Reviewerly/exHarmony huggingface dataset:
<i>Hone:</i> Mitigating Stragglers in Distributed Stream Processing With Tuple Scheduling

Low latency stream processing on large clusters consisting of hundreds to thousands of servers is an increasingly important challenge. A crucial barrier to tackling this challenge is stragglers, i.e., tasks that are significantly straggling behind others in processing the stream data. However, prior straggler mitigation solutions have significant limitations. They balance streaming workloads among tasks but may incur imbalanced backlogs when the workloads exhibit variance, causing stragglers as well. Fortunately, we observe that carefully scheduling the outgoing tuples of different tasks can yield benefits for balancing backlogs, and thus avoids stragglers. To this end, we present Hone, a tuple scheduler that aims to minimize the maximum queue backlog of all tasks over time. Hone leverages an online Largest-Backlog-First (LBF) algorithm with a provable good competitive ratio to perform efficient tuple scheduling. We have implemented Hone based on Apache Storm and evaluated it extensively via both simulations and testbed experiments. Our results show that under the same workload balancing strategy-shuffle grouping, Hone outperforms the original Storm significantly, with the end-to-end tuple processing latency reduced by 78.7 percent on average.
Multiple Target Tracking With Unresolved Measurements

A multiple target tracking filter is developed for merged measurement problems that arise with finite resolution sensors. The resulting combinatorial problem is incorporated directly in the joint likelihood function using analytic combinatorics techniques. The Bayesian filter is a sum of several terms that correspond one-to-one to the set of all feasible hypotheses about measurements and targets, i.e., resolved/unresolved, detected/undetected. Performance is demonstrated for two targets in both crossing and parallel target motion scenarios.
Designing a Personalized Health Dashboard: Interdisciplinary and Participatory Approach

Within the Dutch Child Health Care (CHC), an online tool (360° CHILD-profile) is designed to enhance prevention and transformation toward personalized health care. From a personalized preventive perspective, it is of fundamental importance to timely identify children with emerging health problems interrelated to multiple health determinants. While digitalization of children's health data is now realized, the accessibility of data remains a major challenge for CHC professionals, let alone for parents/youth. Therefore, the idea was initiated from CHC practice to develop a novel approach to make relevant information accessible at a glance.This paper describes the stepwise development of a dashboard, as an example of using a design model to achieve visualization of a comprehensive overview of theoretically structured health data.Developmental process is based on the nested design model with involvement of relevant stakeholders in a real-life context. This model considers immediate upstream validation within 4 cascading design levels: Domain Problem and Data Characterization, Operation and Data Type Abstraction, Visual Encoding and Interaction Design, and Algorithm Design. This model also includes impact-oriented downstream validation, which can be initiated after delivering the prototype.A comprehensible 360° CHILD-profile is developed: an online accessible visualization of CHC data based on the theoretical concept of the International Classification of Functioning, Disability and Health. This dashboard provides caregivers and parents/youth with a holistic view on children's health and "entry points" for preventive, individualized health plans.Describing this developmental process offers guidance on how to utilize the nested design model within a health care context.
Towards Environment-Independent Human Activity Recognition using Deep Learning and Enhanced CSI

Deep learning has shown a strong potential in device-free human activity recognition (HAR). However, a fundamental challenge is ensuring accuracy, without re-training, when exposing a previously trained architecture to a new or unseen environment. To overcome the aforementioned challenge, this paper proposes an environment-robust channel state information (CSI) based HAR by leveraging the properties of a matching network (MatNet) and enhanced features (HAR-MN-EF). To improve the CSI quality, we propose a CSI cleaning and enhancement method (CSI-CE) that includes two key stages: activity-related information extraction (ARIE) and correlation feature extraction based on principal component analysis (CFE-PCA). The ARIE stage is able to effectively enhance the activity-dependent features whilst mitigating behavior-unrelated information. The CFE-PCA stage further improves the extracted features by filtering out the residual activity-unrelated data and the residual noise contained in signals from the former stage. The extracted features are then sequenced into the MatNet to create an environment-robust HAR. Experimental results confirm that an architecture trained by the proposed HAR-MN-EF can be directly adapted to a new environment, achieving reliable sensing accuracies without requiring additional effort.
Visual Summarization of Lecture Video Segments for Enhanced Navigation

The following topics are dealt with: video signal processing; video streaming; feature extraction; deep learning (artificial intelligence); video coding; learning (artificial intelligence); quality of experience; computer aided instruction; convolutional neural nets; image segmentation.
Generative adversarial learning for detail-preserving face sketch synthesis

