliable and stable forecasters. An essential component of the reliability of machine learning (ML) and deep learning (DL) models is the estimation of the uncertainty. In this work, we address building and characterizing time series forecasters, including N-Beats, Long Short-Term Memory (LSTM) and Multilayer Perceptron (MLP) against the Naive model, and define the confidence margins, and uncertainty for the selected model. All the implementations are conducted in Python programming language. Random sampling is performed to avoid overfitting. Our target field data is North American Service Provider data sets (NASP). Among the implemented models, the MLP model is selected to measure the uncertainty and confidence level, and the Monte Carlo dropout, which approximates Bayesian uncertainty, is applied during inference to render the implementation of uncertainty calculations. Quantile Regression is also implemented on the MLP algorithm as a baseline to predict the confidence intervals and to evaluate our strategy for estimating uncertainty. To establish reliable uncertainty estimation in time series predictions, we performed uncertainty calibration. Motivated by recent developments in Expected Uncertainty Calibration Error (UCE), we modified the uncertainty calculated by the probabilistic Bayesian estimations. Detailed experiments and architectures of the solution are presented.
Exploring Action Centers for Temporal Action Localization

Temporal action localization aims at detecting the temporal intervals of human actions in untrimmed videos. Most previous methods rely on locating and matching the start and end times of actions. However, action boundaries are ambiguous and uncertain in nature, which leads to inaccurate action localization and a lot of false positives. In this paper, we introduce a new framework for temporal action localization. It explicitly models temporal action centers to reduce unreliable action detection results caused by ambiguous action boundaries. Since action centers are highly related to semantic actions, they can be detected more reliably than the conventional action boundaries. As a result, our framework can exclude false positives and promote high-quality proposals. Based on action centers, we propose a triplet feature fusion mechanism. It performs neural message passing among the boundaries and the center as well as contextual regions outside of the proposal to enrich its representation. In addition, we introduce a centerness scoring method to suppress proposals deviating from the centers of action instances. Consequently, our network can retrieve high-quality action proposals and locate actions more precisely. Experimental results show our method outperforms state-of-the-art methods on the THUMOS14 and ActivityNet v1.3 datasets.
UDTCWT-PHFMs Domain Statistical Image Watermarking Using Vector BW-Type R Distribution

For any image watermarking algorithm, how to achieve the trade-off among robustness, imperceptibility, and watermark capacity is a challenging problem because of their mutually constrained relationship. In this paper, we design a statistical-based image watermarking system to solve the trade-off problem. In embedding process, consider the imperceptibility and the robustness, we inventively combine the undecimated dual tree complex Wavelet transform (UDTCWT) and the polar harmonic Fourier moments (PHFMs) to obtain the UDTCWT-PHFMs magnitudes as the watermark carriers, and we embed watermark signals in multiplicative manner. In modeling phase, by analyzing the statistical property and the multiple correlations of the UDTCWT-PHFMs magnitudes, we bound the magnitudes of different directions into the vector groups, and then we model them with the vector Roy type bivariate Weibull (BW-Type R) distribution so that we can capture accurately the marginal characteristics, the inter-scale dependencies, and the inter-orientation dependencies at the same time. Moreover, we obtain the model parameters with the double looped maximum likelihood estimation (double-looped MLE). Benefit from the reliable modeling result, we finally derive a novel specific image watermark decoder to blindly extract the hidden watermarks. Extensive experiment results declare the designed statistical image watermarking system achieves the better balance among imperceptibility, robustness, and payload.
Unsupervised Optical Flow Estimation for Differently Exposed Images in LDR Domain

Differently exposed low dynamic range (LDR) images are often captured sequentially using a smart phone or a digital camera with movements. Optical flow thus plays an important role in ghost removal for high dynamic range (HDR) imaging. The optical flow estimation is based on the theory of photometric consistency, which assumes that the corresponding pixels between two images have the same intensity. However, the assumption is no longer valid for the differently exposed LDR images since a pixel’s intensity changes significantly inter images. To address the problem, an unsupervised optical flow estimation framework, is presented in this study. Intensity mapping functions (IMFs) are first adopted to alleviate the intensity changes between the LDR images. Then a novel IMF-based unsupervised learning objective is proposed to circumvent the need for ground truth optical flows when training the deep network. Experimental results and ablation studies on publicly available datasets show that our framework outperforms the state-of-the-art unsupervised optical flow methods, demonstrating the effectiveness of the IMF and the learning objective. Our code is available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/liuziyang123/LDRFlow</uri> .
Robust Multi-Ship Tracker in SAR Imagery by Fusing Feature Matching and Modified KCF

In previous research, most Multi-object tracking (MOT) algorithms focus on the optical image dataset, while the Synthetic Aperture Radar (SAR) image dataset faces the characteristics of few prior samples, high false alarm rate, and various defocusing interference. On the SAR image dataset, a robust MOT algorithm is proposed to fulfill multi-ship tracking in complex imaging conditions. First, the kernelized correlation filters (KCF) algorithm, a single-object tracking algorithm, is modified and applied to reduce the impact of false alarms on tracking performance. After that, different matching strategies are adaptively adapted to associate the targets based on the three intersection patterns between the predictions and the detections, which can reduce the impact of the deviated detections. Finally, the tracker’s time limit with Gaussian distribution is proposed to improve the re-association ability after the tracking interruption caused by the defocusing. The experiment results demonstrate the robust tracking ability of the proposed MOT algorithm.
Exploiting parallelization in positional Burrows–Wheeler transform (PBWT) algorithms for efficient haplotype matching and compression

The positional Burrows-Wheeler transform (PBWT) data structure allows for efficient haplotype data matching and compression. Its performance makes it a powerful tool for bioinformatics. However, existing algorithms do not exploit parallelism due to inner dependencies. We introduce a new method to break the dependencies and show how to fully exploit modern multi-core processors.Source code and applications are available at https://github.com/rwk-unil/parallel_pbwt.Supplementary data are available at Bioinformatics Advances online.
Polycyclic codes over <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" altimg="si1.svg"><mml:msub><mml:mrow><mml:mi mathvariant="double-struck">F</mml:mi></mml:mrow><mml:mrow><mml:msup><mml:mrow><mml:mi>p</mml:mi></mml:mrow><mml:mrow><mml:mi>m</mml:mi></mml:mrow></mml:msup></mml:mrow></mml:msub><mml:mo stretchy="false">[</mml:mo><mml:mi>u</mml:mi><mml:mo stretchy="false">]</mml:mo><mml:mo stretchy="false">/</mml:mo><mml:mo stretchy="false">〈</mml:mo><mml:msup><mml:mrow><mml:mi>u</mml:mi…

Let p be prime integer, s and m positive integers; f(x) be a monic irreducible polynomial over R where R:=Fpm[u]/〈u2〉. This paper gives a complete classification of polycyclic codes over R associated to f(x)ps, and the other hand, when f(x)∈Fpm[x], their Hamming distances and their annihilators are explicitly given.
UPTDNet: A User Preference Transfer and Drift Network for Cross-City Next POI Recommendation

Cross-city point of interest (POI) recommendation for tourists in an unfamiliar city has high application value but is challenging due to the data sparsity. Most existing models attempt to alleviate the sparsity problem by learning the user preference transfer and drift. However, they either fail to simultaneously model the preference transfer and drift in both long- and short-term user preferences or cannot accomplish the task of the next POI recommendation, which is crucial for a wide spectrum of applications ranging from transportation and urban planning to advertising. To address the limitation, we proposed a user preference transfer and drift network (UPTDNet) for cross-city next POI recommendation. UPTDNet excels at cross-city recommendations by learning the transfer and drift of both long- and short-term preferences. For short-term preference, dual recurrent neural network-based (RNN-based) branches are designed to model preference transfer from tourist’s current city and drift among different user roles. For long-term preference, a mapping function and user similarity calculation are employed for preference transfer from the tourist’s home city and drift among individual users. Experiments are conducted on the Gowalla and Foursquare datasets, and the results show that UPTDNet consistently and significantly outperforms state-of-the-art models by an average of 10.22% to 22.63% in the next POI recommendation task. Ablation study and further analysis validate the effectiveness and plausibility of considering both user preference transfer and drift in the cross-city recommendation.
Neural networks contribution in face mask detection to reduce the spread of COVID-19

In front of COVID-19 propagation, we can protect our self by taking precautionary measures such as wearing face masks. It may be mandatory in particular public place although some persons ignore this rule. Several research in face mask detection area have emerged and most of studies are based on deep learning. In this paper, we present a method to detect whether person wear a mask or not to prevent the propagation of virus. The approach is based on combination of Pulse Couple Neural Network and Fully Connected Neural Network and the processing is divided in three steps: geometrical, feature extraction and decision. The geometrical module selects the Region of Interest for given image and the feature extraction module composed by Pulse Couple Neural Network extracts all pertinent information which will be used by the last module for decision. This decision module makes directly a decision in case of non-complex classification without neural network training overwise the Fully Connected Neural Network continues the treatment. The input image may be captured from video surveillance sequence, the system triggers a signal alarm once a person doesn't wear face mask. Our proposed approach was tested with different datasets like Kaggle, AIZOO, Moxa3K, Real-World Masked Face Dataset, Medical Masks Dataset, Face Mask Dataset and the accuracy varies from 83.2% to 100% with minimum computation time.
Parameter control for differential evolution by storage of successful values at an individual level