Face sketch synthesis aims to generate a face sketch image from a corresponding photo image and has wide applications in law enforcement and digital entertainment. Despite the remarkable achievements that have been made in face sketch synthesis, most existing works pay main attention to the facial content transfer, at the expense of facial detail information. In this paper, we present a new generative adversarial learning framework to focus on detail preservation for realistic face sketch synthesis. Specifically, the high-resolution network is modified as generator to transform a face image from photograph to sketch domain. Except for the common adversarial loss, we design a detail loss to force the synthesized face sketch images have proximate details to its corresponding photo images. In addition, the style loss is adopted to restrain the synthesized face sketch images have vivid sketch style as the hand-drawn sketch images. Experimental results demonstrate that the proposed approach achieves superior performance, compared to state-of-the-art approaches, both on visual perception and objective evaluation. Specifically, this study indicated the higher FSIM values (0.7345 and 0.7080) and Scoot values (0.5317 and 0.5091) than most comparison methods on the CUFS and CUFSF datasets, respectively.
BELIEF: A distance-based redundancy-proof feature selection method for Big Data

With the advent of Big Data era, data reduction methods are in highly demand given their ability to simplify huge data, and ease complex learning processes. Concretely, algorithms able to select relevant dimensions from a set of millions are of huge importance. Although effective, these techniques also suffer from the “scalability” curse when they are brought into tackle large-scale problems. In this paper, we propose a distributed feature weighting algorithm which precisely estimates feature importance in large datasets using the well-know algorithm RELIEF in small problems. Our solution, called BELIEF, incorporates a novel redundancy elimination measure that generates similar schemes to those based on entropy, but at a much lower time cost. Furthermore, BELIEF provides a smooth scale-up when more instances are required to increase precision in estimations. Empirical tests performed on our method illustrate the estimation ability of BELIEF in manifold huge sets – both in number of features and instances, as well as its reduced runtime cost as compared to other state-of-the-art methods.
Handwritten Digits Identification Using Mnist Database Via Machine Learning Models

Abstract The identification of hand-written digits is among the most significant issue in the applications for pattern detection. In many application such as postal code, check online routing bank accounts, data form entry, etc., the applications of digits recognition include the center of the issue is the need to construct an appropriate algorithm that can identify hand-written digits and that users upload through a smartphone and scanner and other digital devices. In this paper, we took a repository of MNIST, which is a sub-set of the database of NIST results. The MNIST dataset accommodates the collection of hand-written scanned images from a broader variety of NIST repository produced by hand. The method proposed in this paper is centered on numerous machine learning methods to perform hand-written digit detection that is off-line in the python language platform. The primary objective of this paper is to render hand-written digits recognition reliable and precise. For the identification of digits using MNIST many machine learning algorithms have been used including Support Vector Machine, Multilayer Perceptron, Decision Tree, Naïve Bayes, K-Nearest Neighbor, and Random Forest.
MILAGE LEARN+: A Mobile Learning App to Aid the Students in the Study of Organic Chemistry

Interactive learning is one of the approaches that is very important to explore in higher education. Chemistry students own smartphones, laptops, and tablets and could use appropriate apps to complement traditional forms of learning. Here we introduce a new app, MILAGE LEARN+, which integrates several teaching strategies, such as mobile learning, autonomous learning, peer review, blended learning, and gamification. These strategies were evaluated by the student users through an electronic survey form containing 15 statements with responses graded on a Likert-type scale. Students rate the usage of novel teaching/learning strategies in the MILAGE LEARN+ app as something very positive. There is a positive correlation between the relative grades obtained using MILAGE LEARN+ and the final exam grades. Thus, in this experience, students with better results in MILAGE LEARN+ also had better results in the final exam.
Bias in Error-Corrected Quantum Sensing

The sensitivity afforded by quantum sensors is limited by decoherence. Quantum error correction (QEC) can enhance sensitivity by suppressing decoherence, but it has a side effect: it biases a sensor's output in realistic settings. If unaccounted for, this bias can systematically reduce a sensor's performance in experiment, and also give misleading values for the minimum detectable signal in theory. We analyze this effect in the experimentally motivated setting of continuous-time QEC, showing both how one can remedy it, and how incorrect results can arise when one does not.
Region proposal and object detection using HoG-based CNN feature map

Object region proposal has a wide application on various domains, such as object detection, object tracking, robot navigation, and anomaly detection. Widely used region proposal methods are based on either grouping superpixels or sliding windows. Previous studies have been done by grouping either convolution features, superpixels, or windows. Moreover, histogram of gradients (HoGs) of lower-level features (pixels) can be used for the region proposal task. In this study, both the concepts of HoGs and convolution features are used for proposing object(s) region(s) in an image. Region proposal is done by HoGs of convolution features and statistical application on the gradient data of different feature maps. First, a sized image is passed through one convolution layer to generate reduced feature maps. Then, a window slides over each feature map and HoG is applied over each window. Thereafter, the histogram is analysed using the concept of Coefficient of Variation (CV) and angle (slope) applied on histogram data values. A window is considered as the object region if the CV and angle of its histogram data exceed some thresholds. For all feature maps, we have obtained several widows that represent approximated object regions. These windows are grouped to obtain the possible region(s) proposal. The effectiveness of the proposed method is demonstrated over one of the benchmark datasets, called `PASCAL VOC'. Our method is found superior to two other state-of-the-art, namely region-based segmentation (RBS), and combining efficient object localization and image classification (CEOLIC) in terms of recall.
Quadcopter Fast Pure Descent Maneuver Avoiding Vortex Ring State Using Yaw-Rate Control Scheme

Due to their simplicity and agility, nowadays drones especially quadcopters have become popular in many applications. However, they still have a velocity limitation on fast descent maneuvers, which is known as Vortex Ring State (VRS) constraint in the literature. In this paper, first, we provide a brief description of VRS, and formalize a constraint on the quadcopter's velocity regarding the VRS. Then a novel control strategy using the yaw rate has been suggested to avoid the VRS velocity constraint. Finally, the motion of the quadcopter and the proposed controller are examined in both simulation and experiment, and the performance of the system in avoiding the unstable VRS region in a fast descent maneuver is demonstrated. The quadcopter's equations of motion and the Yaw Rate Controller (YRC) are simulated in the Matlab/Simulink software. The experiments show that the quadcopter can descend about three times faster than the regular descent maneuvers, using the YRC algorithm without any instability. This is due to the creation of horizontal airflow on the blade disk by the yaw rate maneuver. By using this algorithm, quadcopters can descend faster to save their flight endurance for their main mission goal. Moreover, since this method guarantees the stability of quadcopters in pure descents, the drone can be used in chimneys inspections.
Cognitive load reduction strategies in questionnaire design

Survey data quality suffers when respondents have difficulty completing complex tasks in questionnaires. Cognitive load theory informed the development of strategies for educators to reduce the cognitive load of learning tasks. We investigate whether these cognitive load reduction strategies can be used in questionnaire design to reduce task difficulty and, in so doing, improve survey data quality. We find that this is not the case and conclude that some of the traditional survey answer formats, such as grid questions, which have been criticized in the past lead to equally good data and do not frustrate respondents more than alternative formats.
Modeling Future Cost for Neural Machine Translation

Existing neural machine translation (NMT) systems utilize sequence-to-sequence neural networks to generate target translation word by word, and then make the generated word at each time-step and the counterpart in the references as consistent as possible. However, the trained translation model tends to focus on ensuring the accuracy of the generated target word at the current time-step and does not consider its future cost which means the expected cost of generating the subsequent target translation (i.e., the next target word). To respond to this issue, in this article, we propose a simple and effective method to model the future cost of each target word for NMT systems. In detail, a future cost representation is learned based on the current generated target word and its contextual information to compute an additional loss to guide the training of the NMT model. Furthermore, the learned future cost representation at the current time-step is used to help the generation of the next target word in the decoding. Experimental results on three widely-used translation datasets, including the WMT14 English-to-German, WMT14 English-to-French, and WMT17 Chinese-to-English, show that the proposed approach achieves significant improvements over strong Transformer-based NMT baseline.
No strings attached

Strings play many roles in programming because they often contain complex and semantically rich information. For example, programmers use strings to filter inputs via regular expression matching, to express the names of program elements accessed through some form of reflection, to embed code written in another formal language, and to assemble textual output produced by a program. The omnipresence of strings leads to a wide range of mistakes that developers may make, yet little is currently known about these mistakes. The lack of knowledge about string-related bugs leads to developers repeating the same mistakes again and again, and to poor support for finding and fixing such bugs. This paper presents the first empirical study of the root causes, consequences, and other properties of string-related bugs. We systematically study 204 string-related bugs in a diverse set of projects written in JavaScript, a language where strings play a particularly important role. Our findings include (i) that many string-related mistakes are caused by a recurring set of root cause patterns, such as incorrect string literals and regular expressions, (ii) that string-related bugs have a diverse set of consequences, including incorrect output or silent omission of expected behavior, (iii) that fixing string-related bugs often requires changing just a single line, with many of the required repair ingredients available in the surrounding code, (iv) that string-related bugs occur across all parts of applications, including the core components, and (v) that almost none of these bugs are detected by existing static analyzers. Our findings not only show the importance and prevalence of string-related bugs, but they help developers to avoid common mistakes and tool builders to tackle the challenge of finding and fixing string-related bugs.
Identifying interception possibilities for WhatsApp communication