Setting the parameters of differential evolution to appropriate values is critical for attaining satisfactory performance on a given problem (class). Therefore, parameter tuning is typically necessary, at least to some extent. However, even despite extensive tuning, the best possible performance may still not be achieved since different parameter values might be necessary during the different phases of the optimisation process. A solution to this issue is offered by parameter control schemes that properly adjust the parameters during the algorithm run. This paper presents a new self-adaptive parameter control scheme, which is based on the storage of a few previously successful values for each population member separately. These are then subsequently used to generate new and better-fitting parameter values with the aim of maintaining an effective search throughout the algorithm run. The highly competitive results obtained on challenging benchmark functions of different dimensionality suggest its viability and robustness. This is further corroborated by the promising results obtained on the problem of automatic radial basis function network design for classification needs.
Analysis and Evaluation of Major COVID-19 Features: A Pairwise Comparison Approach

The COVID-19 pandemic is a major health threat and its global spread has led governments worldwide to take a series of public health and social measures and restrictions, aiming to reduce its transmission. As COVID-19 outbreak continues, there is a crucial need for further analysis and evaluation of the main features that seem to affect the clinical status of a patient infected by SARS-CoV-2. In this context, the present paper introduces a Covid Patient Assessment Analysis (CPAA) based on operational research, which examines the patient profile, taking into consideration characteristics like gender and age, and also categorizes the experiencing COVID-19 symptoms and the dependency of patient’s clinical status from potential comorbidities. Finally, evaluating all the aforementioned features, CPAA ranks COVID-19 cases based on the severity of each case in low-, medium-, and high-risk groups. For the modeling and the implementation of the CPAA, the Pairwise Comparison (PWC) has been used as an integral part of a decision-making process. The outcomes of the paper are the first step towards an overall operational research framework that would be used to evaluate the clinical status of patients and take automate decisions for their potential hospitalization.
Technical debt (TD) through the lens of Twitter: A survey

Abstract Technical debt (TD) is a metaphor used to refer to the added software system costs acquired from taking shortcuts. Unfortunately, large amounts of TD can lead to serious consequences, and, thus, the management of TD is essential. Due to TD being a relatively new subject of study, many aspects of TD remain ambiguous. Fortunately, Twitter has been proven to hold a wealth of information on many subjects. As such, this survey study aims to gain a better understanding on how interest in TD has evolved over time and how TD is addressed on Twitter. A total of 128,897 TD‐related tweets were scrapped from Twitter and analyzed using a number of proxy measures and Latent Dirichlet Allocation (LDA). The results revealed that interest in TD on Twitter has been generally increasing since the platform's early stages. Furthermore, TD‐related tweets were found to revolve around 11 distinct categories. The TD in games category was discovered to be the most popular category, followed by TD communication and TD repayment. The results highlight that TD is a diverse and overarching topic that contains many potential avenues for further exploration. Software engineering researchers, practitioners, and educators can utilize this study to help steer their TD‐related future efforts.
Literacy Improvement Through Regional Libraries in Maros Regency

The author carries out this service activity with the aim of providing education to increase literacy to the visitors to the Maros Regency regional library. In addition, the author also contributed several anthology books with other author friends. This began when he received an assignment letter from the campus and then communicated with the management of the Maros Regency Regional Library. After being accepted, the author came to provide education in the form of brainstorming with visitors about the importance of increasing literacy. Because at that time there was still a Covid-19 pandemic, visitors were still limited, generally only students and students who were still in the online learning process. The educational process with visitors was not carried out simultaneously, but came one by one at the reading table. The author conveys material about the importance of increasing literacy, supporting and inhibiting factors for increasing literacy, solutions to overcome problems of increasing literacy. In addition, the author also prepared several short story anthology books to be shown and discussed to the visitors. After the implementation was completed, the Short Story Anthology book was handed over to the manager of the Regional Library of Maros Regency. This community service activity was carried out in May - July 2021 at the Regional Library of Maros Regency
Clustering-based method for big spatial data partitioning

“Internet of Things” (IoT) is considered one of the main focus areas of research in computer systems and networks. Since IoT devices are installed in static geographic places or on board a moving trackable object, the data generated by the device is mainly characterized as spatial data. The spatial data generated by IoT devices scale up in volume, velocity, and veracity so they tend to be considered “Big Data”. “Big Spatial Data” requires the development of special frameworks that use state-of-the-art technologies in data storage, query, and analysis. These frameworks are mainly characterized by the use of a parallel programming model that partitions the spatial data into smaller chunks that can be handled in parallel. The development of an optimal method for spatial data partitioning is essential in implementing such systems. In this paper, we propose, design, and implement a new method for spatial data partitioning based on K-Means clustering, an unsupervised machine learning algorithm. The design is based on a well-defined conceptual, mathematical, and programming model for a general spatial data partitioning method. The main component of the suggested model is an implementation of K-Means clustering suited for spatial data. The new method is designed, implemented, and tested to prove its ability to achieve partitioning objectives and the efficiency of its performance. The results of the tests are benchmarked against one of the most widely adopted approaches in partitioning spatial data and prove the ability of the novel method to surpass it in some of the evaluation criteria.