On a daily basis, law enforcement officers struggle with suspects using mobile communication applications for criminal activities. These mobile applications replaced SMS-messaging and evolved the last few years from plain-text data transmission and storage to an encrypted version. Regardless of the benefits for all law abiding citizens, this is considered to be the downside for criminal investigations. Normal smartphone, computer or network investigations do no longer provide the contents of the communication in real-time when suspects are using apps like WhatsApp, Signal or Telegram. Among them, WhatsApp is one of the most common smartphone applications for communication, both criminal as well as legal activities. At the beginning, WhatsApp communication between smartphone and server used to be in plain-text and therefore visible to law enforcement during a wiretap. Early 2016 WhatsApp introduced end-to-end encryption for all users, immediately keeping law enforcement officers around the world in the dark. Existing research to recuperate the position of law enforcement is limited to a single field of investigation and often limited to post mortem research on smartphone or computer while wiretapping is limited to metadata information. Therefore, it provides only historical data or metadata while law enforcement officers want a continuous stream of live and substantive information. This paper identified that gap in available scenarios for law enforcement investigations and identified a gap in methods available for forensic acquiring and processing these scenarios. In this paper, we propose a forensic approach to create real-time insight in the WhatsApp communication. Our approach is based on the wiretapping, decrypting WhatsApp databases, open source intelligence and WhatsApp Web communication analysis. We also evaluate our method with different scenarios in WhatsApp forensics to prove its feasibility and efficiency. Through these scenarios, we found that by providing real-time intelligence such as profile pictures, their activity, voice and video call behaviour including location data as well as remote access to a suspect WhatsApp account, their conversations including voice messages (live) geolocation, shared contacts, documents, images and videos are made accessible. Hence, our corresponding method can be used by law enforcement agencies around the world to reinforce their position in the world of WhatsApp communication interception.
Beyond Doubt and Uncertainty: Religious Education for a Post-COVID-19 World

The COVID-19 pandemic has had a huge impact on every facet of life around the world. For many, the world has become a place of doubt and uncertainty as people struggle to cope with the changes coming in the wake of COVID-19 and seek to determine how best to respond. With ecological, economic, and ideological crises occurring alongside COVID-19, this study proposes religious education that emphasizes the value of life, promotes equality, and fosters global interconnectedness. This religious education provides an open, equal, and interconnected environment where learners can understand, discuss, and confront global problems for the common future.
A New Modulation Recognition Method Based on Wavelet Transform and High-order Cumulants

Abstract In order to improve the recognition performance of digital modulated signals under low signal-to-noise ratio, this paper proposes a new method that combines high-order cumulants and wavelet transform to extract transient features, and the recognition of six kinds of modulation signals is realized through the neural network classifier. Simulation results show that the proposed algorithm can achieve good performance under low signal-to-noise ratio. In addition, only three key features are selected in this method, which greatly reduces the complexity of the algorithm. Furthermore, the effects of sample rate, carrier frequency and symbol rate on recognition performance are analyzed and simulated for the sake of better recognition performance.
Stereoscopic Image Retargeting Based on Deep Convolutional Neural Network

Stereoscopic image retargeting aims at converting stereoscopic images to the target resolution adaptively. Different from 2D image retargeting, stereoscopic image retargeting needs to preserve both the shape structure of salient objects and depth consistency of 3D scenes. In this paper, we present a stereoscopic image retargeting method based on deep convolutional neural network to obtain high-quality retargeted images with both object shape preservation and scene depth preservation. First, a cross-attention extraction mechanism is constructed to generate attention map, which contains the valuable attention features of the left and right images and the common attention features between them. Second, since the disparity map can provide accurate depth information of objects in 3D scenes, a disparity-assisted 3D significance map generation module is utilized to further preserve the valuable depth information of stereoscopic images. Finally, in order to predict the retargeted stereoscopic images accurately, an image consistency loss is developed to preserve the geometric structure of salient objects, and a disparity consistency loss is introduced to eliminate depth distortions. Experimental results demonstrate that the proposed deep convolutional neural network can provide favorable stereoscopic image retargeting results.